experimental type design

Remarkable experimental typefaces and how to use them

Far from your everyday geometric sans-serif, this collection of edgy and intriguing display typefaces will inspire you.

Typeface design by Studio Triple.

Carrie Cousins

Typography is at the core of the personality and style of a design project.

A type choice can blend into the background with subtlety that helps clearly communicate messaging or serve as a dominant visual force that pushes the design forward. Either way, typeface design is an important element in any project.

Experimental typefaces can help add a powerful edge or the right feeling to a design. They often serve as artistic and typographic elements concurrently. But experimental typefaces aren’t always bold and bizarre; this type style encompasses anything new and interesting that pushes type norms just a little outside of the usual comfort zone.

Here’s a guide to everything you need to know about experimental typefaces with a selection of some great examples to try in your projects.

What are experimental typefaces?

Experimental typefaces are generally of the display family, and have an unexpected look or interaction, such as animation, different x-heights, or a general disregard of the rules of letterform shape and spacing.

These text styles include quirky lines, colors, and letterforms. They have flair and plenty of personality.

But that’s not always the case.

“I think there might be a misunderstanding about this term of ‘experimental.’ We tend to use it to describe display typefaces that look either new or weird or disturbing,” says Jérémy Landes, Art Director & Type Designer at Studio Triple .

“The question of the design process behind the creation of these typefaces, and the question of the experimentation in the design process, is not really the point. Anyway, it seems to me that this term tries to convey the idea of a certain novelty, an avant-garde in the display genre,” he notes.

The definition of an experimental font may not be truly solidified, but you can think of it like this: An experimental font is anything that bucks the rules of traditional type design.

What might be most important when designing or using experimental or expressive typefaces is the push and pull between uniqueness and readability.

When working with this kind of type element, there’s a delicate balance between typography as art and typography to convey a message.

According to Jérémy Landes, while experimental typefaces stand out for their innovation, the main criteria to examine them is audiences’ reactions. “They feel new, but the question behind a successful experimental typeface is more one of the Zeitgeist. Will people relate to it? Will people be intrigued by this novelty, more than repelled? The question of why a creation, and a typeface, is successful at one point in time is for me the most intriguing one.”

Before conducting any type of design experiment, it is important to think about how it will resonate with your core audience and overall messaging.

Related: What a queerer design industry might look like: more expressive, collective and subversive

“The question behind a successful experimental typeface is more one of the Zeitgeist. Will people relate to it? Will people be intrigued by this novelty, more than repelled?"

Types of experimental typefaces

Experimental typefaces can have a variety of purposes–purely aesthetic, commercial display, or to promote a specific goal, such as social or promotional engagement.

Visually, these typefaces come in a lot of forms:

Color fonts

Variable fonts

Handwritten fonts

Custom novelty

Illustrated or artistic lettering

Three-dimensional

10 remarkable experimental typefaces

1. solide mirage.

Monospace | Free, open source

Design: Velvetyne Fonts

Solide Mirage was originally designed as a custom typeface for French-British music band, Frànçois and the Atlas Mountains . The band and designer decided to release the font as free and open source, to be used by fans as well as anyone else who appreciates its experimental aesthetic.

This unicase display typeface combines upper and lowercase characters together, resulting in a somewhat haphazard look. It was inspired by the Didone genre, while putting a new spin on the classic style with compressed shapes and long, elaborate serifs, especially on letters with ascenders or descenders. The typeface supports the Latin and Greek writing systems, and has ornamental alternates for some of the letters, shaped as squiggly zig-zags.

Consider using this font for branding. Note that while it is highly readable with some character combinations, others can be more difficult.

2. UltraSolar Normal

Sans serif novelty | Paid

Design: Adrien Midzic for Pizza Typefaces

This experimental typeface is highly readable and includes quirky details such as cutouts and missing curves. These details are inspired by traditional ink traps in typography, only that in this case, the missing details are stylistic interpretations and aren’t meant to be filled with ink in the printing process.

Best use cases would include titles and headers, or a short word or phrase in a display space.

3. Grind Grotesque

Sans serif | Free, open source

Design: Mickaël Emile

This open source font boasts an extremely wide stance, and is adequately named after the sliding stunt performed in extreme sports. In that vein, it’s best used with extreme design intent. The letterforms are sharp and compressed, with long straight lines and very little curves.

4. Euphoria

Novelty typeface | Paid

Design: Janik Sandbothe for Typelab

With hairline-thin strokes and pronounced, bubble-like terminals, this typeface turns to an Art Nouveau magazine cover illustrated by Ludwig von Zumbusch for inspiration. This expressive all-caps font features a single stroke design, as if scribbled lavishly in one go.

Its play on stroke width contrasts and free-flowing curves provide a lot of visual interest for display usage.

5. Digestive

Novelty | Paid

Design: Studio Triple

This font takes inspiration from Gothic architecture, Art Nouveau, seaweeds and human anatomy. The type design strikes a balance between attraction and repulsion, mixing tall proportions with loose, fluid shapes.

Digestive is a type family of seven fonts, ranging from super compressed to wide. This experimental typeface is best used for large text, and adding generous letter-spacing can help improve its legibility.

6. Cascadeur

Modular and variable font | Paid

Design: Peter Bushuev for Naum Type Foundry

This experimental typeface is a modernist homage to space-age typography. Its design is based on a four-lines grid, and together with it’s very tight kerning it creates a sense of filling up the space. The Cascadeur type family is made up of 12 styles and a variable font, supporting a variety of languages. Most letters come with two to four alternates.

This typeface is perfect for a design featuring oversized headlines or type for digital display or print. It would also work well as a primary artistic element.

7. Russibani

Novelty typeface

Design: Sveinn Þorri Davíðsson and Siggi Eggertsson

Aptly named Russibani, meaning roller coaster in Icelandic, this experimental typeface takes inspiration from roller coaster rails, using a single, continuous line for each letterform. The result is a pixelated-like appearance that gives off a mechanical feel.

Best uses may include branding projects or as the type treatment for a main headline or other large lettering.

Animated sans-serif | Paid

Design: James van den Elshout and Hans Renzler for Animography

This highly readable typeface includes 64 adjustable controllers, with each letter composed of six modifiable strokes. You can tweak this font’s motion speed, color, stroke width, and many other features, allowing for versatile uses of the same design.

Lovelo, which offers uppercase letters and extended Latin glyphs, can be downloaded either as a JSX file or an After Effects project. This typeface works best on a solid background as a decorative text element.

9. Singdings

Dingbat-based display | Paid

Design: Fable Type Foundry

With illustrative touches and a hint of whimsy, this experimental font features architectural landmarks and other objects from Singapore in honor of the country’s 55th birthday. The all-caps typeface offers 130 glyphs, with two to three alternates for each letter. These different versions allow designers to blend clean, geometric letters with unique dingbat-style ones, adding flexibility of use.

The specificity of the typeface dictates use somewhat, making it best fitted for projects related to its namesake, or in any design that calls for a palm tree as a capital T, a shrimp for a G and more.

10. David Milan : Hand lettering

Custom created lettering

Design: David Milan

While not an actual typeface, experimental typography can also take on the form of hand lettering. These unique renders of letterforms can add a bespoke touch to a design.

Based in Madrid, David Milan creates his lettering pieces either digitally or with markers and brush pens. His style ranges from hyper-realistic 3D designs, to looser strokes that highlight the human hind behind them. The typography itself also varies, going from handwritten script to cleaner capital letters.

When it comes to custom lettering and phrase design by a lettering artist, use and application is almost unlimited and can accommodate a wide range of design projects.

How do you use experimental typefaces?

Once you become enamored with the idea behind experimental typefaces, it is hard not to dive in. But you want to ensure that you’re using experimental typefaces the right way.

It’s important to consider whether these styles are appropriate for your audience or publication, the value of readability versus artistic value, and the overall relationship to your brand as a whole.

Here’s how you can make experimental typefaces work for your projects:

Use a typeface that feels like it goes with your brand. Experimental typefaces are funky by nature, so choosing an appropriate way to display them is important.

Mix it up. Using experimental typefaces can mean that you have to switch typefaces frequently for some text elements. Is that acceptable within your brand’s style?

Create a messaging match. A color font typically feels fun and light. Does that go with your content and tone? The same is true of any experimental typeface. Type in the words in the font you plan to use. What emotions or meaning come to you when you see it for the first time? Does that fit the goal of your messaging?

Build brand value. For startups or rebrands, an experimental typeface choice spark for a logotype or text element that becomes part of your brand’s visual story.

Think of typography as art. Not all typography is designed to read like a novel. For short words or phrases where the letters meld together in just the right way, type can be used as an artistic element. This concept can be a solid option if you find yourself in a project without a lot of other imagery.

That last point brings about a good question: How do you weigh readability versus the artistic value of an experimental typeface?

There’s no perfect answer. It really depends on your brand voice and tone in relationship to the font and aesthetic. But there are a few things you can do:

Test fonts, pay attention to analytics, and poll your audience. Do they understand what you are trying to communicate? Watch for red flags in your analytics such as drops in traffic, reduced time on site/page, and unclicked calls-to-action.

Pair an experimental typeface with a simpler, more legible font that reiterates key messaging.

Only use an experimental typeface with the finalized version of your UX copy. Don’t use placeholder text or elements that will auto populate, such as a header tag for webpages or a blog. These typefaces require attention and care that only comes with manual typesetting.

Experimental typography is one of those design tools that people tend to either love or hate. (I’m in the former category.) These typefaces are expressive, creative, and special.

They give type designers a chance to stretch with concepts, letterforms, and artistic vision.

The key thing when looking at typefaces in this category is toggling between readability and artistic value (more traditional fonts for websites here). Not all type styles have both, and it can be a determining factor when it comes to using an experimental typeface or not.

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Home » Experimental Design – Types, Methods, Guide

Experimental Design – Types, Methods, Guide

Table of Contents

Experimental Design

Experimental design is a process of planning and conducting scientific experiments to investigate a hypothesis or research question. It involves carefully designing an experiment that can test the hypothesis, and controlling for other variables that may influence the results.

Experimental design typically includes identifying the variables that will be manipulated or measured, defining the sample or population to be studied, selecting an appropriate method of sampling, choosing a method for data collection and analysis, and determining the appropriate statistical tests to use.

Types of Experimental Design

Here are the different types of experimental design:

Completely Randomized Design

In this design, participants are randomly assigned to one of two or more groups, and each group is exposed to a different treatment or condition.

Randomized Block Design

This design involves dividing participants into blocks based on a specific characteristic, such as age or gender, and then randomly assigning participants within each block to one of two or more treatment groups.

Factorial Design

In a factorial design, participants are randomly assigned to one of several groups, each of which receives a different combination of two or more independent variables.

Repeated Measures Design

In this design, each participant is exposed to all of the different treatments or conditions, either in a random order or in a predetermined order.

Crossover Design

This design involves randomly assigning participants to one of two or more treatment groups, with each group receiving one treatment during the first phase of the study and then switching to a different treatment during the second phase.

Split-plot Design

In this design, the researcher manipulates one or more variables at different levels and uses a randomized block design to control for other variables.

Nested Design

This design involves grouping participants within larger units, such as schools or households, and then randomly assigning these units to different treatment groups.

Laboratory Experiment

Laboratory experiments are conducted under controlled conditions, which allows for greater precision and accuracy. However, because laboratory conditions are not always representative of real-world conditions, the results of these experiments may not be generalizable to the population at large.

Field Experiment

Field experiments are conducted in naturalistic settings and allow for more realistic observations. However, because field experiments are not as controlled as laboratory experiments, they may be subject to more sources of error.

Experimental Design Methods

Experimental design methods refer to the techniques and procedures used to design and conduct experiments in scientific research. Here are some common experimental design methods:

Randomization

This involves randomly assigning participants to different groups or treatments to ensure that any observed differences between groups are due to the treatment and not to other factors.

Control Group

The use of a control group is an important experimental design method that involves having a group of participants that do not receive the treatment or intervention being studied. The control group is used as a baseline to compare the effects of the treatment group.

Blinding involves keeping participants, researchers, or both unaware of which treatment group participants are in, in order to reduce the risk of bias in the results.

Counterbalancing

This involves systematically varying the order in which participants receive treatments or interventions in order to control for order effects.

Replication

Replication involves conducting the same experiment with different samples or under different conditions to increase the reliability and validity of the results.

This experimental design method involves manipulating multiple independent variables simultaneously to investigate their combined effects on the dependent variable.

This involves dividing participants into subgroups or blocks based on specific characteristics, such as age or gender, in order to reduce the risk of confounding variables.

Data Collection Method

Experimental design data collection methods are techniques and procedures used to collect data in experimental research. Here are some common experimental design data collection methods:

Direct Observation

This method involves observing and recording the behavior or phenomenon of interest in real time. It may involve the use of structured or unstructured observation, and may be conducted in a laboratory or naturalistic setting.

Self-report Measures

Self-report measures involve asking participants to report their thoughts, feelings, or behaviors using questionnaires, surveys, or interviews. These measures may be administered in person or online.

Behavioral Measures

Behavioral measures involve measuring participants’ behavior directly, such as through reaction time tasks or performance tests. These measures may be administered using specialized equipment or software.

Physiological Measures

Physiological measures involve measuring participants’ physiological responses, such as heart rate, blood pressure, or brain activity, using specialized equipment. These measures may be invasive or non-invasive, and may be administered in a laboratory or clinical setting.

Archival Data

Archival data involves using existing records or data, such as medical records, administrative records, or historical documents, as a source of information. These data may be collected from public or private sources.

Computerized Measures

Computerized measures involve using software or computer programs to collect data on participants’ behavior or responses. These measures may include reaction time tasks, cognitive tests, or other types of computer-based assessments.

Video Recording

Video recording involves recording participants’ behavior or interactions using cameras or other recording equipment. This method can be used to capture detailed information about participants’ behavior or to analyze social interactions.

Data Analysis Method

Experimental design data analysis methods refer to the statistical techniques and procedures used to analyze data collected in experimental research. Here are some common experimental design data analysis methods:

Descriptive Statistics

Descriptive statistics are used to summarize and describe the data collected in the study. This includes measures such as mean, median, mode, range, and standard deviation.

Inferential Statistics

Inferential statistics are used to make inferences or generalizations about a larger population based on the data collected in the study. This includes hypothesis testing and estimation.

Analysis of Variance (ANOVA)

ANOVA is a statistical technique used to compare means across two or more groups in order to determine whether there are significant differences between the groups. There are several types of ANOVA, including one-way ANOVA, two-way ANOVA, and repeated measures ANOVA.

Regression Analysis

Regression analysis is used to model the relationship between two or more variables in order to determine the strength and direction of the relationship. There are several types of regression analysis, including linear regression, logistic regression, and multiple regression.

Factor Analysis

Factor analysis is used to identify underlying factors or dimensions in a set of variables. This can be used to reduce the complexity of the data and identify patterns in the data.

Structural Equation Modeling (SEM)

SEM is a statistical technique used to model complex relationships between variables. It can be used to test complex theories and models of causality.

Cluster Analysis

Cluster analysis is used to group similar cases or observations together based on similarities or differences in their characteristics.

Time Series Analysis

Time series analysis is used to analyze data collected over time in order to identify trends, patterns, or changes in the data.

Multilevel Modeling

Multilevel modeling is used to analyze data that is nested within multiple levels, such as students nested within schools or employees nested within companies.

Applications of Experimental Design 

Experimental design is a versatile research methodology that can be applied in many fields. Here are some applications of experimental design:

• Medical Research: Experimental design is commonly used to test new treatments or medications for various medical conditions. This includes clinical trials to evaluate the safety and effectiveness of new drugs or medical devices.
• Agriculture : Experimental design is used to test new crop varieties, fertilizers, and other agricultural practices. This includes randomized field trials to evaluate the effects of different treatments on crop yield, quality, and pest resistance.
• Environmental science: Experimental design is used to study the effects of environmental factors, such as pollution or climate change, on ecosystems and wildlife. This includes controlled experiments to study the effects of pollutants on plant growth or animal behavior.
• Psychology : Experimental design is used to study human behavior and cognitive processes. This includes experiments to test the effects of different interventions, such as therapy or medication, on mental health outcomes.
• Engineering : Experimental design is used to test new materials, designs, and manufacturing processes in engineering applications. This includes laboratory experiments to test the strength and durability of new materials, or field experiments to test the performance of new technologies.
• Education : Experimental design is used to evaluate the effectiveness of teaching methods, educational interventions, and programs. This includes randomized controlled trials to compare different teaching methods or evaluate the impact of educational programs on student outcomes.
• Marketing : Experimental design is used to test the effectiveness of marketing campaigns, pricing strategies, and product designs. This includes experiments to test the impact of different marketing messages or pricing schemes on consumer behavior.

Examples of Experimental Design 

Here are some examples of experimental design in different fields:

• Example in Medical research : A study that investigates the effectiveness of a new drug treatment for a particular condition. Patients are randomly assigned to either a treatment group or a control group, with the treatment group receiving the new drug and the control group receiving a placebo. The outcomes, such as improvement in symptoms or side effects, are measured and compared between the two groups.
• Example in Education research: A study that examines the impact of a new teaching method on student learning outcomes. Students are randomly assigned to either a group that receives the new teaching method or a group that receives the traditional teaching method. Student achievement is measured before and after the intervention, and the results are compared between the two groups.
• Example in Environmental science: A study that tests the effectiveness of a new method for reducing pollution in a river. Two sections of the river are selected, with one section treated with the new method and the other section left untreated. The water quality is measured before and after the intervention, and the results are compared between the two sections.
• Example in Marketing research: A study that investigates the impact of a new advertising campaign on consumer behavior. Participants are randomly assigned to either a group that is exposed to the new campaign or a group that is not. Their behavior, such as purchasing or product awareness, is measured and compared between the two groups.
• Example in Social psychology: A study that examines the effect of a new social intervention on reducing prejudice towards a marginalized group. Participants are randomly assigned to either a group that receives the intervention or a control group that does not. Their attitudes and behavior towards the marginalized group are measured before and after the intervention, and the results are compared between the two groups.

When to use Experimental Research Design 

Experimental research design should be used when a researcher wants to establish a cause-and-effect relationship between variables. It is particularly useful when studying the impact of an intervention or treatment on a particular outcome.

Here are some situations where experimental research design may be appropriate:

• When studying the effects of a new drug or medical treatment: Experimental research design is commonly used in medical research to test the effectiveness and safety of new drugs or medical treatments. By randomly assigning patients to treatment and control groups, researchers can determine whether the treatment is effective in improving health outcomes.
• When evaluating the effectiveness of an educational intervention: An experimental research design can be used to evaluate the impact of a new teaching method or educational program on student learning outcomes. By randomly assigning students to treatment and control groups, researchers can determine whether the intervention is effective in improving academic performance.
• When testing the effectiveness of a marketing campaign: An experimental research design can be used to test the effectiveness of different marketing messages or strategies. By randomly assigning participants to treatment and control groups, researchers can determine whether the marketing campaign is effective in changing consumer behavior.
• When studying the effects of an environmental intervention: Experimental research design can be used to study the impact of environmental interventions, such as pollution reduction programs or conservation efforts. By randomly assigning locations or areas to treatment and control groups, researchers can determine whether the intervention is effective in improving environmental outcomes.
• When testing the effects of a new technology: An experimental research design can be used to test the effectiveness and safety of new technologies or engineering designs. By randomly assigning participants or locations to treatment and control groups, researchers can determine whether the new technology is effective in achieving its intended purpose.

How to Conduct Experimental Research

Here are the steps to conduct Experimental Research:

• Identify a Research Question : Start by identifying a research question that you want to answer through the experiment. The question should be clear, specific, and testable.
• Develop a Hypothesis: Based on your research question, develop a hypothesis that predicts the relationship between the independent and dependent variables. The hypothesis should be clear and testable.
• Design the Experiment : Determine the type of experimental design you will use, such as a between-subjects design or a within-subjects design. Also, decide on the experimental conditions, such as the number of independent variables, the levels of the independent variable, and the dependent variable to be measured.
• Select Participants: Select the participants who will take part in the experiment. They should be representative of the population you are interested in studying.
• Randomly Assign Participants to Groups: If you are using a between-subjects design, randomly assign participants to groups to control for individual differences.
• Conduct the Experiment : Conduct the experiment by manipulating the independent variable(s) and measuring the dependent variable(s) across the different conditions.
• Analyze the Data: Analyze the data using appropriate statistical methods to determine if there is a significant effect of the independent variable(s) on the dependent variable(s).
• Draw Conclusions: Based on the data analysis, draw conclusions about the relationship between the independent and dependent variables. If the results support the hypothesis, then it is accepted. If the results do not support the hypothesis, then it is rejected.
• Communicate the Results: Finally, communicate the results of the experiment through a research report or presentation. Include the purpose of the study, the methods used, the results obtained, and the conclusions drawn.

Purpose of Experimental Design 

The purpose of experimental design is to control and manipulate one or more independent variables to determine their effect on a dependent variable. Experimental design allows researchers to systematically investigate causal relationships between variables, and to establish cause-and-effect relationships between the independent and dependent variables. Through experimental design, researchers can test hypotheses and make inferences about the population from which the sample was drawn.

Experimental design provides a structured approach to designing and conducting experiments, ensuring that the results are reliable and valid. By carefully controlling for extraneous variables that may affect the outcome of the study, experimental design allows researchers to isolate the effect of the independent variable(s) on the dependent variable(s), and to minimize the influence of other factors that may confound the results.

Experimental design also allows researchers to generalize their findings to the larger population from which the sample was drawn. By randomly selecting participants and using statistical techniques to analyze the data, researchers can make inferences about the larger population with a high degree of confidence.

Overall, the purpose of experimental design is to provide a rigorous, systematic, and scientific method for testing hypotheses and establishing cause-and-effect relationships between variables. Experimental design is a powerful tool for advancing scientific knowledge and informing evidence-based practice in various fields, including psychology, biology, medicine, engineering, and social sciences.

Advantages of Experimental Design 

Experimental design offers several advantages in research. Here are some of the main advantages:

• Control over extraneous variables: Experimental design allows researchers to control for extraneous variables that may affect the outcome of the study. By manipulating the independent variable and holding all other variables constant, researchers can isolate the effect of the independent variable on the dependent variable.
• Establishing causality: Experimental design allows researchers to establish causality by manipulating the independent variable and observing its effect on the dependent variable. This allows researchers to determine whether changes in the independent variable cause changes in the dependent variable.
• Replication : Experimental design allows researchers to replicate their experiments to ensure that the findings are consistent and reliable. Replication is important for establishing the validity and generalizability of the findings.
• Random assignment: Experimental design often involves randomly assigning participants to conditions. This helps to ensure that individual differences between participants are evenly distributed across conditions, which increases the internal validity of the study.
• Precision : Experimental design allows researchers to measure variables with precision, which can increase the accuracy and reliability of the data.
• Generalizability : If the study is well-designed, experimental design can increase the generalizability of the findings. By controlling for extraneous variables and using random assignment, researchers can increase the likelihood that the findings will apply to other populations and contexts.

Limitations of Experimental Design

Experimental design has some limitations that researchers should be aware of. Here are some of the main limitations:

• Artificiality : Experimental design often involves creating artificial situations that may not reflect real-world situations. This can limit the external validity of the findings, or the extent to which the findings can be generalized to real-world settings.
• Ethical concerns: Some experimental designs may raise ethical concerns, particularly if they involve manipulating variables that could cause harm to participants or if they involve deception.
• Participant bias : Participants in experimental studies may modify their behavior in response to the experiment, which can lead to participant bias.
• Limited generalizability: The conditions of the experiment may not reflect the complexities of real-world situations. As a result, the findings may not be applicable to all populations and contexts.
• Cost and time : Experimental design can be expensive and time-consuming, particularly if the experiment requires specialized equipment or if the sample size is large.
• Researcher bias : Researchers may unintentionally bias the results of the experiment if they have expectations or preferences for certain outcomes.
• Lack of feasibility : Experimental design may not be feasible in some cases, particularly if the research question involves variables that cannot be manipulated or controlled.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Design Trend: Experimental Typefaces & Fonts

One of the biggest trends in website design is experimental typefaces. These funky, unique, and always special fonts can serve as the backbone of a project or provide a special spark.

Experimental typefaces come in a variety of formats from handwriting styles to interesting serifs to color or animated fonts. There’s something for everyone with fonts that are a little bit different (or even customized).

Here’s a look at this design trend and how to make it work for you.

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What is an experimental typeface.

First impressions of these text styles include quirky lines, colors, and letterforms. They have flair and plenty of personality.

Moreover, experimental typefaces have a strong visual presence. Maybe the x-heights of characters differ or rules of letterform shape and spacing are abandoned. There may be hints of animation or an abundance of color.

What might be most important when thinking about, designing, or using experimental typefaces is the push and pull between uniqueness and readability.

Many of these typefaces are immensely unusual. That makes them beautiful and visually appealing.

So, when we talk about experimental typefaces it is important to think about display typography.

It’s also important to note that while some experimental typefaces have a fully polished and finished feel, many are imperfect and don’t have the same technical precision as something like Helvetica. While families and character sets tend to be more limited, you may find more glyphs, swashes, or behaviors that you would not find with more traditional typefaces.

While this category of typography is “newer” to a website and digital design, it is not new. Experimental typefaces have been at the root of poster design for decades. Just look at the cool type treatment for the promotion of “Vertigo” (1958, above).

Search “iconic movie poster” and you’ll be flooded with decades of examples of experimental typography in action. While trends in typography change, much of what defines this style remains the same.

Characteristics of Experimental Typefaces

A key characteristic of the style is overall creativity which shows words, letters, and individual characters in a different way.

Sometimes this means they can be difficult to read.

When working with this kind of type element, there’s a delicate balance between typography as art and typography to convey a message. Use experimental typefaces with enough supporting elements – backgrounds, visual cues or images, text in other typefaces – to ensure that messaging is clear and understandable, even if the display type takes a moment to discern.

As with other typefaces, experimental styles can come in quite a few forms. Experimental typefaces can serve a variety of roles with a purely visual design, designed for a specific commercial or display purpose, or to draw attention to a project.

You may note that experimental typefaces can be:

• Edgy and funky with strange lines, missing strokes, or unusual swashes
• Animated or function with motion
• Three-dimensional
• Include illustrations as part or as the whole of the typeface
• Color fonts
• Variable fonts
• Truly custom with a style that only appears in one place

Tips for Using Experimental Typefaces Well

Experimental typefaces aren’t for everyone or every project. They work best when you have a specific typography goal in mind – such as creating a specific feel or using type as art.

Many of these type styles can create strong emotional vibes or limit readability, so having a goal is key.

When it comes to using experimental typefaces keep these things in mind:

• The experimental typeface is your one design “trick;” keep the rest of the project streamlined visually.
• Support experimental typefaces with highly readable secondary fonts to ensure your message gets across.
• Create ample contrast, including color and space, for experimental options. Color and animated fonts may need more room to breathe than some other styles.
• Most experimental fonts are best for display use and should be avoided in body copy or long blocks of text.
• Play with lettering and the actual words you plan to use. With highly interesting and unique styles, some fonts might not look great with some letter combinations. Work with ligatures, swashes, and overlaps to ensure you have the best typeface for your words.

5 Places to Find Experimental Typefaces

You can find experimental typefaces in all the places you traditionally look for fonts. Finding them is a know-it-when-you-see-it kind of thing.

There are free and paid options out there with many independent type designers offering experimental options. Mainstream foundries and typography vendors also have options in the style and some type houses even focus primarily on experimental styles.

If you are struggling to find an experimental option that works for you, try one of these five sources.

• Typelab: The studio includes more than 20 type designers and their projects. (They have a fun space promotion running now with cool animations of various typefaces, above.)
• Future Fonts: Find funky and high-quality fonts that are brand new and don’t have a lot of use. There’s plenty of experimentation happening here and you can buy typefaces before they are finished and get updates.
• Font Space: Browse more than 124 free typefaces that have been tagged experimental by the designer.
• MyFonts: With hundreds of premium options available this list includes everything from modern designs to lettering that is best suited as art.
• Envato Elements: With 48 options to choose from, you’ll find anything from scripts to variable fonts to animated styles.

5 Designs with Beautiful Experimental Typefaces

San Diego Design Week

Jessica Bayer

Fiddle.Digital

The best part about working with experimental fonts is that you get to try something new. You’ll display a typeface that isn’t the overused Helvetica or Roboto and create personality for your project.

While these fonts aren’t for everyone, they can add a perfect finish to some projects with flair.

Experimental Design: Types, Examples & Methods

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Experimental design refers to how participants are allocated to different groups in an experiment. Types of design include repeated measures, independent groups, and matched pairs designs.

Probably the most common way to design an experiment in psychology is to divide the participants into two groups, the experimental group and the control group, and then introduce a change to the experimental group, not the control group.

The researcher must decide how he/she will allocate their sample to the different experimental groups.  For example, if there are 10 participants, will all 10 participants participate in both groups (e.g., repeated measures), or will the participants be split in half and take part in only one group each?

Three types of experimental designs are commonly used:

1. Independent Measures

Independent measures design, also known as between-groups , is an experimental design where different participants are used in each condition of the independent variable.  This means that each condition of the experiment includes a different group of participants.

This should be done by random allocation, ensuring that each participant has an equal chance of being assigned to one group.

Independent measures involve using two separate groups of participants, one in each condition. For example:

• Con : More people are needed than with the repeated measures design (i.e., more time-consuming).
• Pro : Avoids order effects (such as practice or fatigue) as people participate in one condition only.  If a person is involved in several conditions, they may become bored, tired, and fed up by the time they come to the second condition or become wise to the requirements of the experiment!
• Con : Differences between participants in the groups may affect results, for example, variations in age, gender, or social background.  These differences are known as participant variables (i.e., a type of extraneous variable ).
• Control : After the participants have been recruited, they should be randomly assigned to their groups. This should ensure the groups are similar, on average (reducing participant variables).

2. Repeated Measures Design

Repeated Measures design is an experimental design where the same participants participate in each independent variable condition.  This means that each experiment condition includes the same group of participants.

Repeated Measures design is also known as within-groups or within-subjects design .

• Pro : As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
• Con : There may be order effects. Order effects refer to the order of the conditions affecting the participants’ behavior.  Performance in the second condition may be better because the participants know what to do (i.e., practice effect).  Or their performance might be worse in the second condition because they are tired (i.e., fatigue effect). This limitation can be controlled using counterbalancing.
• Pro : Fewer people are needed as they participate in all conditions (i.e., saves time).
• Control : To combat order effects, the researcher counter-balances the order of the conditions for the participants.  Alternating the order in which participants perform in different conditions of an experiment.

Counterbalancing

Suppose we used a repeated measures design in which all of the participants first learned words in “loud noise” and then learned them in “no noise.”

We expect the participants to learn better in “no noise” because of order effects, such as practice. However, a researcher can control for order effects using counterbalancing.

The sample would be split into two groups: experimental (A) and control (B).  For example, group 1 does ‘A’ then ‘B,’ and group 2 does ‘B’ then ‘A.’ This is to eliminate order effects.

Although order effects occur for each participant, they balance each other out in the results because they occur equally in both groups.

3. Matched Pairs Design

A matched pairs design is an experimental design where pairs of participants are matched in terms of key variables, such as age or socioeconomic status. One member of each pair is then placed into the experimental group and the other member into the control group .

One member of each matched pair must be randomly assigned to the experimental group and the other to the control group.

• Con : If one participant drops out, you lose 2 PPs’ data.
• Pro : Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
• Con : Very time-consuming trying to find closely matched pairs.
• Pro : It avoids order effects, so counterbalancing is not necessary.
• Con : Impossible to match people exactly unless they are identical twins!
• Control : Members of each pair should be randomly assigned to conditions. However, this does not solve all these problems.

Experimental design refers to how participants are allocated to an experiment’s different conditions (or IV levels). There are three types:

1. Independent measures / between-groups : Different participants are used in each condition of the independent variable.

2. Repeated measures /within groups : The same participants take part in each condition of the independent variable.

3. Matched pairs : Each condition uses different participants, but they are matched in terms of important characteristics, e.g., gender, age, intelligence, etc.

Learning Check

Read about each of the experiments below. For each experiment, identify (1) which experimental design was used; and (2) why the researcher might have used that design.

1 . To compare the effectiveness of two different types of therapy for depression, depressed patients were assigned to receive either cognitive therapy or behavior therapy for a 12-week period.

The researchers attempted to ensure that the patients in the two groups had similar severity of depressed symptoms by administering a standardized test of depression to each participant, then pairing them according to the severity of their symptoms.

2 . To assess the difference in reading comprehension between 7 and 9-year-olds, a researcher recruited each group from a local primary school. They were given the same passage of text to read and then asked a series of questions to assess their understanding.

3 . To assess the effectiveness of two different ways of teaching reading, a group of 5-year-olds was recruited from a primary school. Their level of reading ability was assessed, and then they were taught using scheme one for 20 weeks.

At the end of this period, their reading was reassessed, and a reading improvement score was calculated. They were then taught using scheme two for a further 20 weeks, and another reading improvement score for this period was calculated. The reading improvement scores for each child were then compared.

4 . To assess the effect of the organization on recall, a researcher randomly assigned student volunteers to two conditions.

Condition one attempted to recall a list of words that were organized into meaningful categories; condition two attempted to recall the same words, randomly grouped on the page.

Experiment Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of taking part in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

19+ Experimental Design Examples (Methods + Types)

Ever wondered how scientists discover new medicines, psychologists learn about behavior, or even how marketers figure out what kind of ads you like? Well, they all have something in common: they use a special plan or recipe called an "experimental design."

Imagine you're baking cookies. You can't just throw random amounts of flour, sugar, and chocolate chips into a bowl and hope for the best. You follow a recipe, right? Scientists and researchers do something similar. They follow a "recipe" called an experimental design to make sure their experiments are set up in a way that the answers they find are meaningful and reliable.

Experimental design is the roadmap researchers use to answer questions. It's a set of rules and steps that researchers follow to collect information, or "data," in a way that is fair, accurate, and makes sense.

Long ago, people didn't have detailed game plans for experiments. They often just tried things out and saw what happened. But over time, people got smarter about this. They started creating structured plans—what we now call experimental designs—to get clearer, more trustworthy answers to their questions.

In this article, we'll take you on a journey through the world of experimental designs. We'll talk about the different types, or "flavors," of experimental designs, where they're used, and even give you a peek into how they came to be.

What Is Experimental Design?

Alright, before we dive into the different types of experimental designs, let's get crystal clear on what experimental design actually is.

Imagine you're a detective trying to solve a mystery. You need clues, right? Well, in the world of research, experimental design is like the roadmap that helps you find those clues. It's like the game plan in sports or the blueprint when you're building a house. Just like you wouldn't start building without a good blueprint, researchers won't start their studies without a strong experimental design.

So, why do we need experimental design? Think about baking a cake. If you toss ingredients into a bowl without measuring, you'll end up with a mess instead of a tasty dessert.

Similarly, in research, if you don't have a solid plan, you might get confusing or incorrect results. A good experimental design helps you ask the right questions ( think critically ), decide what to measure ( come up with an idea ), and figure out how to measure it (test it). It also helps you consider things that might mess up your results, like outside influences you hadn't thought of.

For example, let's say you want to find out if listening to music helps people focus better. Your experimental design would help you decide things like: Who are you going to test? What kind of music will you use? How will you measure focus? And, importantly, how will you make sure that it's really the music affecting focus and not something else, like the time of day or whether someone had a good breakfast?

In short, experimental design is the master plan that guides researchers through the process of collecting data, so they can answer questions in the most reliable way possible. It's like the GPS for the journey of discovery!

History of Experimental Design

Around 350 BCE, people like Aristotle were trying to figure out how the world works, but they mostly just thought really hard about things. They didn't test their ideas much. So while they were super smart, their methods weren't always the best for finding out the truth.

Fast forward to the Renaissance (14th to 17th centuries), a time of big changes and lots of curiosity. People like Galileo started to experiment by actually doing tests, like rolling balls down inclined planes to study motion. Galileo's work was cool because he combined thinking with doing. He'd have an idea, test it, look at the results, and then think some more. This approach was a lot more reliable than just sitting around and thinking.

Now, let's zoom ahead to the 18th and 19th centuries. This is when people like Francis Galton, an English polymath, started to get really systematic about experimentation. Galton was obsessed with measuring things. Seriously, he even tried to measure how good-looking people were ! His work helped create the foundations for a more organized approach to experiments.

Next stop: the early 20th century. Enter Ronald A. Fisher , a brilliant British statistician. Fisher was a game-changer. He came up with ideas that are like the bread and butter of modern experimental design.

Fisher invented the concept of the " control group "—that's a group of people or things that don't get the treatment you're testing, so you can compare them to those who do. He also stressed the importance of " randomization ," which means assigning people or things to different groups by chance, like drawing names out of a hat. This makes sure the experiment is fair and the results are trustworthy.

Around the same time, American psychologists like John B. Watson and B.F. Skinner were developing " behaviorism ." They focused on studying things that they could directly observe and measure, like actions and reactions.

Skinner even built boxes—called Skinner Boxes —to test how animals like pigeons and rats learn. Their work helped shape how psychologists design experiments today. Watson performed a very controversial experiment called The Little Albert experiment that helped describe behaviour through conditioning—in other words, how people learn to behave the way they do.

In the later part of the 20th century and into our time, computers have totally shaken things up. Researchers now use super powerful software to help design their experiments and crunch the numbers.

With computers, they can simulate complex experiments before they even start, which helps them predict what might happen. This is especially helpful in fields like medicine, where getting things right can be a matter of life and death.

Also, did you know that experimental designs aren't just for scientists in labs? They're used by people in all sorts of jobs, like marketing, education, and even video game design! Yes, someone probably ran an experiment to figure out what makes a game super fun to play.

So there you have it—a quick tour through the history of experimental design, from Aristotle's deep thoughts to Fisher's groundbreaking ideas, and all the way to today's computer-powered research. These designs are the recipes that help people from all walks of life find answers to their big questions.

Key Terms in Experimental Design

Before we dig into the different types of experimental designs, let's get comfy with some key terms. Understanding these terms will make it easier for us to explore the various types of experimental designs that researchers use to answer their big questions.

Independent Variable : This is what you change or control in your experiment to see what effect it has. Think of it as the "cause" in a cause-and-effect relationship. For example, if you're studying whether different types of music help people focus, the kind of music is the independent variable.

Dependent Variable : This is what you're measuring to see the effect of your independent variable. In our music and focus experiment, how well people focus is the dependent variable—it's what "depends" on the kind of music played.

Control Group : This is a group of people who don't get the special treatment or change you're testing. They help you see what happens when the independent variable is not applied. If you're testing whether a new medicine works, the control group would take a fake pill, called a placebo , instead of the real medicine.

Experimental Group : This is the group that gets the special treatment or change you're interested in. Going back to our medicine example, this group would get the actual medicine to see if it has any effect.

Randomization : This is like shaking things up in a fair way. You randomly put people into the control or experimental group so that each group is a good mix of different kinds of people. This helps make the results more reliable.

Sample : This is the group of people you're studying. They're a "sample" of a larger group that you're interested in. For instance, if you want to know how teenagers feel about a new video game, you might study a sample of 100 teenagers.

Bias : This is anything that might tilt your experiment one way or another without you realizing it. Like if you're testing a new kind of dog food and you only test it on poodles, that could create a bias because maybe poodles just really like that food and other breeds don't.

Data : This is the information you collect during the experiment. It's like the treasure you find on your journey of discovery!

Replication : This means doing the experiment more than once to make sure your findings hold up. It's like double-checking your answers on a test.

Hypothesis : This is your educated guess about what will happen in the experiment. It's like predicting the end of a movie based on the first half.

Steps of Experimental Design

Alright, let's say you're all fired up and ready to run your own experiment. Cool! But where do you start? Well, designing an experiment is a bit like planning a road trip. There are some key steps you've got to take to make sure you reach your destination. Let's break it down:

• Ask a Question : Before you hit the road, you've got to know where you're going. Same with experiments. You start with a question you want to answer, like "Does eating breakfast really make you do better in school?"
• Do Some Homework : Before you pack your bags, you look up the best places to visit, right? In science, this means reading up on what other people have already discovered about your topic.
• Form a Hypothesis : This is your educated guess about what you think will happen. It's like saying, "I bet this route will get us there faster."
• Plan the Details : Now you decide what kind of car you're driving (your experimental design), who's coming with you (your sample), and what snacks to bring (your variables).
• Randomization : Remember, this is like shuffling a deck of cards. You want to mix up who goes into your control and experimental groups to make sure it's a fair test.
• Run the Experiment : Finally, the rubber hits the road! You carry out your plan, making sure to collect your data carefully.
• Analyze the Data : Once the trip's over, you look at your photos and decide which ones are keepers. In science, this means looking at your data to see what it tells you.
• Draw Conclusions : Based on your data, did you find an answer to your question? This is like saying, "Yep, that route was faster," or "Nope, we hit a ton of traffic."
• Share Your Findings : After a great trip, you want to tell everyone about it, right? Scientists do the same by publishing their results so others can learn from them.
• Do It Again? : Sometimes one road trip just isn't enough. In the same way, scientists often repeat their experiments to make sure their findings are solid.

So there you have it! Those are the basic steps you need to follow when you're designing an experiment. Each step helps make sure that you're setting up a fair and reliable way to find answers to your big questions.

Let's get into examples of experimental designs.

1) True Experimental Design

In the world of experiments, the True Experimental Design is like the superstar quarterback everyone talks about. Born out of the early 20th-century work of statisticians like Ronald A. Fisher, this design is all about control, precision, and reliability.

Researchers carefully pick an independent variable to manipulate (remember, that's the thing they're changing on purpose) and measure the dependent variable (the effect they're studying). Then comes the magic trick—randomization. By randomly putting participants into either the control or experimental group, scientists make sure their experiment is as fair as possible.

No sneaky biases here!

True Experimental Design Pros

The pros of True Experimental Design are like the perks of a VIP ticket at a concert: you get the best and most trustworthy results. Because everything is controlled and randomized, you can feel pretty confident that the results aren't just a fluke.

True Experimental Design Cons

However, there's a catch. Sometimes, it's really tough to set up these experiments in a real-world situation. Imagine trying to control every single detail of your day, from the food you eat to the air you breathe. Not so easy, right?

True Experimental Design Uses

The fields that get the most out of True Experimental Designs are those that need super reliable results, like medical research.

When scientists were developing COVID-19 vaccines, they used this design to run clinical trials. They had control groups that received a placebo (a harmless substance with no effect) and experimental groups that got the actual vaccine. Then they measured how many people in each group got sick. By comparing the two, they could say, "Yep, this vaccine works!"

So next time you read about a groundbreaking discovery in medicine or technology, chances are a True Experimental Design was the VIP behind the scenes, making sure everything was on point. It's been the go-to for rigorous scientific inquiry for nearly a century, and it's not stepping off the stage anytime soon.

2) Quasi-Experimental Design

So, let's talk about the Quasi-Experimental Design. Think of this one as the cool cousin of True Experimental Design. It wants to be just like its famous relative, but it's a bit more laid-back and flexible. You'll find quasi-experimental designs when it's tricky to set up a full-blown True Experimental Design with all the bells and whistles.

Quasi-experiments still play with an independent variable, just like their stricter cousins. The big difference? They don't use randomization. It's like wanting to divide a bag of jelly beans equally between your friends, but you can't quite do it perfectly.

In real life, it's often not possible or ethical to randomly assign people to different groups, especially when dealing with sensitive topics like education or social issues. And that's where quasi-experiments come in.

Quasi-Experimental Design Pros

Even though they lack full randomization, quasi-experimental designs are like the Swiss Army knives of research: versatile and practical. They're especially popular in fields like education, sociology, and public policy.

For instance, when researchers wanted to figure out if the Head Start program , aimed at giving young kids a "head start" in school, was effective, they used a quasi-experimental design. They couldn't randomly assign kids to go or not go to preschool, but they could compare kids who did with kids who didn't.

Quasi-Experimental Design Cons

Of course, quasi-experiments come with their own bag of pros and cons. On the plus side, they're easier to set up and often cheaper than true experiments. But the flip side is that they're not as rock-solid in their conclusions. Because the groups aren't randomly assigned, there's always that little voice saying, "Hey, are we missing something here?"

Quasi-Experimental Design Uses

Quasi-Experimental Design gained traction in the mid-20th century. Researchers were grappling with real-world problems that didn't fit neatly into a laboratory setting. Plus, as society became more aware of ethical considerations, the need for flexible designs increased. So, the quasi-experimental approach was like a breath of fresh air for scientists wanting to study complex issues without a laundry list of restrictions.

In short, if True Experimental Design is the superstar quarterback, Quasi-Experimental Design is the versatile player who can adapt and still make significant contributions to the game.

3) Pre-Experimental Design

Now, let's talk about the Pre-Experimental Design. Imagine it as the beginner's skateboard you get before you try out for all the cool tricks. It has wheels, it rolls, but it's not built for the professional skatepark.

Similarly, pre-experimental designs give researchers a starting point. They let you dip your toes in the water of scientific research without diving in head-first.

So, what's the deal with pre-experimental designs?

Pre-Experimental Designs are the basic, no-frills versions of experiments. Researchers still mess around with an independent variable and measure a dependent variable, but they skip over the whole randomization thing and often don't even have a control group.

It's like baking a cake but forgetting the frosting and sprinkles; you'll get some results, but they might not be as complete or reliable as you'd like.

Pre-Experimental Design Pros

Why use such a simple setup? Because sometimes, you just need to get the ball rolling. Pre-experimental designs are great for quick-and-dirty research when you're short on time or resources. They give you a rough idea of what's happening, which you can use to plan more detailed studies later.

A good example of this is early studies on the effects of screen time on kids. Researchers couldn't control every aspect of a child's life, but they could easily ask parents to track how much time their kids spent in front of screens and then look for trends in behavior or school performance.

Pre-Experimental Design Cons

But here's the catch: pre-experimental designs are like that first draft of an essay. It helps you get your ideas down, but you wouldn't want to turn it in for a grade. Because these designs lack the rigorous structure of true or quasi-experimental setups, they can't give you rock-solid conclusions. They're more like clues or signposts pointing you in a certain direction.

Pre-Experimental Design Uses

This type of design became popular in the early stages of various scientific fields. Researchers used them to scratch the surface of a topic, generate some initial data, and then decide if it's worth exploring further. In other words, pre-experimental designs were the stepping stones that led to more complex, thorough investigations.

So, while Pre-Experimental Design may not be the star player on the team, it's like the practice squad that helps everyone get better. It's the starting point that can lead to bigger and better things.

4) Factorial Design

Now, buckle up, because we're moving into the world of Factorial Design, the multi-tasker of the experimental universe.

Imagine juggling not just one, but multiple balls in the air—that's what researchers do in a factorial design.

In Factorial Design, researchers are not satisfied with just studying one independent variable. Nope, they want to study two or more at the same time to see how they interact.

It's like cooking with several spices to see how they blend together to create unique flavors.

Factorial Design became the talk of the town with the rise of computers. Why? Because this design produces a lot of data, and computers are the number crunchers that help make sense of it all. So, thanks to our silicon friends, researchers can study complicated questions like, "How do diet AND exercise together affect weight loss?" instead of looking at just one of those factors.

Factorial Design Pros

This design's main selling point is its ability to explore interactions between variables. For instance, maybe a new study drug works really well for young people but not so great for older adults. A factorial design could reveal that age is a crucial factor, something you might miss if you only studied the drug's effectiveness in general. It's like being a detective who looks for clues not just in one room but throughout the entire house.

Factorial Design Cons

However, factorial designs have their own bag of challenges. First off, they can be pretty complicated to set up and run. Imagine coordinating a four-way intersection with lots of cars coming from all directions—you've got to make sure everything runs smoothly, or you'll end up with a traffic jam. Similarly, researchers need to carefully plan how they'll measure and analyze all the different variables.

Factorial Design Uses

Factorial designs are widely used in psychology to untangle the web of factors that influence human behavior. They're also popular in fields like marketing, where companies want to understand how different aspects like price, packaging, and advertising influence a product's success.

And speaking of success, the factorial design has been a hit since statisticians like Ronald A. Fisher (yep, him again!) expanded on it in the early-to-mid 20th century. It offered a more nuanced way of understanding the world, proving that sometimes, to get the full picture, you've got to juggle more than one ball at a time.

So, if True Experimental Design is the quarterback and Quasi-Experimental Design is the versatile player, Factorial Design is the strategist who sees the entire game board and makes moves accordingly.

5) Longitudinal Design

Alright, let's take a step into the world of Longitudinal Design. Picture it as the grand storyteller, the kind who doesn't just tell you about a single event but spins an epic tale that stretches over years or even decades. This design isn't about quick snapshots; it's about capturing the whole movie of someone's life or a long-running process.

You know how you might take a photo every year on your birthday to see how you've changed? Longitudinal Design is kind of like that, but for scientific research.

With Longitudinal Design, instead of measuring something just once, researchers come back again and again, sometimes over many years, to see how things are going. This helps them understand not just what's happening, but why it's happening and how it changes over time.

This design really started to shine in the latter half of the 20th century, when researchers began to realize that some questions can't be answered in a hurry. Think about studies that look at how kids grow up, or research on how a certain medicine affects you over a long period. These aren't things you can rush.

The famous Framingham Heart Study , started in 1948, is a prime example. It's been studying heart health in a small town in Massachusetts for decades, and the findings have shaped what we know about heart disease.

Longitudinal Design Pros

So, what's to love about Longitudinal Design? First off, it's the go-to for studying change over time, whether that's how people age or how a forest recovers from a fire.

Longitudinal Design Cons

But it's not all sunshine and rainbows. Longitudinal studies take a lot of patience and resources. Plus, keeping track of participants over many years can be like herding cats—difficult and full of surprises.

Longitudinal Design Uses

Despite these challenges, longitudinal studies have been key in fields like psychology, sociology, and medicine. They provide the kind of deep, long-term insights that other designs just can't match.

So, if the True Experimental Design is the superstar quarterback, and the Quasi-Experimental Design is the flexible athlete, then the Factorial Design is the strategist, and the Longitudinal Design is the wise elder who has seen it all and has stories to tell.

6) Cross-Sectional Design

Now, let's flip the script and talk about Cross-Sectional Design, the polar opposite of the Longitudinal Design. If Longitudinal is the grand storyteller, think of Cross-Sectional as the snapshot photographer. It captures a single moment in time, like a selfie that you take to remember a fun day. Researchers using this design collect all their data at one point, providing a kind of "snapshot" of whatever they're studying.

In a Cross-Sectional Design, researchers look at multiple groups all at the same time to see how they're different or similar.

This design rose to popularity in the mid-20th century, mainly because it's so quick and efficient. Imagine wanting to know how people of different ages feel about a new video game. Instead of waiting for years to see how opinions change, you could just ask people of all ages what they think right now. That's Cross-Sectional Design for you—fast and straightforward.

You'll find this type of research everywhere from marketing studies to healthcare. For instance, you might have heard about surveys asking people what they think about a new product or political issue. Those are usually cross-sectional studies, aimed at getting a quick read on public opinion.

Cross-Sectional Design Pros

So, what's the big deal with Cross-Sectional Design? Well, it's the go-to when you need answers fast and don't have the time or resources for a more complicated setup.

Cross-Sectional Design Cons

Remember, speed comes with trade-offs. While you get your results quickly, those results are stuck in time. They can't tell you how things change or why they're changing, just what's happening right now.

Cross-Sectional Design Uses

Also, because they're so quick and simple, cross-sectional studies often serve as the first step in research. They give scientists an idea of what's going on so they can decide if it's worth digging deeper. In that way, they're a bit like a movie trailer, giving you a taste of the action to see if you're interested in seeing the whole film.

So, in our lineup of experimental designs, if True Experimental Design is the superstar quarterback and Longitudinal Design is the wise elder, then Cross-Sectional Design is like the speedy running back—fast, agile, but not designed for long, drawn-out plays.

7) Correlational Design

Next on our roster is the Correlational Design, the keen observer of the experimental world. Imagine this design as the person at a party who loves people-watching. They don't interfere or get involved; they just observe and take mental notes about what's going on.

In a correlational study, researchers don't change or control anything; they simply observe and measure how two variables relate to each other.

The correlational design has roots in the early days of psychology and sociology. Pioneers like Sir Francis Galton used it to study how qualities like intelligence or height could be related within families.

This design is all about asking, "Hey, when this thing happens, does that other thing usually happen too?" For example, researchers might study whether students who have more study time get better grades or whether people who exercise more have lower stress levels.

One of the most famous correlational studies you might have heard of is the link between smoking and lung cancer. Back in the mid-20th century, researchers started noticing that people who smoked a lot also seemed to get lung cancer more often. They couldn't say smoking caused cancer—that would require a true experiment—but the strong correlation was a red flag that led to more research and eventually, health warnings.

Correlational Design Pros

This design is great at proving that two (or more) things can be related. Correlational designs can help prove that more detailed research is needed on a topic. They can help us see patterns or possible causes for things that we otherwise might not have realized.

Correlational Design Cons

But here's where you need to be careful: correlational designs can be tricky. Just because two things are related doesn't mean one causes the other. That's like saying, "Every time I wear my lucky socks, my team wins." Well, it's a fun thought, but those socks aren't really controlling the game.

Correlational Design Uses

Despite this limitation, correlational designs are popular in psychology, economics, and epidemiology, to name a few fields. They're often the first step in exploring a possible relationship between variables. Once a strong correlation is found, researchers may decide to conduct more rigorous experimental studies to examine cause and effect.

So, if the True Experimental Design is the superstar quarterback and the Longitudinal Design is the wise elder, the Factorial Design is the strategist, and the Cross-Sectional Design is the speedster, then the Correlational Design is the clever scout, identifying interesting patterns but leaving the heavy lifting of proving cause and effect to the other types of designs.

8) Meta-Analysis

Last but not least, let's talk about Meta-Analysis, the librarian of experimental designs.

If other designs are all about creating new research, Meta-Analysis is about gathering up everyone else's research, sorting it, and figuring out what it all means when you put it together.

Imagine a jigsaw puzzle where each piece is a different study. Meta-Analysis is the process of fitting all those pieces together to see the big picture.

The concept of Meta-Analysis started to take shape in the late 20th century, when computers became powerful enough to handle massive amounts of data. It was like someone handed researchers a super-powered magnifying glass, letting them examine multiple studies at the same time to find common trends or results.

You might have heard of the Cochrane Reviews in healthcare . These are big collections of meta-analyses that help doctors and policymakers figure out what treatments work best based on all the research that's been done.

For example, if ten different studies show that a certain medicine helps lower blood pressure, a meta-analysis would pull all that information together to give a more accurate answer.

Meta-Analysis Pros

The beauty of Meta-Analysis is that it can provide really strong evidence. Instead of relying on one study, you're looking at the whole landscape of research on a topic.

Meta-Analysis Cons

However, it does have some downsides. For one, Meta-Analysis is only as good as the studies it includes. If those studies are flawed, the meta-analysis will be too. It's like baking a cake: if you use bad ingredients, it doesn't matter how good your recipe is—the cake won't turn out well.

Meta-Analysis Uses

Despite these challenges, meta-analyses are highly respected and widely used in many fields like medicine, psychology, and education. They help us make sense of a world that's bursting with information by showing us the big picture drawn from many smaller snapshots.

So, in our all-star lineup, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, the Factorial Design is the strategist, the Cross-Sectional Design is the speedster, and the Correlational Design is the scout, then the Meta-Analysis is like the coach, using insights from everyone else's plays to come up with the best game plan.

9) Non-Experimental Design

Now, let's talk about a player who's a bit of an outsider on this team of experimental designs—the Non-Experimental Design. Think of this design as the commentator or the journalist who covers the game but doesn't actually play.

In a Non-Experimental Design, researchers are like reporters gathering facts, but they don't interfere or change anything. They're simply there to describe and analyze.

Non-Experimental Design Pros

So, what's the deal with Non-Experimental Design? Its strength is in description and exploration. It's really good for studying things as they are in the real world, without changing any conditions.

Non-Experimental Design Cons

Because a non-experimental design doesn't manipulate variables, it can't prove cause and effect. It's like a weather reporter: they can tell you it's raining, but they can't tell you why it's raining.

The downside? Since researchers aren't controlling variables, it's hard to rule out other explanations for what they observe. It's like hearing one side of a story—you get an idea of what happened, but it might not be the complete picture.

Non-Experimental Design Uses

Non-Experimental Design has always been a part of research, especially in fields like anthropology, sociology, and some areas of psychology.

For instance, if you've ever heard of studies that describe how people behave in different cultures or what teens like to do in their free time, that's often Non-Experimental Design at work. These studies aim to capture the essence of a situation, like painting a portrait instead of taking a snapshot.

One well-known example you might have heard about is the Kinsey Reports from the 1940s and 1950s, which described sexual behavior in men and women. Researchers interviewed thousands of people but didn't manipulate any variables like you would in a true experiment. They simply collected data to create a comprehensive picture of the subject matter.

So, in our metaphorical team of research designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, and Meta-Analysis is the coach, then Non-Experimental Design is the sports journalist—always present, capturing the game, but not part of the action itself.

10) Repeated Measures Design

Time to meet the Repeated Measures Design, the time traveler of our research team. If this design were a player in a sports game, it would be the one who keeps revisiting past plays to figure out how to improve the next one.

Repeated Measures Design is all about studying the same people or subjects multiple times to see how they change or react under different conditions.

The idea behind Repeated Measures Design isn't new; it's been around since the early days of psychology and medicine. You could say it's a cousin to the Longitudinal Design, but instead of looking at how things naturally change over time, it focuses on how the same group reacts to different things.

Imagine a study looking at how a new energy drink affects people's running speed. Instead of comparing one group that drank the energy drink to another group that didn't, a Repeated Measures Design would have the same group of people run multiple times—once with the energy drink, and once without. This way, you're really zeroing in on the effect of that energy drink, making the results more reliable.

Repeated Measures Design Pros

The strong point of Repeated Measures Design is that it's super focused. Because it uses the same subjects, you don't have to worry about differences between groups messing up your results.

Repeated Measures Design Cons

But the downside? Well, people can get tired or bored if they're tested too many times, which might affect how they respond.

Repeated Measures Design Uses

A famous example of this design is the "Little Albert" experiment, conducted by John B. Watson and Rosalie Rayner in 1920. In this study, a young boy was exposed to a white rat and other stimuli several times to see how his emotional responses changed. Though the ethical standards of this experiment are often criticized today, it was groundbreaking in understanding conditioned emotional responses.

In our metaphorical lineup of research designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, and Non-Experimental Design is the journalist, then Repeated Measures Design is the time traveler—always looping back to fine-tune the game plan.

11) Crossover Design

Next up is Crossover Design, the switch-hitter of the research world. If you're familiar with baseball, you'll know a switch-hitter is someone who can bat both right-handed and left-handed.

In a similar way, Crossover Design allows subjects to experience multiple conditions, flipping them around so that everyone gets a turn in each role.

This design is like the utility player on our team—versatile, flexible, and really good at adapting.

The Crossover Design has its roots in medical research and has been popular since the mid-20th century. It's often used in clinical trials to test the effectiveness of different treatments.

Crossover Design Pros

The neat thing about this design is that it allows each participant to serve as their own control group. Imagine you're testing two new kinds of headache medicine. Instead of giving one type to one group and another type to a different group, you'd give both kinds to the same people but at different times.

Crossover Design Cons

What's the big deal with Crossover Design? Its major strength is in reducing the "noise" that comes from individual differences. Since each person experiences all conditions, it's easier to see real effects. However, there's a catch. This design assumes that there's no lasting effect from the first condition when you switch to the second one. That might not always be true. If the first treatment has a long-lasting effect, it could mess up the results when you switch to the second treatment.

Crossover Design Uses

A well-known example of Crossover Design is in studies that look at the effects of different types of diets—like low-carb vs. low-fat diets. Researchers might have participants follow a low-carb diet for a few weeks, then switch them to a low-fat diet. By doing this, they can more accurately measure how each diet affects the same group of people.

In our team of experimental designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, and Repeated Measures Design is the time traveler, then Crossover Design is the versatile utility player—always ready to adapt and play multiple roles to get the most accurate results.

12) Cluster Randomized Design

Meet the Cluster Randomized Design, the team captain of group-focused research. In our imaginary lineup of experimental designs, if other designs focus on individual players, then Cluster Randomized Design is looking at how the entire team functions.

This approach is especially common in educational and community-based research, and it's been gaining traction since the late 20th century.

Here's how Cluster Randomized Design works: Instead of assigning individual people to different conditions, researchers assign entire groups, or "clusters." These could be schools, neighborhoods, or even entire towns. This helps you see how the new method works in a real-world setting.

Imagine you want to see if a new anti-bullying program really works. Instead of selecting individual students, you'd introduce the program to a whole school or maybe even several schools, and then compare the results to schools without the program.

Cluster Randomized Design Pros

Why use Cluster Randomized Design? Well, sometimes it's just not practical to assign conditions at the individual level. For example, you can't really have half a school following a new reading program while the other half sticks with the old one; that would be way too confusing! Cluster Randomization helps get around this problem by treating each "cluster" as its own mini-experiment.

Cluster Randomized Design Cons

There's a downside, too. Because entire groups are assigned to each condition, there's a risk that the groups might be different in some important way that the researchers didn't account for. That's like having one sports team that's full of veterans playing against a team of rookies; the match wouldn't be fair.

Cluster Randomized Design Uses

A famous example is the research conducted to test the effectiveness of different public health interventions, like vaccination programs. Researchers might roll out a vaccination program in one community but not in another, then compare the rates of disease in both.

In our metaphorical research team, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, and Crossover Design is the utility player, then Cluster Randomized Design is the team captain—always looking out for the group as a whole.

13) Mixed-Methods Design

Say hello to Mixed-Methods Design, the all-rounder or the "Renaissance player" of our research team.

Mixed-Methods Design uses a blend of both qualitative and quantitative methods to get a more complete picture, just like a Renaissance person who's good at lots of different things. It's like being good at both offense and defense in a sport; you've got all your bases covered!

Mixed-Methods Design is a fairly new kid on the block, becoming more popular in the late 20th and early 21st centuries as researchers began to see the value in using multiple approaches to tackle complex questions. It's the Swiss Army knife in our research toolkit, combining the best parts of other designs to be more versatile.

Here's how it could work: Imagine you're studying the effects of a new educational app on students' math skills. You might use quantitative methods like tests and grades to measure how much the students improve—that's the 'numbers part.'

But you also want to know how the students feel about math now, or why they think they got better or worse. For that, you could conduct interviews or have students fill out journals—that's the 'story part.'

Mixed-Methods Design Pros

So, what's the scoop on Mixed-Methods Design? The strength is its versatility and depth; you're not just getting numbers or stories, you're getting both, which gives a fuller picture.

Mixed-Methods Design Cons

But, it's also more challenging. Imagine trying to play two sports at the same time! You have to be skilled in different research methods and know how to combine them effectively.

Mixed-Methods Design Uses

A high-profile example of Mixed-Methods Design is research on climate change. Scientists use numbers and data to show temperature changes (quantitative), but they also interview people to understand how these changes are affecting communities (qualitative).

In our team of experimental designs, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, Crossover Design is the utility player, and Cluster Randomized Design is the team captain, then Mixed-Methods Design is the Renaissance player—skilled in multiple areas and able to bring them all together for a winning strategy.

14) Multivariate Design

Now, let's turn our attention to Multivariate Design, the multitasker of the research world.

If our lineup of research designs were like players on a basketball court, Multivariate Design would be the player dribbling, passing, and shooting all at once. This design doesn't just look at one or two things; it looks at several variables simultaneously to see how they interact and affect each other.

Multivariate Design is like baking a cake with many ingredients. Instead of just looking at how flour affects the cake, you also consider sugar, eggs, and milk all at once. This way, you understand how everything works together to make the cake taste good or bad.

Multivariate Design has been a go-to method in psychology, economics, and social sciences since the latter half of the 20th century. With the advent of computers and advanced statistical software, analyzing multiple variables at once became a lot easier, and Multivariate Design soared in popularity.

Multivariate Design Pros

So, what's the benefit of using Multivariate Design? Its power lies in its complexity. By studying multiple variables at the same time, you can get a really rich, detailed understanding of what's going on.

Multivariate Design Cons

But that complexity can also be a drawback. With so many variables, it can be tough to tell which ones are really making a difference and which ones are just along for the ride.

Multivariate Design Uses

Imagine you're a coach trying to figure out the best strategy to win games. You wouldn't just look at how many points your star player scores; you'd also consider assists, rebounds, turnovers, and maybe even how loud the crowd is. A Multivariate Design would help you understand how all these factors work together to determine whether you win or lose.

A well-known example of Multivariate Design is in market research. Companies often use this approach to figure out how different factors—like price, packaging, and advertising—affect sales. By studying multiple variables at once, they can find the best combination to boost profits.

In our metaphorical research team, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, Crossover Design is the utility player, Cluster Randomized Design is the team captain, and Mixed-Methods Design is the Renaissance player, then Multivariate Design is the multitasker—juggling many variables at once to get a fuller picture of what's happening.

15) Pretest-Posttest Design

Let's introduce Pretest-Posttest Design, the "Before and After" superstar of our research team. You've probably seen those before-and-after pictures in ads for weight loss programs or home renovations, right?

Well, this design is like that, but for science! Pretest-Posttest Design checks out what things are like before the experiment starts and then compares that to what things are like after the experiment ends.

This design is one of the classics, a staple in research for decades across various fields like psychology, education, and healthcare. It's so simple and straightforward that it has stayed popular for a long time.

In Pretest-Posttest Design, you measure your subject's behavior or condition before you introduce any changes—that's your "before" or "pretest." Then you do your experiment, and after it's done, you measure the same thing again—that's your "after" or "posttest."

Pretest-Posttest Design Pros

What makes Pretest-Posttest Design special? It's pretty easy to understand and doesn't require fancy statistics.

Pretest-Posttest Design Cons

But there are some pitfalls. For example, what if the kids in our math example get better at multiplication just because they're older or because they've taken the test before? That would make it hard to tell if the program is really effective or not.

Pretest-Posttest Design Uses

Let's say you're a teacher and you want to know if a new math program helps kids get better at multiplication. First, you'd give all the kids a multiplication test—that's your pretest. Then you'd teach them using the new math program. At the end, you'd give them the same test again—that's your posttest. If the kids do better on the second test, you might conclude that the program works.

One famous use of Pretest-Posttest Design is in evaluating the effectiveness of driver's education courses. Researchers will measure people's driving skills before and after the course to see if they've improved.

16) Solomon Four-Group Design

Next up is the Solomon Four-Group Design, the "chess master" of our research team. This design is all about strategy and careful planning. Named after Richard L. Solomon who introduced it in the 1940s, this method tries to correct some of the weaknesses in simpler designs, like the Pretest-Posttest Design.

Here's how it rolls: The Solomon Four-Group Design uses four different groups to test a hypothesis. Two groups get a pretest, then one of them receives the treatment or intervention, and both get a posttest. The other two groups skip the pretest, and only one of them receives the treatment before they both get a posttest.

Sound complicated? It's like playing 4D chess; you're thinking several moves ahead!

Solomon Four-Group Design Pros

What's the pro and con of the Solomon Four-Group Design? On the plus side, it provides really robust results because it accounts for so many variables.

Solomon Four-Group Design Cons

The downside? It's a lot of work and requires a lot of participants, making it more time-consuming and costly.

Solomon Four-Group Design Uses

Let's say you want to figure out if a new way of teaching history helps students remember facts better. Two classes take a history quiz (pretest), then one class uses the new teaching method while the other sticks with the old way. Both classes take another quiz afterward (posttest).

Meanwhile, two more classes skip the initial quiz, and then one uses the new method before both take the final quiz. Comparing all four groups will give you a much clearer picture of whether the new teaching method works and whether the pretest itself affects the outcome.

The Solomon Four-Group Design is less commonly used than simpler designs but is highly respected for its ability to control for more variables. It's a favorite in educational and psychological research where you really want to dig deep and figure out what's actually causing changes.

17) Adaptive Designs

Now, let's talk about Adaptive Designs, the chameleons of the experimental world.

Imagine you're a detective, and halfway through solving a case, you find a clue that changes everything. You wouldn't just stick to your old plan; you'd adapt and change your approach, right? That's exactly what Adaptive Designs allow researchers to do.

In an Adaptive Design, researchers can make changes to the study as it's happening, based on early results. In a traditional study, once you set your plan, you stick to it from start to finish.

Adaptive Design Pros

This method is particularly useful in fast-paced or high-stakes situations, like developing a new vaccine in the middle of a pandemic. The ability to adapt can save both time and resources, and more importantly, it can save lives by getting effective treatments out faster.

Adaptive Design Cons

But Adaptive Designs aren't without their drawbacks. They can be very complex to plan and carry out, and there's always a risk that the changes made during the study could introduce bias or errors.

Adaptive Design Uses

Adaptive Designs are most often seen in clinical trials, particularly in the medical and pharmaceutical fields.

For instance, if a new drug is showing really promising results, the study might be adjusted to give more participants the new treatment instead of a placebo. Or if one dose level is showing bad side effects, it might be dropped from the study.

The best part is, these changes are pre-planned. Researchers lay out in advance what changes might be made and under what conditions, which helps keep everything scientific and above board.

In terms of applications, besides their heavy usage in medical and pharmaceutical research, Adaptive Designs are also becoming increasingly popular in software testing and market research. In these fields, being able to quickly adjust to early results can give companies a significant advantage.

Adaptive Designs are like the agile startups of the research world—quick to pivot, keen to learn from ongoing results, and focused on rapid, efficient progress. However, they require a great deal of expertise and careful planning to ensure that the adaptability doesn't compromise the integrity of the research.

18) Bayesian Designs

Next, let's dive into Bayesian Designs, the data detectives of the research universe. Named after Thomas Bayes, an 18th-century statistician and minister, this design doesn't just look at what's happening now; it also takes into account what's happened before.

Imagine if you were a detective who not only looked at the evidence in front of you but also used your past cases to make better guesses about your current one. That's the essence of Bayesian Designs.

Bayesian Designs are like detective work in science. As you gather more clues (or data), you update your best guess on what's really happening. This way, your experiment gets smarter as it goes along.

In the world of research, Bayesian Designs are most notably used in areas where you have some prior knowledge that can inform your current study. For example, if earlier research shows that a certain type of medicine usually works well for a specific illness, a Bayesian Design would include that information when studying a new group of patients with the same illness.

Bayesian Design Pros

One of the major advantages of Bayesian Designs is their efficiency. Because they use existing data to inform the current experiment, often fewer resources are needed to reach a reliable conclusion.

Bayesian Design Cons

However, they can be quite complicated to set up and require a deep understanding of both statistics and the subject matter at hand.

Bayesian Design Uses

Bayesian Designs are highly valued in medical research, finance, environmental science, and even in Internet search algorithms. Their ability to continually update and refine hypotheses based on new evidence makes them particularly useful in fields where data is constantly evolving and where quick, informed decisions are crucial.

Here's a real-world example: In the development of personalized medicine, where treatments are tailored to individual patients, Bayesian Designs are invaluable. If a treatment has been effective for patients with similar genetics or symptoms in the past, a Bayesian approach can use that data to predict how well it might work for a new patient.

This type of design is also increasingly popular in machine learning and artificial intelligence. In these fields, Bayesian Designs help algorithms "learn" from past data to make better predictions or decisions in new situations. It's like teaching a computer to be a detective that gets better and better at solving puzzles the more puzzles it sees.

19) Covariate Adaptive Randomization

Now let's turn our attention to Covariate Adaptive Randomization, which you can think of as the "matchmaker" of experimental designs.

Picture a soccer coach trying to create the most balanced teams for a friendly match. They wouldn't just randomly assign players; they'd take into account each player's skills, experience, and other traits.

Covariate Adaptive Randomization is all about creating the most evenly matched groups possible for an experiment.

In traditional randomization, participants are allocated to different groups purely by chance. This is a pretty fair way to do things, but it can sometimes lead to unbalanced groups.

Imagine if all the professional-level players ended up on one soccer team and all the beginners on another; that wouldn't be a very informative match! Covariate Adaptive Randomization fixes this by using important traits or characteristics (called "covariates") to guide the randomization process.

Covariate Adaptive Randomization Pros

The benefits of this design are pretty clear: it aims for balance and fairness, making the final results more trustworthy.

Covariate Adaptive Randomization Cons

But it's not perfect. It can be complex to implement and requires a deep understanding of which characteristics are most important to balance.

Covariate Adaptive Randomization Uses

This design is particularly useful in medical trials. Let's say researchers are testing a new medication for high blood pressure. Participants might have different ages, weights, or pre-existing conditions that could affect the results.

Covariate Adaptive Randomization would make sure that each treatment group has a similar mix of these characteristics, making the results more reliable and easier to interpret.

In practical terms, this design is often seen in clinical trials for new drugs or therapies, but its principles are also applicable in fields like psychology, education, and social sciences.

For instance, in educational research, it might be used to ensure that classrooms being compared have similar distributions of students in terms of academic ability, socioeconomic status, and other factors.

Covariate Adaptive Randomization is like the wise elder of the group, ensuring that everyone has an equal opportunity to show their true capabilities, thereby making the collective results as reliable as possible.

20) Stepped Wedge Design

Let's now focus on the Stepped Wedge Design, a thoughtful and cautious member of the experimental design family.

Imagine you're trying out a new gardening technique, but you're not sure how well it will work. You decide to apply it to one section of your garden first, watch how it performs, and then gradually extend the technique to other sections. This way, you get to see its effects over time and across different conditions. That's basically how Stepped Wedge Design works.

In a Stepped Wedge Design, all participants or clusters start off in the control group, and then, at different times, they 'step' over to the intervention or treatment group. This creates a wedge-like pattern over time where more and more participants receive the treatment as the study progresses. It's like rolling out a new policy in phases, monitoring its impact at each stage before extending it to more people.

Stepped Wedge Design Pros

The Stepped Wedge Design offers several advantages. Firstly, it allows for the study of interventions that are expected to do more good than harm, which makes it ethically appealing.

Secondly, it's useful when resources are limited and it's not feasible to roll out a new treatment to everyone at once. Lastly, because everyone eventually receives the treatment, it can be easier to get buy-in from participants or organizations involved in the study.

Stepped Wedge Design Cons

However, this design can be complex to analyze because it has to account for both the time factor and the changing conditions in each 'step' of the wedge. And like any study where participants know they're receiving an intervention, there's the potential for the results to be influenced by the placebo effect or other biases.

Stepped Wedge Design Uses

This design is particularly useful in health and social care research. For instance, if a hospital wants to implement a new hygiene protocol, it might start in one department, assess its impact, and then roll it out to other departments over time. This allows the hospital to adjust and refine the new protocol based on real-world data before it's fully implemented.

In terms of applications, Stepped Wedge Designs are commonly used in public health initiatives, organizational changes in healthcare settings, and social policy trials. They are particularly useful in situations where an intervention is being rolled out gradually and it's important to understand its impacts at each stage.

21) Sequential Design

Next up is Sequential Design, the dynamic and flexible member of our experimental design family.

Imagine you're playing a video game where you can choose different paths. If you take one path and find a treasure chest, you might decide to continue in that direction. If you hit a dead end, you might backtrack and try a different route. Sequential Design operates in a similar fashion, allowing researchers to make decisions at different stages based on what they've learned so far.

In a Sequential Design, the experiment is broken down into smaller parts, or "sequences." After each sequence, researchers pause to look at the data they've collected. Based on those findings, they then decide whether to stop the experiment because they've got enough information, or to continue and perhaps even modify the next sequence.

Sequential Design Pros

This allows for a more efficient use of resources, as you're only continuing with the experiment if the data suggests it's worth doing so.

One of the great things about Sequential Design is its efficiency. Because you're making data-driven decisions along the way, you can often reach conclusions more quickly and with fewer resources.

Sequential Design Cons

However, it requires careful planning and expertise to ensure that these "stop or go" decisions are made correctly and without bias.

Sequential Design Uses

In terms of its applications, besides healthcare and medicine, Sequential Design is also popular in quality control in manufacturing, environmental monitoring, and financial modeling. In these areas, being able to make quick decisions based on incoming data can be a big advantage.

This design is often used in clinical trials involving new medications or treatments. For example, if early results show that a new drug has significant side effects, the trial can be stopped before more people are exposed to it.

On the flip side, if the drug is showing promising results, the trial might be expanded to include more participants or to extend the testing period.

Think of Sequential Design as the nimble athlete of experimental designs, capable of quick pivots and adjustments to reach the finish line in the most effective way possible. But just like an athlete needs a good coach, this design requires expert oversight to make sure it stays on the right track.

22) Field Experiments

Last but certainly not least, let's explore Field Experiments—the adventurers of the experimental design world.

Picture a scientist leaving the controlled environment of a lab to test a theory in the real world, like a biologist studying animals in their natural habitat or a social scientist observing people in a real community. These are Field Experiments, and they're all about getting out there and gathering data in real-world settings.

Field Experiments embrace the messiness of the real world, unlike laboratory experiments, where everything is controlled down to the smallest detail. This makes them both exciting and challenging.

Field Experiment Pros

On one hand, the results often give us a better understanding of how things work outside the lab.

While Field Experiments offer real-world relevance, they come with challenges like controlling for outside factors and the ethical considerations of intervening in people's lives without their knowledge.

Field Experiment Cons

On the other hand, the lack of control can make it harder to tell exactly what's causing what. Yet, despite these challenges, they remain a valuable tool for researchers who want to understand how theories play out in the real world.

Field Experiment Uses

Let's say a school wants to improve student performance. In a Field Experiment, they might change the school's daily schedule for one semester and keep track of how students perform compared to another school where the schedule remained the same.

Because the study is happening in a real school with real students, the results could be very useful for understanding how the change might work in other schools. But since it's the real world, lots of other factors—like changes in teachers or even the weather—could affect the results.

Field Experiments are widely used in economics, psychology, education, and public policy. For example, you might have heard of the famous "Broken Windows" experiment in the 1980s that looked at how small signs of disorder, like broken windows or graffiti, could encourage more serious crime in neighborhoods. This experiment had a big impact on how cities think about crime prevention.

From the foundational concepts of control groups and independent variables to the sophisticated layouts like Covariate Adaptive Randomization and Sequential Design, it's clear that the realm of experimental design is as varied as it is fascinating.

We've seen that each design has its own special talents, ideal for specific situations. Some designs, like the Classic Controlled Experiment, are like reliable old friends you can always count on.

Others, like Sequential Design, are flexible and adaptable, making quick changes based on what they learn. And let's not forget the adventurous Field Experiments, which take us out of the lab and into the real world to discover things we might not see otherwise.

Choosing the right experimental design is like picking the right tool for the job. The method you choose can make a big difference in how reliable your results are and how much people will trust what you've discovered. And as we've learned, there's a design to suit just about every question, every problem, and every curiosity.

So the next time you read about a new discovery in medicine, psychology, or any other field, you'll have a better understanding of the thought and planning that went into figuring things out. Experimental design is more than just a set of rules; it's a structured way to explore the unknown and answer questions that can change the world.

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• 11+ Psychology Experiment Ideas (Goals + Methods)
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Statistics By Jim

Making statistics intuitive

Experimental Design: Definition and Types

By Jim Frost 3 Comments

What is Experimental Design?

An experimental design is a detailed plan for collecting and using data to identify causal relationships. Through careful planning, the design of experiments allows your data collection efforts to have a reasonable chance of detecting effects and testing hypotheses that answer your research questions.

An experiment is a data collection procedure that occurs in controlled conditions to identify and understand causal relationships between variables. Researchers can use many potential designs. The ultimate choice depends on their research question, resources, goals, and constraints. In some fields of study, researchers refer to experimental design as the design of experiments (DOE). Both terms are synonymous.

Ultimately, the design of experiments helps ensure that your procedures and data will evaluate your research question effectively. Without an experimental design, you might waste your efforts in a process that, for many potential reasons, can’t answer your research question. In short, it helps you trust your results.

Learn more about Independent and Dependent Variables .

Design of Experiments: Goals & Settings

Experiments occur in many settings, ranging from psychology, social sciences, medicine, physics, engineering, and industrial and service sectors. Typically, experimental goals are to discover a previously unknown effect , confirm a known effect, or test a hypothesis.

Effects represent causal relationships between variables. For example, in a medical experiment, does the new medicine cause an improvement in health outcomes? If so, the medicine has a causal effect on the outcome.

An experimental design’s focus depends on the subject area and can include the following goals:

• Understanding the relationships between variables.
• Identifying the variables that have the largest impact on the outcomes.
• Finding the input variable settings that produce an optimal result.

For example, psychologists have conducted experiments to understand how conformity affects decision-making. Sociologists have performed experiments to determine whether ethnicity affects the public reaction to staged bike thefts. These experiments map out the causal relationships between variables, and their primary goal is to understand the role of various factors.

Conversely, in a manufacturing environment, the researchers might use an experimental design to find the factors that most effectively improve their product’s strength, identify the optimal manufacturing settings, and do all that while accounting for various constraints. In short, a manufacturer’s goal is often to use experiments to improve their products cost-effectively.

In a medical experiment, the goal might be to quantify the medicine’s effect and find the optimum dosage.

Developing an Experimental Design

Developing an experimental design involves planning that maximizes the potential to collect data that is both trustworthy and able to detect causal relationships. Specifically, these studies aim to see effects when they exist in the population the researchers are studying, preferentially favor causal effects, isolate each factor’s true effect from potential confounders, and produce conclusions that you can generalize to the real world.

To accomplish these goals, experimental designs carefully manage data validity and reliability , and internal and external experimental validity. When your experiment is valid and reliable, you can expect your procedures and data to produce trustworthy results.

An excellent experimental design involves the following:

• Lots of preplanning.
• Developing experimental treatments.
• Determining how to assign subjects to treatment groups.

The remainder of this article focuses on how experimental designs incorporate these essential items to accomplish their research goals.

Learn more about Data Reliability vs. Validity and Internal and External Experimental Validity .

Preplanning, Defining, and Operationalizing for Design of Experiments

This phase of the design of experiments helps you identify critical variables, know how to measure them while ensuring reliability and validity, and understand the relationships between them. The review can also help you find ways to reduce sources of variability, which increases your ability to detect treatment effects. Notably, the literature review allows you to learn how similar studies designed their experiments and the challenges they faced.

Operationalizing a study involves taking your research question, using the background information you gathered, and formulating an actionable plan.

This process should produce a specific and testable hypothesis using data that you can reasonably collect given the resources available to the experiment.

• Null hypothesis : The jumping exercise intervention does not affect bone density.
• Alternative hypothesis : The jumping exercise intervention affects bone density.

To learn more about this early phase, read Five Steps for Conducting Scientific Studies with Statistical Analyses .

Formulating Treatments in Experimental Designs

In an experimental design, treatments are variables that the researchers control. They are the primary independent variables of interest. Researchers administer the treatment to the subjects or items in the experiment and want to know whether it causes changes in the outcome.

As the name implies, a treatment can be medical in nature, such as a new medicine or vaccine. But it’s a general term that applies to other things such as training programs, manufacturing settings, teaching methods, and types of fertilizers. I helped run an experiment where the treatment was a jumping exercise intervention that we hoped would increase bone density. All these treatment examples are things that potentially influence a measurable outcome.

Even when you know your treatment generally, you must carefully consider the amount. How large of a dose? If you’re comparing three different temperatures in a manufacturing process, how far apart are they? For my bone mineral density study, we had to determine how frequently the exercise sessions would occur and how long each lasted.

How you define the treatments in the design of experiments can affect your findings and the generalizability of your results.

Assigning Subjects to Experimental Groups

A crucial decision for all experimental designs is determining how researchers assign subjects to the experimental conditions—the treatment and control groups. The control group is often, but not always, the lack of a treatment. It serves as a basis for comparison by showing outcomes for subjects who don’t receive a treatment. Learn more about Control Groups .

How your experimental design assigns subjects to the groups affects how confident you can be that the findings represent true causal effects rather than mere correlation caused by confounders. Indeed, the assignment method influences how you control for confounding variables. This is the difference between correlation and causation .

Imagine a study finds that vitamin consumption correlates with better health outcomes. As a researcher, you want to be able to say that vitamin consumption causes the improvements. However, with the wrong experimental design, you might only be able to say there is an association. A confounder, and not the vitamins, might actually cause the health benefits.

Let’s explore some of the ways to assign subjects in design of experiments.

Completely Randomized Designs

A completely randomized experimental design randomly assigns all subjects to the treatment and control groups. You simply take each participant and use a random process to determine their group assignment. You can flip coins, roll a die, or use a computer. Randomized experiments must be prospective studies because they need to be able to control group assignment.

Random assignment in the design of experiments helps ensure that the groups are roughly equivalent at the beginning of the study. This equivalence at the start increases your confidence that any differences you see at the end were caused by the treatments. The randomization tends to equalize confounders between the experimental groups and, thereby, cancels out their effects, leaving only the treatment effects.

For example, in a vitamin study, the researchers can randomly assign participants to either the control or vitamin group. Because the groups are approximately equal when the experiment starts, if the health outcomes are different at the end of the study, the researchers can be confident that the vitamins caused those improvements.

Statisticians consider randomized experimental designs to be the best for identifying causal relationships.

If you can’t randomly assign subjects but want to draw causal conclusions about an intervention, consider using a quasi-experimental design .

Learn more about Randomized Controlled Trials and Random Assignment in Experiments .

Randomized Block Designs

Nuisance factors are variables that can affect the outcome, but they are not the researcher’s primary interest. Unfortunately, they can hide or distort the treatment results. When experimenters know about specific nuisance factors, they can use a randomized block design to minimize their impact.

This experimental design takes subjects with a shared “nuisance” characteristic and groups them into blocks. The participants in each block are then randomly assigned to the experimental groups. This process allows the experiment to control for known nuisance factors.

Blocking in the design of experiments reduces the impact of nuisance factors on experimental error. The analysis assesses the effects of the treatment within each block, which removes the variability between blocks. The result is that blocked experimental designs can reduce the impact of nuisance variables, increasing the ability to detect treatment effects accurately.

Suppose you’re testing various teaching methods. Because grade level likely affects educational outcomes, you might use grade level as a blocking factor. To use a randomized block design for this scenario, divide the participants by grade level and then randomly assign the members of each grade level to the experimental groups.

A standard guideline for an experimental design is to “Block what you can, randomize what you cannot.” Use blocking for a few primary nuisance factors. Then use random assignment to distribute the unblocked nuisance factors equally between the experimental conditions.

You can also use covariates to control nuisance factors. Learn about Covariates: Definition and Uses .

Observational Studies

In some experimental designs, randomly assigning subjects to the experimental conditions is impossible or unethical. The researchers simply can’t assign participants to the experimental groups. However, they can observe them in their natural groupings, measure the essential variables, and look for correlations. These observational studies are also known as quasi-experimental designs. Retrospective studies must be observational in nature because they look back at past events.

Imagine you’re studying the effects of depression on an activity. Clearly, you can’t randomly assign participants to the depression and control groups. But you can observe participants with and without depression and see how their task performance differs.

Observational studies let you perform research when you can’t control the treatment. However, quasi-experimental designs increase the problem of confounding variables. For this design of experiments, correlation does not necessarily imply causation. While special procedures can help control confounders in an observational study, you’re ultimately less confident that the results represent causal findings.

Learn more about Observational Studies .

For a good comparison, learn about the differences and tradeoffs between Observational Studies and Randomized Experiments .

Between-Subjects vs. Within-Subjects Experimental Designs

When you think of the design of experiments, you probably picture a treatment and control group. Researchers assign participants to only one of these groups, so each group contains entirely different subjects than the other groups. Analysts compare the groups at the end of the experiment. Statisticians refer to this method as a between-subjects, or independent measures, experimental design.

In a between-subjects design , you can have more than one treatment group, but each subject is exposed to only one condition, the control group or one of the treatment groups.

A potential downside to this approach is that differences between groups at the beginning can affect the results at the end. As you’ve read earlier, random assignment can reduce those differences, but it is imperfect. There will always be some variability between the groups.

In a  within-subjects experimental design , also known as repeated measures, subjects experience all treatment conditions and are measured for each. Each subject acts as their own control, which reduces variability and increases the statistical power to detect effects.

In this experimental design, you minimize pre-existing differences between the experimental conditions because they all contain the same subjects. However, the order of treatments can affect the results. Beware of practice and fatigue effects. Learn more about Repeated Measures Designs .

Design of Experiments Examples

For example, a bone density study has three experimental groups—a control group, a stretching exercise group, and a jumping exercise group.

In a between-subjects experimental design, scientists randomly assign each participant to one of the three groups.

In a within-subjects design, all subjects experience the three conditions sequentially while the researchers measure bone density repeatedly. The procedure can switch the order of treatments for the participants to help reduce order effects.

Matched Pairs Experimental Design

A matched pairs experimental design is a between-subjects study that uses pairs of similar subjects. Researchers use this approach to reduce pre-existing differences between experimental groups. It’s yet another design of experiments method for reducing sources of variability.

Researchers identify variables likely to affect the outcome, such as demographics. When they pick a subject with a set of characteristics, they try to locate another participant with similar attributes to create a matched pair. Scientists randomly assign one member of a pair to the treatment group and the other to the control group.

On the plus side, this process creates two similar groups, and it doesn’t create treatment order effects. While matched pairs do not produce the perfectly matched groups of a within-subjects design (which uses the same subjects in all conditions), it aims to reduce variability between groups relative to a between-subjects study.

On the downside, finding matched pairs is very time-consuming. Additionally, if one member of a matched pair drops out, the other subject must leave the study too.

Learn more about Matched Pairs Design: Uses & Examples .

Another consideration is whether you’ll use a cross-sectional design (one point in time) or use a longitudinal study to track changes over time .

A case study is a research method that often serves as a precursor to a more rigorous experimental design by identifying research questions, variables, and hypotheses to test. Learn more about What is a Case Study? Definition & Examples .

In conclusion, the design of experiments is extremely sensitive to subject area concerns and the time and resources available to the researchers. Developing a suitable experimental design requires balancing a multitude of considerations. A successful design is necessary to obtain trustworthy answers to your research question and to have a reasonable chance of detecting treatment effects when they exist.

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March 23, 2024 at 2:35 pm

Dear Jim You wrote a superb document, I will use it in my Buistatistics course, along with your three books. Thank you very much! Miguel

March 23, 2024 at 5:43 pm

Thanks so much, Miguel! Glad this post was helpful and I trust the books will be as well.

April 10, 2023 at 4:36 am

What are the purpose and uses of experimental research design?

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The 3 Types Of Experimental Design

Dave Cornell (PhD)

Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

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Chris Drew (PhD)

This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

Experimental design refers to a research methodology that allows researchers to test a hypothesis regarding the effects of an independent variable on a dependent variable.

There are three types of experimental design: pre-experimental design, quasi-experimental design, and true experimental design.

Experimental Design in a Nutshell

A typical and simple experiment will look like the following:

• The experiment consists of two groups: treatment and control.
• Participants are randomly assigned to be in one of the groups (‘conditions’).
• The treatment group participants are administered the independent variable (e.g. given a medication).
• The control group is not given the treatment.
• The researchers then measure a dependent variable (e.g improvement in health between the groups).

If the independent variable affects the dependent variable, then there should be noticeable differences on the dependent variable between the treatment and control conditions.

The experiment is a type of research methodology that involves the manipulation of at least one independent variable and the measurement of at least one dependent variable.

If the independent variable affects the dependent variable, then the researchers can use the term “causality.”

Types of Experimental Design

1. pre-experimental design.

A researcher may use pre-experimental design if they want to test the effects of the independent variable on a single participant or a small group of participants.

The purpose is exploratory in nature , to see if the independent variable has any effect at all.

The pre-experiment is the simplest form of an experiment that does not contain a control condition.

However, because there is no control condition for comparison, the researcher cannot conclude that the independent variable causes change in the dependent variable.

Examples include:

• Action Research in the Classroom: Action research in education involves a teacher conducting small-scale research in their classroom designed to address problems they and their students currently face.
• Case Study Research : Case studies are small-scale, often in-depth, studies that are notusually generalizable.
• A Pilot Study: Pilot studies are small-scale studies that take place before the main experiment to test the feasibility of the project.
• Ethnography: An ethnographic research study will involve thick research of a small cohort to generate descriptive rather than predictive results.

2. Quasi-Experimental Design

The quasi-experiment is a methodology to test the effects of an independent variable on a dependent variable. However, the participants are not randomly assigned to treatment or control conditions. Instead, the participants already exist in representative sample groups or categories, such as male/female or high/low SES class.

Because the participants cannot be randomly assigned to male/female or high/low SES, there are limitations on the use of the term “causality.”

Researchers must refrain from inferring that the independent variable caused changes in the dependent variable because the participants existed in already formed categories before the study began.

• Homogenous Representative Sampling: When the research participant group is homogenous (i.e. not diverse) then the generalizability of the study is diminished.
• Non-Probability Sampling: When researchers select participants through subjective means such as non-probability sampling, they are engaging in quasi-experimental design and cannot assign causality.

See more Examples of Quasi-Experimental Design

3. True Experimental Design

A true experiment involves a design in which participants are randomly assigned to conditions, there exists at least two conditions (treatment and control) and the researcher manipulates the level of the independent variable (independent variable).

When these three criteria are met, then the observed changes in the dependent variable (dependent variable) are most likely caused by the different levels of the independent variable.

The true experiment is the only research design that allows the inference of causality .

Of course, no study is perfect, so researchers must also take into account any threats to internal validity that may exist such as confounding variables or experimenter bias.

• Heterogenous Sample Groups: True experiments often contain heterogenous groups that represent a wide population.
• Clinical Trials: Clinical trials such as those required for approval of new medications are required to be true experiments that can assign causality.

See More Examples of Experimental Design

Experimental Design vs Observational Design

Experimental design is often contrasted to observational design. Defined succinctly, an experimental design is a method in which the researcher manipulates one or more variables to determine their effects on another variable, while observational design involves the observation and analysis of a subject without influencing their behavior or conditions.

Observational design primarily involves data collection without direct involvement from the researcher. Here, the variables aren’t manipulated as they would be in an experimental design.

An example of an observational study might be research examining the correlation between exercise frequency and academic performance using data from students’ gym and classroom records.

The key difference between these two designs is the degree of control exerted in the experiment . In experimental studies, the investigator controls conditions and their manipulation, while observational studies only allow the observation of conditions as independently determined (Althubaiti, 2016).

Observational designs cannot infer causality as well as experimental designs; but they are highly effective at generating descriptive statistics.

For more, read: Observational vs Experimental Studies

Generally speaking, there are three broad categories of experiments. Each one serves a specific purpose and has associated limitations . The pre-experiment is an exploratory study to gather preliminary data on the effectiveness of a treatment and determine if a larger study is warranted.

The quasi-experiment is used when studying preexisting groups, such as people living in various cities or falling into various demographic categories. Although very informative, the results are limited by the presence of possible extraneous variables that cannot be controlled.

The true experiment is the most scientifically rigorous type of study. The researcher can manipulate the level of the independent variable and observe changes, if any, on the dependent variable. The key to the experiment is randomly assigning participants to conditions. Random assignment eliminates a lot of confounds and extraneous variables, and allows the researchers to use the term “causality.”

For More, See: Examples of Random Assignment

Baumrind, D. (1991). Parenting styles and adolescent development. In R. M. Lerner, A. C. Peterson, & J. Brooks-Gunn (Eds.), Encyclopedia of Adolescence (pp. 746–758). New York: Garland Publishing, Inc.

Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design & analysis issues in field settings . Boston, MA: Houghton Mifflin.

Loftus, E. F., & Palmer, J. C. (1974). Reconstruction of automobile destruction: An example of the interaction between language and memory. Journal of Verbal Learning and Verbal Behavior, 13 (5), 585–589.

Matthew L. Maciejewski (2020) Quasi-experimental design. Biostatistics & Epidemiology, 4 (1), 38-47. https://doi.org/10.1080/24709360.2018.1477468

Thyer, Bruce. (2012). Quasi-Experimental Research Designs . Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195387384.001.0001

• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 23 Achieved Status Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 25 Defense Mechanisms Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 15 Theory of Planned Behavior Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 18 Adaptive Behavior Examples

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 23 Achieved Status Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 15 Ableism Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 25 Defense Mechanisms Examples
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Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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• A Quick Guide to Experimental Design | 5 Steps & Examples

A Quick Guide to Experimental Design | 5 Steps & Examples

Published on 11 April 2022 by Rebecca Bevans . Revised on 5 December 2022.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design means creating a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying. 

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If if random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, frequently asked questions about experimental design.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalised and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomised design vs a randomised block design .
• A between-subjects design vs a within-subjects design .

Randomisation

An experiment can be completely randomised or randomised within blocks (aka strata):

• In a completely randomised design , every subject is assigned to a treatment group at random.
• In a randomised block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomisation isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomising or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimise bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalised to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group.

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Rebecca Bevans

Experimental design: Guide, steps, examples

Last updated

27 April 2023

Reviewed by

Miroslav Damyanov

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Experimental research design is a scientific framework that allows you to manipulate one or more variables while controlling the test environment. 

When testing a theory or new product, it can be helpful to have a certain level of control and manipulate variables to discover different outcomes. You can use these experiments to determine cause and effect or study variable associations. 

This guide explores the types of experimental design, the steps in designing an experiment, and the advantages and limitations of experimental design. 

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• What is experimental research design?

You can determine the relationship between each of the variables by: 

Manipulating one or more independent variables (i.e., stimuli or treatments)

Applying the changes to one or more dependent variables (i.e., test groups or outcomes)

With the ability to analyze the relationship between variables and using measurable data, you can increase the accuracy of the result. 

What is a good experimental design?

A good experimental design requires: 

Significant planning to ensure control over the testing environment

Sound experimental treatments

Properly assigning subjects to treatment groups

Without proper planning, unexpected external variables can alter an experiment's outcome. 

To meet your research goals, your experimental design should include these characteristics:

Provide unbiased estimates of inputs and associated uncertainties

Enable the researcher to detect differences caused by independent variables

Include a plan for analysis and reporting of the results

Provide easily interpretable results with specific conclusions

What's the difference between experimental and quasi-experimental design?

The major difference between experimental and quasi-experimental design is the random assignment of subjects to groups. 

A true experiment relies on certain controls. Typically, the researcher designs the treatment and randomly assigns subjects to control and treatment groups. 

However, these conditions are unethical or impossible to achieve in some situations.

When it's unethical or impractical to assign participants randomly, that’s when a quasi-experimental design comes in. 

This design allows researchers to conduct a similar experiment by assigning subjects to groups based on non-random criteria. 

Another type of quasi-experimental design might occur when the researcher doesn't have control over the treatment but studies pre-existing groups after they receive different treatments.

When can a researcher conduct experimental research?

Various settings and professions can use experimental research to gather information and observe behavior in controlled settings. 

Basically, a researcher can conduct experimental research any time they want to test a theory with variable and dependent controls. 

Experimental research is an option when the project includes an independent variable and a desire to understand the relationship between cause and effect. 

• The importance of experimental research design

Experimental research enables researchers to conduct studies that provide specific, definitive answers to questions and hypotheses. 

Researchers can test Independent variables in controlled settings to:

Test the effectiveness of a new medication

Design better products for consumers

Answer questions about human health and behavior

Developing a quality research plan means a researcher can accurately answer vital research questions with minimal error. As a result, definitive conclusions can influence the future of the independent variable. 

Types of experimental research designs

There are three main types of experimental research design. The research type you use will depend on the criteria of your experiment, your research budget, and environmental limitations. 

Pre-experimental research design

A pre-experimental research study is a basic observational study that monitors independent variables’ effects. 

During research, you observe one or more groups after applying a treatment to test whether the treatment causes any change. 

The three subtypes of pre-experimental research design are:

One-shot case study research design

This research method introduces a single test group to a single stimulus to study the results at the end of the application. 

After researchers presume the stimulus or treatment has caused changes, they gather results to determine how it affects the test subjects. 

One-group pretest-posttest design

This method uses a single test group but includes a pretest study as a benchmark. The researcher applies a test before and after the group’s exposure to a specific stimulus. 

Static group comparison design

This method includes two or more groups, enabling the researcher to use one group as a control. They apply a stimulus to one group and leave the other group static. 

A posttest study compares the results among groups. 

True experimental research design

A true experiment is the most common research method. It involves statistical analysis to prove or disprove a specific hypothesis . 

Under completely experimental conditions, researchers expose participants in two or more randomized groups to different stimuli. 

Random selection removes any potential for bias, providing more reliable results. 

These are the three main sub-groups of true experimental research design:

Posttest-only control group design

This structure requires the researcher to divide participants into two random groups. One group receives no stimuli and acts as a control while the other group experiences stimuli.

Researchers perform a test at the end of the experiment to observe the stimuli exposure results.

Pretest-posttest control group design

This test also requires two groups. It includes a pretest as a benchmark before introducing the stimulus. 

The pretest introduces multiple ways to test subjects. For instance, if the control group also experiences a change, it reveals that taking the test twice changes the results.

Solomon four-group design

This structure divides subjects into two groups, with two as control groups. Researchers assign the first control group a posttest only and the second control group a pretest and a posttest. 

The two variable groups mirror the control groups, but researchers expose them to stimuli. The ability to differentiate between groups in multiple ways provides researchers with more testing approaches for data-based conclusions. 

Quasi-experimental research design

Although closely related to a true experiment, quasi-experimental research design differs in approach and scope. 

Quasi-experimental research design doesn’t have randomly selected participants. Researchers typically divide the groups in this research by pre-existing differences. 

Quasi-experimental research is more common in educational studies, nursing, or other research projects where it's not ethical or practical to use randomized subject groups.

• 5 steps for designing an experiment

Experimental research requires a clearly defined plan to outline the research parameters and expected goals. 

Here are five key steps in designing a successful experiment:

Step 1: Define variables and their relationship

Your experiment should begin with a question: What are you hoping to learn through your experiment? 

The relationship between variables in your study will determine your answer.

Define the independent variable (the intended stimuli) and the dependent variable (the expected effect of the stimuli). After identifying these groups, consider how you might control them in your experiment. 

Could natural variations affect your research? If so, your experiment should include a pretest and posttest. 

Step 2: Develop a specific, testable hypothesis

With a firm understanding of the system you intend to study, you can write a specific, testable hypothesis. 

What is the expected outcome of your study? 

Develop a prediction about how the independent variable will affect the dependent variable. 

How will the stimuli in your experiment affect your test subjects? 

Your hypothesis should provide a prediction of the answer to your research question . 

Step 3: Design experimental treatments to manipulate your independent variable

Depending on your experiment, your variable may be a fixed stimulus (like a medical treatment) or a variable stimulus (like a period during which an activity occurs). 

Determine which type of stimulus meets your experiment’s needs and how widely or finely to vary your stimuli. 

Step 4: Assign subjects to groups

When you have a clear idea of how to carry out your experiment, you can determine how to assemble test groups for an accurate study. 

When choosing your study groups, consider: 

The size of your experiment

Whether you can select groups randomly

Your target audience for the outcome of the study

You should be able to create groups with an equal number of subjects and include subjects that match your target audience. Remember, you should assign one group as a control and use one or more groups to study the effects of variables. 

Step 5: Plan how to measure your dependent variable

This step determines how you'll collect data to determine the study's outcome. You should seek reliable and valid measurements that minimize research bias or error. 

You can measure some data with scientific tools, while you’ll need to operationalize other forms to turn them into measurable observations.

• Advantages of experimental research

Experimental research is an integral part of our world. It allows researchers to conduct experiments that answer specific questions. 

While researchers use many methods to conduct different experiments, experimental research offers these distinct benefits:

Researchers can determine cause and effect by manipulating variables.

It gives researchers a high level of control.

Researchers can test multiple variables within a single experiment.

All industries and fields of knowledge can use it. 

Researchers can duplicate results to promote the validity of the study .

Replicating natural settings rapidly means immediate research.

Researchers can combine it with other research methods.

It provides specific conclusions about the validity of a product, theory, or idea.

• Disadvantages (or limitations) of experimental research

Unfortunately, no research type yields ideal conditions or perfect results. 

While experimental research might be the right choice for some studies, certain conditions could render experiments useless or even dangerous. 

Before conducting experimental research, consider these disadvantages and limitations:

Required professional qualification

Only competent professionals with an academic degree and specific training are qualified to conduct rigorous experimental research. This ensures results are unbiased and valid. 

Limited scope

Experimental research may not capture the complexity of some phenomena, such as social interactions or cultural norms. These are difficult to control in a laboratory setting.

Resource-intensive

Experimental research can be expensive, time-consuming, and require significant resources, such as specialized equipment or trained personnel.

Limited generalizability

The controlled nature means the research findings may not fully apply to real-world situations or people outside the experimental setting.

Practical or ethical concerns

Some experiments may involve manipulating variables that could harm participants or violate ethical guidelines . 

Researchers must ensure their experiments do not cause harm or discomfort to participants. 

Sometimes, recruiting a sample of people to randomly assign may be difficult. 

• Experimental research design example

Experiments across all industries and research realms provide scientists, developers, and other researchers with definitive answers. These experiments can solve problems, create inventions, and heal illnesses. 

Product design testing is an excellent example of experimental research. 

A company in the product development phase creates multiple prototypes for testing. With a randomized selection, researchers introduce each test group to a different prototype. 

When groups experience different product designs , the company can assess which option most appeals to potential customers. 

Experimental research design provides researchers with a controlled environment to conduct experiments that evaluate cause and effect. 

Using the five steps to develop a research plan ensures you anticipate and eliminate external variables while answering life’s crucial questions.

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• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Experimental Design: The Complete Pocket Guide

Bryn Farnsworth

Our comprehensive manual on experimental design provides guidance on avoiding common mistakes and pitfalls when establishing the optimal experiment for your research.

Table of Contents

• Introduction to experimental methods

Humans are a quite curious species. We explore new grounds, improve products and services, find faster and safer ways to produce or transport goods, and we solve the mysteries of global diseases. All of these activities are guided by asking the right questions, by searching for answers in the right spots and taking appropriate decisions. Academic and commercial research have professionalized this quest for knowledge and insights into ourselves and the world surrounding us.

Every day, research institutions across the globe investigate the inner workings of our universe – from cellular levels of our synapses and neurons to macroscopic levels of planets and solar systems – by means of experimentation. Simply put: Experiments are the professional way to answer questions, identify cause and effect or determine predictors and outcomes. These insights help us understand how and why things are what they are and can ultimately be used to change the world by improving the good and overcoming the bad.

N.B. this post is an excerpt from our Experimental Design Guide. You can download your free copy below and get even more insights into the world of Experimental Design.

Free 44-page Experimental Design Guide

For Beginners and Intermediates

• Respondent management with groups and populations
• How to set up stimulus selection and arrangement

In contrast to the early years of scientific research, modern-age experiments are not merely results of scientists randomly probing assumptions combined with the pure luck to be at the right place at the right time and observe outcomes.

Today’s scientific insights are the result of careful thinking and experimental planning, proper collecting of data, and drawing of appropriate conclusions.

Experimental Design Example

Researchers use experiments to learn something new about the world, to answer questions or to probe theoretical assumptions.

Typical examples for research questions in human cognitive-behavioral research are:

• How does sensory stimulation affect human attention? How do, for example, moving dot patterns, sounds or electrical stimulation alter our perception of the world?

• What are the changes in human physiology during information uptake? How do heart rate and galvanic skin response, for example, change as we recall correct or incorrect information?

• How does virtual reality compared to real physical environments affect human behavior? Do humans learn faster in the real world compared to VR?

• How does stress affect the interaction with other colleagues or machines in the workplace?

• How does packaging of a product affect shoppers’ frustration levels? Is the new package intuitive to open, and if not, how does it affect the behavior of the person?

• How does the new TV commercial impact on emotional expressions and brand memory? Does gender have an influence on purchase decisions after watching the ad?

• How does a website affect users’ stress levels in terms of galvanic skin response, ECG and facial expressions?

• Which intersections in town cause most frustration in bicyclists?

• What are the aspects in a presidential campaign speech that drive voters’ decisions?

As you can see, research questions can be somewhat generic. Experiments are supposed to clarify these questions in a more standardized framework. In order to do so, several steps are necessary to fine-tune the research question into a more testable form:

Step 1: Phrase a hypothesis

First, the general research question is broken down into a testable hypothesis or several hypotheses. Hypotheses are explicit statements about cause and effect and address what outcomes occur when specific factors are manipulated:

Hypotheses phrase a relationship between one or more independent variables and one or more dependent variables:

•Independent variable

The independent variable (IV) is strategically changed, or manipulated, by the experimenter. IVs are also referred to as factors.

• Dependent variable (DV)

The dependent variable (DV) is measured by the experimenter. Experiments with one DV are called univariate, experiments with two or more DV are called multivariate.

The general research question “How does stress affect the interaction with others? ” might lead to the following hypotheses about how stress (independent variable) affects interaction with others (dependent variable):

1) “Having to reply to 100 or more incoming emails per hour results in reduced verbal interaction with colleagues.”

Independent variable: Number of emails per hour Dependent variable: Number of verbal interactions with colleagues per hour

2) “Sleeping 8 hours or more per night results in increased informal sport activities with colleagues.”

Independent variable : Duration of sleep per night Dependent variable : Number of sport meetups with colleagues per week

3) “Regular physical exercise in the evening results in increased occurrences of smiles when talking to others in business meetings.”

Independent variable : Number of evening sport activities per week Dependent variable : Smile occurrences when talking with others

Hypotheses make the research question more explicit by stating an observable relationship between cause and effect. Hypotheses also determine which stimuli are used and what respondents are exposed to.

A stimulus doesn’t have to be just pictures or tones, much more constitutes a stimulus, for example, questionnaires, websites, videos, speech and conversations with others, visual and proprioceptive input while driving and much more. We will address stimuli in more detail below.

Step 2: Sample Groups

Define sample groups.

After specifying the hypothesis, you need to clarify the respondent group characteristics for your experiment. This step is necessary to exclude side effects that could alter the outcomes of your experimental data collection. Make sure that demographic characteristics such as age, gender, education level, income, marital status, occupation etc. are consistent across the respondent pool. Individual characteristics such as state of health or exposure to certain life events should be considered as they might affect experimental outcomes. For example, mothers might respond differently to a TV ad for baby toys than women without kids. Soldiers suffering from PTSD might respond differently to stress-provoking stimuli than software developers.

Step 3: Assign subjects to groups

In this step, you randomly distribute subjects to the different experimental conditions. For example, for your stress in the workplace study you could create two experimental groups, where group one receives 10 emails per hour, and group two receives 100 emails per hour. You could now analyze how the two groups differ in their social interaction with others within the next 6 hours. Ideally, the assignment to experimental groups is done in a randomized fashion, such that all respondents have the same probability for ending up in the available experimental groups. There should not be any bias to assign specific respondents to one group or the other.

Step 4: Determine sampling frequency.

How often do you want to measure from respondents? Clinical trials typically measure patients’ state of health once per month over the course of several months or years. In usability studies you might ask respondents once at the end of the session several questions, either verbally or via surveys and questionnaires.

However, when you collect cognitive-behavioral data from EEG, EMG, ECG, GSR or other biosensors while respondents are doing a specific task, you are collecting tens to hundreds of data points per second – even though all of these sub-second samples might be used to compute an overall score reflecting a certain cognitive or affective state. We will address later in this guide which sensors are ideal to collect specific cognitive-behavioral metrics.

Step 5: Conduct the experiment and collect data.

In this step, you execute the experimental paradigm according to the selected methods. Make sure to observe, monitor, and report any important moments during data collection. Prior to conducting the experiment, run a pilot test to rule out any issues that might arise during data collection (stimulus was wrong length/non-randomized/not optimal, etc.)

Check out : 7 Tips & Tricks For a Smooth Lab Experience

Step 6: Pre-process data and analyze metrics.

In human cognitive-behavioral research, raw data can consist of self-reports or data from biosensors. Of course, video footage of experimental sessions such as focus groups and interviews also constitute raw data and have to be analyzed using coding schemes. Due to the wide range of statistical methods to analyze raw data and metrics, we will not address this step in the current guide. However, one crucial aspect should be mentioned here: The selection of a specific statistical method for data analysis should always be driven by the original hypothesis and the collected data.

Of course, not all experiments require the precise specification of all of these steps. Sometimes you as a researcher don’t have control of certain factors, or you are lacking access to specific respondent populations. Dependent on the amount of control that you have over the relationship between cause and effect, the following types of experiments can be distinguished:

Types of Experimental design

1. laboratory experiments.

Whenever we speak informally of experiments, lab experiments might come to mind where researchers in white lab coats observe others from behind one-side mirrors, taking minute notes on the performance and behavior of human participants executing key-press tasks in front of somewhat unpredictable machines. In fact, this is how human cognitive-behavioral research started (see the Milgram experiment ).

Gladfully, the days of sterile lab environments are long gone, and you can run your study wearing your favorite sweater. However, a core aspect still holds: Being able to control all factors and conditions that could have an effect. For example, in lab experiments you can select specific respondent groups and assign them to different experimental conditions, determine the precise timing and configuration of all stimuli, and exclude any problematic side-effects.

What you should know about laboratory experiments…

• Precise control of all external and internal factors that could affect experimental outcomes.
• Random assignment of respondents to experimental groups, ideally by means of randomization.
• Allows identification of cause-effect relationships with highest accuracy.
• Since everything is standardized, others can replicate your study, which makes your study more “credible” compared to non-standardized scenarios.

Limitations.

• Controlled experiments do not reflect the real world. Respondents might not respond naturally because the lab doesn’t reflect the natural environment. In technical terms, lab experiments are lacking ecological validity.
• Observer effects might change respondents’ behavior. An experimenter sitting right next to a respondent or observing them via webcam might bias experimental outcomes (read up on the Hawthorne Effect ).

2. Field experiments

In contrast to lab experiments, field experiments are done in the natural surroundings of respondents. While the experimenter manipulates the “cause”-aspect, there’s no control of what else could potentially affect the effects and outcomes (such as the Hofling’s Hospital Experiment based on Milgram‘s work).

Quite often, engineers also conduct field tests of prototypes of soft- and hardware to validate earlier lab tests and to obtain broader feedback from respondents in real life.

What you should know about field experiments…

>>  strengths..

• Field experiments reflect real-life scenarios more than lab experiments. They have higher ecological validity
• When experiments are covert and respondents don’t feel observed, the observed behavior is much closer to real life compared to lab settings.

>> Limitations.

• No control over external factors that could potentially affect outcomes. The outcomes are therefore much more varied. More respondents are therefore needed to compensate the variation.
• Difficult to replicate by others.
• Limited ability to obtain informed consent from respondents.

3. Natural experiments.

Natural experiments are pure observation studies in the sense that the experimenter doesn’t have any control. Respondent groups are observed as-is and not strategically assigned to different experimental conditions.

You might want to compare existing iPhone and Android users, people living close to Chernobyl and people living somewhere else, or patients suffering from cancer and healthy populations. In this case, the groups that you’d like to compare already exist by nature – you don’t have to create them.

What you should know about natural experiments…

• Behavior in natural experiments more likely reflects real life.
• Ideal in situations where it would be ethically unacceptable to manipulate the group assignment (e.g., expose respondents to radiation).
• More expensive and time-consuming than lab experiments.
• No control over any factors implies that replication by others is almost impossible.

How can I measure human behavior?

Laboratory, field and natural experiments all have one aspect in common: Insights are accomplished empirically. “Empirical” means that research questions and hypotheses are not answered by mere reflection or thought experiments.

Instead of leaning back in a chair and pondering over the potential outcomes of a thought experiment, researchers in human cognitive-behavioral science accomplish their work by means of active observation and probing of the environment in order to identify the underlying processes as well as the ultimate “driving forces” of human behavior.

Within the last decades, researchers have developed intricate experimental techniques and procedures that have found their way also into commercial testing of emotional, cognitive and attentional effects of new products and services, or how personality traits and problem-solving strategies have an impact on brand likeability and consumer preferences.

Two ways to study Human Behavior

Qualitative studies on human behavior.

Qualitative studies gather observational insights. Examples include the investigation of diary entries, open questionnaires, unstructured interviews or observations. Because nothing is counted or quantified and every observation is described as-is, qualitative data is also referred to as descriptive.

In qualitative field studies or usability studies, for example, researchers directly observe how respondents are using the technology, allowing them to directly ask questions, probe on behavior or potentially even adjust the experimental protocol to incorporate the individual’s behavior. The focus of qualitative studies is primarily on understanding how respondents see the world and why they react in a specific way.

What you should know about qualitative studies…

•  Ideal to answer “why” and “how to fix a problem?” questions.
• Focus on individual experience of the respondent.
• Small respondent samples required.
• Knowledge gained in the specific study might not be transferrable to other groups.
• Data collection might take longer per respondent.
• Risk that results are affected by researcher’s biases and preferences.

Typical use cases.

•  UX, web and software usability tests (description of user journeys).
• Open-ended interviews and surveys on biographical events.
• Focus groups with / without experimenter present.

Check out: How to Deliver better UX with Emotion Detection 

Quantitative studies

Quantitative studies by contrast, quantitative studies characterize the systematic empirical investigation of observable phenomena via statistical, mathematical or computational techniques. In other words, quantitative studies use numbers to describe and characterize human behavior.

Examples for quantitative techniques include structured surveys and interviews, observations with dedicated coding schemes (e.g., counting the number of cigarettes smoked within a day), or physiological measurements from EEG, EMG, ECG, GSR and other sensors producing numerical output. Whenever researchers are using quantitative methods, they translate behavioral observations into countable numbers and statistical outputs. All of this is done to guarantee maximum experimental control.

What you should know about quantitative studies…

• Ideal for answering “how many” and “how much” questions.
• Useful to analyze large respondent groups, focus on entire populations.
• High amount of standardization requires less time than qualitative studies.
• Provides numerical values that can be analyzed statistically.
• Experimenter might miss out phenomena because the measurement tool is too narrow.
• Contextual factors are often ignored or missing.
• Studies are expensive and time-consuming.
• Behavioral observation using coding schemes (e.g., on facial expressions or action occurrences within a certain time frame)
• Structured interviews and surveys containing single- or multiple-choice questions as well as scales.
• Physiological measurements of bodily processes (EEG, EMG, GSR etc.)

Psychology Research with iMotions

The world’s leading human behavior software

Check out: Qualitative vs Quantitative Research 

Which numbers could human cognitive-behavioral research potentially use to describe our complex inner workings, our intelligence, personality traits or skill levels? What are measurable indicators of a person being a shopaholic, for example?

Indicators that can be counted might be the average time spent in department stores during a week, the cumulative amount of money laid out for certain lifestyle products, or the number of shoe boxes filling up the closet under the stairs (have a look at our reading tip on measurement and the assignment of numbers or events).

The basic principle is that hidden factors of our personality can be made visible (and therefore measurable) by breaking them into feasible and tangible, graspable and observable units which can be counted numerically. This “making visible” of latent constructs of our personality and identity is referred to as operationalization.

While some measures are more suitable to capture an underlying latent characteristic, others might fail. So the question is, what actually constitutes an appropriate measure?

Measurements to avoid bias

This is generally described with respect to the following criteria:

Objectivity

Objectivity is the most general requirement and reflects the fact that measures should come to the same result no matter who is using them. Also, they should generate the same outcomes independent of the outside influences. For example, a multiple-choice personality questionnaire or survey is objective if it returns the same score irrelevant of whether the participant responds verbally or in written form. Further, the result should be independent of the knowledge or attitude of the experimenter, so that the results are purely driven by the performance of the respondent.

Reliability

A measure is said to have high reliability if it returns the same value under consistent conditions. There are several sub-categories of reliability. For example, “retest reliability” describes the stability of a measure over time, “inter-rater reliability” reflects the amount to which different experimenters give consistent estimates of the same behavior, while “split-half reliability” breaks a test into two and examines to what extent the two halves generate identical results.

This is the final and most crucial criterion. It reflects the extent to which a measure collects what it is supposed to collect. Imagine an experiment where body size is collected to measure its relationship with happiness. Obviously, the measure is both objective and reliable (body size measures are quite consistent irrespective of the person taking the measurement) but it is truly a poor measure with respect to its construct validity (i.e., its capability to truly capture the underlying variable) for happiness.

Once you have identified measures that fulfill objectivity, reliability and validity criteria at the same time, you are on the right track to generate experimental outcomes that will push beyond the frontiers of our existing knowledge.

Respondent Management

While Iceland has research programs where experiments are applied to the entire nation, other countries and situations do not allow testing everybody. Of course, it would grant maximum insights into your research question, but due to time and resource constraints studies and experiments are generally carried out on respondent groups rather than entire populations.

The most challenging part is to find respondents that truly represent the larger target population allowing you to generalize, or infer, from your study group findings to the population. You might have heard the phrase “representative sample” before. This describes respondent groups where each and every member of the population has an equal chance of being selected for your experiment. Populations don’t necessarily have to be entire countries – the term simply reflects “all people that share certain characteristics” (height, weight, BMI, hemoglobin levels, experience, income, nationality etc.) which are considered relevant for your experiment.

Exemplary populations are:

• Female academics between 30 and 40 years in the US with an average annual income of $50k
• Software developers with more than 5 years of experience in C#
• Patients suffering from secondary progressive Multiple Sclerosis
• After-work shoppers of any age and gender
• Danish mothers up to 50 years
• People wearing glasses

A sample now can be a group of 100 Multiple Sclerosis patients, or 20 dog owners. Finding “representative samples” is not that easy as there is some bias in almost all studies. Samples can be found as following:

Non-random respondent sampling

Non-random sampling can be done during initial pre-screening phases, where generalization is not important. In that case, the experimental outcomes only apply to the tested respondent group. Sampling is done as following:

• Volunteers . You ask people on the street, and whoever agrees to participate is tested.
• Snowball sample . One case identifies others of his kind (e.g., HSE shoppers).
• Convenience sample . You test your co-workers and colleagues or other readily available groups.
• Quota sample . At-will selection of a fixed number from several groups (e.g., 30 male and 30 female respondents).

Random respondent sampling .

Random sampling is actually giving everyone in the population the same chance of being included in your experiment. The benefit of being able to conclude from your research findings obtained from several respondents to the general public comes, however, with high demands on time and resources. The following random sampling strategies exist:

Simple random sampling

In random samples chances for everyone are identical to being included in your test. This means that you had to identify, for example, every female academic between 30 and 40 years in the US with an average annual income of $50k, or every dog owner. Subsequently, you draw random samples and only contact those. Random sampling disallows any selection bias based on volunteering or cooperation.

Systematic sampling

Instead of a completely random selection, you systematically select every nth person from an existing list, for example ordered by respondent age, disease duration, membership, distance etc.

Multistage sampling

Sampling can be done in multiple steps. For example, to find representative students for testing, you can first draw a random selection of counties, then proceed with random drawing of cities, schools, and classes. Finally, you randomly draw students for observation and recording.

Cluster sampling

Particularly for self-reports, studies are carried out on large and geographically dispersed populations. In order to obtain the required number of respondents for testing, clusters may be identified and randomly drawn. Subsuequently, all members of the drawn samples are tested. For example, clustering might be done using households – in this case, all household members are tested, reducing the time and resources for testing massively.

Which sampling method you use is generally determined by feasibility in terms of time and resources. It might often be difficult to obtain truly random samples, particularly in field research. You can find more details on suggested procedures for representative sampling in Banerjee and colleagues (2007; 2010).

How many respondents do I need?

Sampling strategies are closely linked to the sample size of your experiment. If you would like to do a single case study, of course only one respondent is needed. In this case, however, you cannot generalize any findings to the larger population. On the other hand, sampling from the entire population is not possible. The question is, how many respondents are suitable for your experiment? What is the ideal sample size?

Martinez and colleagues (2014) as well as Niles (2011) provide recommendations. Without delving too deep into statistics, the main message is about this: Always collect as many respondents as necessary. For quantitative usability testing 20 respondents might be sufficient, but more respondents should be tested whenever the expected effects are smaller, for example, if there’s only subtle differences between the different stimulus conditions.

This is why academic researchers run studies with dozens to hundreds or thousands of respondents. With more respondents, you reduce the ambiguity of individual variation that could have affected experimental outcomes.Top of Page

The amount of security about your findings is typically expressed with respect to confidence, which is roughly expressed with the following formula:

N is the sample size. As you can see, higher respondent samples cause confidence to become smaller (which is the desired outcome). In other words, testing more people gives you more accurate results.

For example, if you tested the preference for a new product with 10 out of 10,000 respondents, then the confidence is at 32%. If 7 out of 10 respondents (70%) liked the new product, the actual proportion in the population could be as low as 48% (70-32) and as high as 100% (70+32, you can’t be above 100). With a variation from 48% to 100%, your test might not be that helpful.

If you increase the sample size to 100 respondents out of 10,000, the confidence is at 10%. With 70 out of 100 respondents liking the product, the actual value in the population is somewhere between 60% and 80%. You’re getting much closer!

If you would like to further reduce the confidence to 5%, you have to test at least 500 randomly-selected respondents. The bottom line is, you have to test lots of respondents before being able to get to conclusions. For more information visit the Creative Research Systems website , where you can find a more exact formula as well as a sample size calculator tool.

Cross-sectional vs. longitudinal designs

Experimental design and the way your study is carried out depends on the nature of your research question. If you’re interested in how a new TV advertisement is perceived by the general public in terms of attention, cognition and affect, there’s several ways to design your study. Do you want to compare cognitive-behavioral outcomes of the ad among different populations of low and high-income households at the same point in time? Or, do you want to measure the TV ad effects in a single population (say, male high-income shoppers with specific demographic characteristics) over an extended period of time? The former approach is generally referred to as cross-sectional design. The latter is called longitudinal design. The two can further be combined (mixed design)

Cross-sectional design

In cross-sectional studies two or more groups are compared at a single point in time. Similar to taking a snapshot, every respondent is invited and tested just once. In our example, you would show the new TV ad to respondents from low- and high-income households. You would not, however, invite them and show them the TV ad again a week later.

Other examples of cross-sectional studies are:

• Gaming. Compare effects of video games on emotional responsiveness of healthy children and children suffering from ADHS.
• Web testing. Compare website usability evaluation of young, middle-aged and senior shoppers.
• Psychology. Compare evaluation of parenting style of mothers and fathers.

The primary benefit of a cross-sectional experimental design is that it allows you to compare many different variables at the same time. You could, for example, investigate the impact of age, gender, experience or educational levels on respondents’ cognitive-emotional evaluation of the TV ad with little or no additional cost. The only thing you have to do is collect the data (for example, by means of interviews or surveys).

Longitudinal design

In a longitudinal study you conduct several observations of the same respondent group over time, lasting from hours to days, months and many years. By doing this, you establish a sequence of events and minimize the noise that could potentially affect each of the single measurements. In other words, you simply make the outcomes more robust against potential side effects.

For example, you could show a TV ad several times to your group of interest (male high-income shoppers) and see how their preference for the ad changes over time.

Other examples for longitudinal designs are:

• Media / package testing. Two or more media trailers or packages are shown in sequence to a group of respondents who evaluate how much they like each of the presented items.
• Food and flavor testing. Respondents are exposed to two or more flavors presented in sequence and asked for their feedback.
• UI and UX testing. Respondents navigate two or more websites and are interviewed with respect to usability questions.
• Psychology and Training. A group of respondents attending a professional training session answers a questionnaire on emotional well-being before, during and after training.
• Physiology. You monitor EEG, GSR, EMG, facial expressions, etc. while respondents are exposed to pictures, sounds or video stimuli.

The primary benefit of longitudinal designs is that you obtain a time-course of values within one group of respondents. Even if you only obtain cognitive-affective test scores before and after the experimental intervention, you are more likely to understand the impact of the intervention on already existing levels of attention, cognition or affect. Therefore, longitudinal studies are more likely to suggest cause-and-effect relationships than cross-sectional studies.

Mixed design

Mixed designs combine the best of two worlds as they allow you to collect longitudinal data across several groups. Strictly spoken, whenever you collect physiological data (like EEG, GSR, EMG, ECG, facial expressions, etc.) from several respondent groups in order to compare different populations, you have a mixed study design. The data itself is longitudinal (several samples over time), while the group comparison has cross-sectional aspects.

Typical examples for mixed designs are:

• Product / media testing. Two or more versions of a product or service are compared with respect to cognitive-behavioral outcomes of two or more groups (e.g., novices and experts, male and female, young and old).
• A-B testing. Two versions of a website or app are compared with respect to cognitive-behavioral outcomes of two or more groups.

Mixed design experiments are ideal for collecting time-courses across several groups of interest, allowing you to investigate the driving forces of human behavior in more detail than cross-sectional or longitudinal designs alone.

Ultimately, which design you choose is driven primarily by your research question. Of course, you can run a cross-sectional study first to get an idea of the potential factors affecting outcomes, and then do a more fine-grained longitudinal study to investigate cause and effect in more detail.

In the next section we will explain in more detail how stimuli should be arranged and which sensors are relevant.

Selecting and arranging stimuli

Experiments in human cognitive-behavior research typically involve some kind of stimulation used to evoke a reaction from respondents. The two most crucial stimulus-related questions are: Which stimuli do I need? In which sequence shall I present the stimuli?

Types of stimuli

Stimuli come in a range of modalities including audio, visual, haptic, olfactory etc. Multimodal stimuli combine several modalities. The following stimuli are used in academic and commercial research studies on human behavior:

• Images / pictures
• Software interfaces
• Devices (car interieur, aircraft cockpit, milkshake machine etc.)
• Communication with others via phone, web or face-to-face
• Complex scenes (VR, real environments)
• Sound (sine waves, complex sound, spoken language, music)
• Olfaction (flavors, smells)
• Haptic stimuli (object exploration by touch, pressure plates, vibrating sensors, haptic robots)
• Questionnaires and surveys (web- or software-based, paper and pencil)

Stimulus sequence

Stimuli are generally presented to respondents in a specific sequence. What are typical sequences used in human cognitive-behavioral research?

Fixed stimulus sequence

Fixed sequences are necessary whenever randomized sequences do not make sense or cannot be employed. For example, when combining a website test with a website-related interview it doesn’t make sense to ask website-related questions first and then tell the respondent to actually use the website.

Here, the only meaningful sequence is to do the website exploration first and the questionnaire second. When it comes to comparing different versions of a stimulus, for example, websites A and B, fixed sequences can also be used.

Random stimulus sequence

As you have learned before, presenting stimuli in the same sequence to all respondents bears the risk of sequential effects. Respondents might rate the first stimulus always higher because they are still motivated, engaged and curious.

After two long hours at the lab, exhaustion might take over, so ratings might be low even if the tested product or service exceeds all previous expectations. This can be avoided by presenting stimuli in random order.

Counterbalanced sequence

To avoid the issues of complete randomization, counterbalanced designs try to achieve an even distribution of conditions across the stimulus slots of the experiment. In the example below, two stimulus conditions A and B are counterbalanced across six respondents, so that three respondents are exposed to stimulus A first, and the other three respondents are exposed to stimulus B first.

Block design

Sometimes it doesn’t make sense to randomize the entire stimulus list as there might be some internal logic and sequence. Let’s assume you would like to evaluate respondents’ behavior when unpacking several food packages.

For each package, there’s a fixed evaluation protocol where (a) the package is unveiled and (b) respondents are asked to describe their associations verbally. Then, (c) they should pick up the package and open it and (d) describe their experience. This sequence from step (a) to (d) can also be characterized as an experimental “block”, which is supposed to be repeated for all tested packages.

While the package presentation sequence is randomized, the content of each of the blocks stays the same.

Repeated design

EEG and other physiological recordings sometimes require repeated presentations of the same stimulus. This is necessary because the stimulus-driven changes in brain activity are much smaller compared to the ongoing activity. Presenting the same stimulus several times makes sure that enough data is present to get to valid conclusions.

However, stimulus repetition can also be done for eye tracking studies. In this case, the randomization procedures listed above apply as well.

You might be interested in the number of repetitions necessary to get to results. Unfortunately, this cannot be answered globally, as it depends on several factors such as magnitude of the expected effect/difference between two conditions, stimulus modality, physiological effect of interest, and other factors that take impact on experimental outcomes.

Also, there are strong statistical considerations which are beyond the scope of this general introduction.

Modalities and sensors

Whenever you design experiments for human cognitive-behavior research, you certainly want to consider which biosensors you collect data from. Human behavior is a complex interplay of a variety of different processes, ranging from completely unconscious modulations of emotional reactions to decision-making based on conscious thoughts and cognition. In fact, each of our emotional and cognitive responses is driven by factors such as arousal, workload, and environmental conditions that impact our well-being in that very moment.

All of these aspects of human behavior can be captured by self-reports (via interviews or surveys), specific devices (such as eye trackers, EEG systems, GSR and ECG sensors ) or camera-based facial expression analysis.

TV ads, video games, movies, websites, devices as well as social interaction partners in private life and in the workplace – we could process none of these without our vision. The human brain is fine-tuned for visual input and controlling eye movements. Therefore, it makes immediate sense to collect information on gaze position and pupil dilation from eye tracking. If you present visual stimuli on screen, you should always collect eye tracking data to be absolutely sure where respondents are directing their gaze to and how this is affecting cognitive processing. Second, monitoring pupil dilation can give valuable insights into arousal and stress levels of a respondent. As pupil dilation is an autonomic process, it cannot be controlled consciously. Eye tracking recordings allow you to monitor both respondents’ engagement and motivation as well as arousal levels during the encounter with emotional or cognitively challenging stimuli.

Galvanic skin response (GSR) or electrodermal activity (EDA) reflects the amount of sweat secretion from sweat glands in our skin. Increased sweating results in higher skin conductivity. When exposed to emotional content, we sweat emotionally. GSR recordings in conjunction with EEG are extremely powerful as skin conductance is controlled subconsciously, that is, by deeper and older brain structures than the cognitive processes that are monitored by EEG. Therefore, adding GSR offers tremendous insights into the unfiltered, unbiased emotional arousal of a respondent.

Facial Expression Analysis

With facial expression analysis you can assess if respondents are truly expressing their positive attitude in observable behavior. Facial expression analysis is a non-intrusive method to assess head position and orientation (so you always know where your respondents are positioned relative to the stimulus), expressions (such as lifting of the eyebrows or opening of the mouth) and global facial expressions of basic emotions (joy, anger, surprise etc.) using a webcam placed in front of the respondent. Facial data is extremely helpful to monitor engagement, frustration or drowsiness.

(facial) EMG

Electromyographic sensors monitor the electric energy generated by body movements. EMG sensors can be used to monitor muscular responses of the face, hands or fingers in response to any type of stimulus material. Even subtle activation patterns associated with consciously controlled hand/finger movements (startle reflex) can be assessed with EMG. Collecting synchronized EMG data is relevant for anyone interested in how movements of the eyes and limbs are prepared and executed, but also how movements are prevented and actions are inhibited.

Monitoring heart activity with ECG electrodes attached to the chest or optical heart rate sensors attached to finger tips allows you to track respondents’ physical state, their anxiety and stress levels (arousal), and how changes in physiological state relate to their actions and decisions. Tracking respondents’ physical exhaustion with ECG sensors can provide helpful insights into cognitive-affective processes under bodily straining activity.

Electroencephalography (EEG) is a neuroimaging technique measuring electrical activity generated by the brain from the scalp surface using portable sensors and amplifier systems. It undoubtedly is your means of choice when it comes to assess brain activity associated with perception, cognitive behavior, and emotional processes. EEG reveals substantial insights into sub-second brain dynamics of engagement, motivation, frustration, cognitive workload, and further metrics associated with stimulus processing, action preparation, and execution. Simply put: EEG impressively tells which parts of the brain are active while we perform a task or are exposed to certain stimulus material.

Self-reports

Any experiment should contain self-reported data collection stages, for example at the beginning of the session, during data collection , and at the very end. Gathering demographic data (gender, age, socio-economical status, etc.) helps describing the respondent group in more detail. Also, self-reported data from interviews and surveys helps tremendously to gain insights into the subjective world of the respondents – their self-perceived levels of attention, motivation and engagement – beyond quantitative values reported by biosensors. Of course, survey results can be utilized to segment your respondents into specific groups for analysis (e.g., young vs. old; male vs. female; novice vs. experienced users).

Experimental design done right with iMotions

Properly designed experiments allow you deep insights into attention, cognition and emotional processing of your desired target population when confronted with physical objects or stimuli. Experimental research has come up with dedicated recommendations on how to prevent experimenter or segmentation bias – randomization strategies for respondent and stimulus selection are an excellent starting point.

Before you get started designing your next human cognitive-behavioral experiment, you certainly want to think about how to arrange stimuli, how to select respondents and which biosensors to use in order to gain maximum insights.

What if there was a multimodal software solution that allows for loading and arranging any type of stimuli, for example, in fixed or randomized sequences, while recording data from EEG, eye tracking, facial expression analysis and other biosensors (such as GSR, ECG, EMG) without having to manually piece everything together?

The iMotions Platform

The iMotions Platform is one easy-to-use software solution for study design, multi-sensor calibration, data collection, and analysis.

Out of the box, iMotions supports over 50 leading biosensors including facial expression analysis, GSR, eye tracking, EEG, ECG, and EMG, as well as surveys for multimodal human behavior research.

• Banerjee, Chaudhury, et al. (2007). Statistics without tears – inputs for sample size calculations. Indian Psychiatry Journal, 16, 150–152.
• Banerjee & Chaudhury (2010). Statistics without tears: Populations and samples. Industrial Psychiatry Journal, 19(1), 60–65.
• Creative Research Systems (2003). Sample Size Calculator. Retrieved from https://www.surveysystem.com/sscalc.htm on 2016-08-06.
• Cooper, Camic et al. (2012). APA handbook of research methods in psychology, Vol 1: Foundations, planning, measures, and psychometrics.
• Cooper, Camic et al. (2012). APA handbook of research methods in psychology, Vol 2: Research designs: Quantitative, qualitative, neuropsychological, and biological.
• Farrington (1991). Longitudinal research strategies: advantages, problems, and prospects. Journal of the American Academy of Child and Adolescent Psychiatry, 30(3), 369–374.
• Hofling et al. (1966). An experimental study of nurse-physician relationships“. Journal of Nervous and Mental Disease, 143, pp. 171-180.
• McLeod (2007). The Milgram Experiment. Retrieved from www.simplypsychology.org/milgram.html on 2016-07-31.
• Martinez-Mesa, Gonzalez-Chica et al. (2014). Sample size: How many participants do need in my research? Anais Brasileiros de Dermatologia, 89(4), 609–615.
• Monahan & Fisher (2010). Benefits of observer effects: Lessons from the field Qualitative Research, 10(1), pp. 357-376.
• Niles (2014). Sample size: How many survey participants do I need ? Retrieved from https://www.sciencebuddies.org/science-fair-projects/project_ideas/Soc_participants.shtml on 2016-08-06
• Ryan (2006). Modern Experimental Design (2nd edition). New York: Wiley Interscience.

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• Experimental Research Designs: Types, Examples & Methods

Experimental research is the most familiar type of research design for individuals in the physical sciences and a host of other fields. This is mainly because experimental research is a classical scientific experiment, similar to those performed in high school science classes.

Imagine taking 2 samples of the same plant and exposing one of them to sunlight, while the other is kept away from sunlight. Let the plant exposed to sunlight be called sample A, while the latter is called sample B.

If after the duration of the research, we find out that sample A grows and sample B dies, even though they are both regularly wetted and given the same treatment. Therefore, we can conclude that sunlight will aid growth in all similar plants.

What is Experimental Research?

Experimental research is a scientific approach to research, where one or more independent variables are manipulated and applied to one or more dependent variables to measure their effect on the latter. The effect of the independent variables on the dependent variables is usually observed and recorded over some time, to aid researchers in drawing a reasonable conclusion regarding the relationship between these 2 variable types.

The experimental research method is widely used in physical and social sciences, psychology, and education. It is based on the comparison between two or more groups with a straightforward logic, which may, however, be difficult to execute.

Mostly related to a laboratory test procedure, experimental research designs involve collecting quantitative data and performing statistical analysis on them during research. Therefore, making it an example of quantitative research method .

What are The Types of Experimental Research Design?

The types of experimental research design are determined by the way the researcher assigns subjects to different conditions and groups. They are of 3 types, namely; pre-experimental, quasi-experimental, and true experimental research.

Pre-experimental Research Design

In pre-experimental research design, either a group or various dependent groups are observed for the effect of the application of an independent variable which is presumed to cause change. It is the simplest form of experimental research design and is treated with no control group.

Although very practical, experimental research is lacking in several areas of the true-experimental criteria. The pre-experimental research design is further divided into three types

• One-shot Case Study Research Design

In this type of experimental study, only one dependent group or variable is considered. The study is carried out after some treatment which was presumed to cause change, making it a posttest study.

• One-group Pretest-posttest Research Design: 

This research design combines both posttest and pretest study by carrying out a test on a single group before the treatment is administered and after the treatment is administered. With the former being administered at the beginning of treatment and later at the end.

• Static-group Comparison: 

In a static-group comparison study, 2 or more groups are placed under observation, where only one of the groups is subjected to some treatment while the other groups are held static. All the groups are post-tested, and the observed differences between the groups are assumed to be a result of the treatment.

Quasi-experimental Research Design

  The word “quasi” means partial, half, or pseudo. Therefore, the quasi-experimental research bearing a resemblance to the true experimental research, but not the same.  In quasi-experiments, the participants are not randomly assigned, and as such, they are used in settings where randomization is difficult or impossible.

 This is very common in educational research, where administrators are unwilling to allow the random selection of students for experimental samples.

Some examples of quasi-experimental research design include; the time series, no equivalent control group design, and the counterbalanced design.

True Experimental Research Design

The true experimental research design relies on statistical analysis to approve or disprove a hypothesis. It is the most accurate type of experimental design and may be carried out with or without a pretest on at least 2 randomly assigned dependent subjects.

The true experimental research design must contain a control group, a variable that can be manipulated by the researcher, and the distribution must be random. The classification of true experimental design include:

• The posttest-only Control Group Design: In this design, subjects are randomly selected and assigned to the 2 groups (control and experimental), and only the experimental group is treated. After close observation, both groups are post-tested, and a conclusion is drawn from the difference between these groups.
• The pretest-posttest Control Group Design: For this control group design, subjects are randomly assigned to the 2 groups, both are presented, but only the experimental group is treated. After close observation, both groups are post-tested to measure the degree of change in each group.
• Solomon four-group Design: This is the combination of the pretest-only and the pretest-posttest control groups. In this case, the randomly selected subjects are placed into 4 groups.

The first two of these groups are tested using the posttest-only method, while the other two are tested using the pretest-posttest method.

Examples of Experimental Research

Experimental research examples are different, depending on the type of experimental research design that is being considered. The most basic example of experimental research is laboratory experiments, which may differ in nature depending on the subject of research.

Administering Exams After The End of Semester

During the semester, students in a class are lectured on particular courses and an exam is administered at the end of the semester. In this case, the students are the subjects or dependent variables while the lectures are the independent variables treated on the subjects.

Only one group of carefully selected subjects are considered in this research, making it a pre-experimental research design example. We will also notice that tests are only carried out at the end of the semester, and not at the beginning.

Further making it easy for us to conclude that it is a one-shot case study research. 

Employee Skill Evaluation

Before employing a job seeker, organizations conduct tests that are used to screen out less qualified candidates from the pool of qualified applicants. This way, organizations can determine an employee’s skill set at the point of employment.

In the course of employment, organizations also carry out employee training to improve employee productivity and generally grow the organization. Further evaluation is carried out at the end of each training to test the impact of the training on employee skills, and test for improvement.

Here, the subject is the employee, while the treatment is the training conducted. This is a pretest-posttest control group experimental research example.

Evaluation of Teaching Method

Let us consider an academic institution that wants to evaluate the teaching method of 2 teachers to determine which is best. Imagine a case whereby the students assigned to each teacher is carefully selected probably due to personal request by parents or due to stubbornness and smartness.

This is a no equivalent group design example because the samples are not equal. By evaluating the effectiveness of each teacher’s teaching method this way, we may conclude after a post-test has been carried out.

However, this may be influenced by factors like the natural sweetness of a student. For example, a very smart student will grab more easily than his or her peers irrespective of the method of teaching.

What are the Characteristics of Experimental Research?  

Experimental research contains dependent, independent and extraneous variables. The dependent variables are the variables being treated or manipulated and are sometimes called the subject of the research.

The independent variables are the experimental treatment being exerted on the dependent variables. Extraneous variables, on the other hand, are other factors affecting the experiment that may also contribute to the change.

The setting is where the experiment is carried out. Many experiments are carried out in the laboratory, where control can be exerted on the extraneous variables, thereby eliminating them.

Other experiments are carried out in a less controllable setting. The choice of setting used in research depends on the nature of the experiment being carried out.

• Multivariable

Experimental research may include multiple independent variables, e.g. time, skills, test scores, etc.

Why Use Experimental Research Design?  

Experimental research design can be majorly used in physical sciences, social sciences, education, and psychology. It is used to make predictions and draw conclusions on a subject matter. 

Some uses of experimental research design are highlighted below.

• Medicine: Experimental research is used to provide the proper treatment for diseases. In most cases, rather than directly using patients as the research subject, researchers take a sample of the bacteria from the patient’s body and are treated with the developed antibacterial

The changes observed during this period are recorded and evaluated to determine its effectiveness. This process can be carried out using different experimental research methods.

• Education: Asides from science subjects like Chemistry and Physics which involves teaching students how to perform experimental research, it can also be used in improving the standard of an academic institution. This includes testing students’ knowledge on different topics, coming up with better teaching methods, and the implementation of other programs that will aid student learning.
• Human Behavior: Social scientists are the ones who mostly use experimental research to test human behaviour. For example, consider 2 people randomly chosen to be the subject of the social interaction research where one person is placed in a room without human interaction for 1 year.

The other person is placed in a room with a few other people, enjoying human interaction. There will be a difference in their behaviour at the end of the experiment.

• UI/UX: During the product development phase, one of the major aims of the product team is to create a great user experience with the product. Therefore, before launching the final product design, potential are brought in to interact with the product.

For example, when finding it difficult to choose how to position a button or feature on the app interface, a random sample of product testers are allowed to test the 2 samples and how the button positioning influences the user interaction is recorded.

What are the Disadvantages of Experimental Research?  

• It is highly prone to human error due to its dependency on variable control which may not be properly implemented. These errors could eliminate the validity of the experiment and the research being conducted.
• Exerting control of extraneous variables may create unrealistic situations. Eliminating real-life variables will result in inaccurate conclusions. This may also result in researchers controlling the variables to suit his or her personal preferences.
• It is a time-consuming process. So much time is spent on testing dependent variables and waiting for the effect of the manipulation of dependent variables to manifest.
• It is expensive.
• It is very risky and may have ethical complications that cannot be ignored. This is common in medical research, where failed trials may lead to a patient’s death or a deteriorating health condition.
• Experimental research results are not descriptive.
• Response bias can also be supplied by the subject of the conversation.
• Human responses in experimental research can be difficult to measure.

What are the Data Collection Methods in Experimental Research?  

Data collection methods in experimental research are the different ways in which data can be collected for experimental research. They are used in different cases, depending on the type of research being carried out.

1. Observational Study

This type of study is carried out over a long period. It measures and observes the variables of interest without changing existing conditions.

When researching the effect of social interaction on human behavior, the subjects who are placed in 2 different environments are observed throughout the research. No matter the kind of absurd behavior that is exhibited by the subject during this period, its condition will not be changed.

This may be a very risky thing to do in medical cases because it may lead to death or worse medical conditions.

2. Simulations

This procedure uses mathematical, physical, or computer models to replicate a real-life process or situation. It is frequently used when the actual situation is too expensive, dangerous, or impractical to replicate in real life.

This method is commonly used in engineering and operational research for learning purposes and sometimes as a tool to estimate possible outcomes of real research. Some common situation software are Simulink, MATLAB, and Simul8.

Not all kinds of experimental research can be carried out using simulation as a data collection tool . It is very impractical for a lot of laboratory-based research that involves chemical processes.

A survey is a tool used to gather relevant data about the characteristics of a population and is one of the most common data collection tools. A survey consists of a group of questions prepared by the researcher, to be answered by the research subject.

Surveys can be shared with the respondents both physically and electronically. When collecting data through surveys, the kind of data collected depends on the respondent, and researchers have limited control over it.

Formplus is the best tool for collecting experimental data using survey s. It has relevant features that will aid the data collection process and can also be used in other aspects of experimental research.

Differences between Experimental and Non-Experimental Research 

1. In experimental research, the researcher can control and manipulate the environment of the research, including the predictor variable which can be changed. On the other hand, non-experimental research cannot be controlled or manipulated by the researcher at will.

This is because it takes place in a real-life setting, where extraneous variables cannot be eliminated. Therefore, it is more difficult to conclude non-experimental studies, even though they are much more flexible and allow for a greater range of study fields.

2. The relationship between cause and effect cannot be established in non-experimental research, while it can be established in experimental research. This may be because many extraneous variables also influence the changes in the research subject, making it difficult to point at a particular variable as the cause of a particular change

3. Independent variables are not introduced, withdrawn, or manipulated in non-experimental designs, but the same may not be said about experimental research.

Experimental Research vs. Alternatives and When to Use Them

1. experimental research vs causal comparative.

Experimental research enables you to control variables and identify how the independent variable affects the dependent variable. Causal-comparative find out the cause-and-effect relationship between the variables by comparing already existing groups that are affected differently by the independent variable.

For example, in an experiment to see how K-12 education affects children and teenager development. An experimental research would split the children into groups, some would get formal K-12 education, while others won’t. This is not ethically right because every child has the right to education. So, what we do instead would be to compare already existing groups of children who are getting formal education with those who due to some circumstances can not.

Pros and Cons of Experimental vs Causal-Comparative Research

• Causal-Comparative:   Strengths:  More realistic than experiments, can be conducted in real-world settings.  Weaknesses:  Establishing causality can be weaker due to the lack of manipulation.

2. Experimental Research vs Correlational Research

When experimenting, you are trying to establish a cause-and-effect relationship between different variables. For example, you are trying to establish the effect of heat on water, the temperature keeps changing (independent variable) and you see how it affects the water (dependent variable).

For correlational research, you are not necessarily interested in the why or the cause-and-effect relationship between the variables, you are focusing on the relationship. Using the same water and temperature example, you are only interested in the fact that they change, you are not investigating which of the variables or other variables causes them to change.

Pros and Cons of Experimental vs Correlational Research

3. experimental research vs descriptive research.

With experimental research, you alter the independent variable to see how it affects the dependent variable, but with descriptive research you are simply studying the characteristics of the variable you are studying.

So, in an experiment to see how blown glass reacts to temperature, experimental research would keep altering the temperature to varying levels of high and low to see how it affects the dependent variable (glass). But descriptive research would investigate the glass properties.

Pros and Cons of Experimental vs Descriptive Research

4. experimental research vs action research.

Experimental research tests for causal relationships by focusing on one independent variable vs the dependent variable and keeps other variables constant. So, you are testing hypotheses and using the information from the research to contribute to knowledge.

However, with action research, you are using a real-world setting which means you are not controlling variables. You are also performing the research to solve actual problems and improve already established practices.

For example, if you are testing for how long commutes affect workers’ productivity. With experimental research, you would vary the length of commute to see how the time affects work. But with action research, you would account for other factors such as weather, commute route, nutrition, etc. Also, experimental research helps know the relationship between commute time and productivity, while action research helps you look for ways to improve productivity

Pros and Cons of Experimental vs Action Research

Conclusion  .

Experimental research designs are often considered to be the standard in research designs. This is partly due to the common misconception that research is equivalent to scientific experiments—a component of experimental research design.

In this research design, one or more subjects or dependent variables are randomly assigned to different treatments (i.e. independent variables manipulated by the researcher) and the results are observed to conclude. One of the uniqueness of experimental research is in its ability to control the effect of extraneous variables.

Experimental research is suitable for research whose goal is to examine cause-effect relationships, e.g. explanatory research. It can be conducted in the laboratory or field settings, depending on the aim of the research that is being carried out. 

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• Published: 28 August 2024

Proton exchange membrane-like alkaline water electrolysis using flow-engineered three-dimensional electrodes

• Fernando Rocha 1 ,
• Christos Georgiadis   ORCID: orcid.org/0000-0003-0251-8025 1 ,
• Kevin Van Droogenbroek 1 ,
• Renaud Delmelle   ORCID: orcid.org/0000-0003-1455-4656 1 ,
• Xavier Pinon 1 ,
• Grzegorz Pyka   ORCID: orcid.org/0000-0001-7616-5811 1 ,
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• Franz Egert   ORCID: orcid.org/0009-0005-7415-7830 2 , 3 ,
• Fatemeh Razmjooei   ORCID: orcid.org/0000-0002-8355-6252 2 ,
• Syed-Asif Ansar 2 ,
• Shigenori Mitsushima 4 &
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• Electrochemistry
• Hydrogen energy

For high rate water electrolysers, minimising Ohmic losses through efficient gas bubble evacuation away from the active electrode is as important as minimising activation losses by improving the electrode’s electrocatalytic properties. In this work, by a combined experimental and computational fluid dynamics (CFD) approach, we identify the topological parameters of flow-engineered 3-D electrodes that direct their performance towards enhanced bubble evacuation. In particular, we show that integrating Ni-based foam electrodes into a laterally-graded bi-layer zero-gap cell configuration allows for alkaline water electrolysis to become Proton Exchange Membrane (PEM)-like, even when keeping a state-of-the-art Zirfon diaphragm. Detailed CFD simulations, explicitly taking into account the entire 3-D electrode and cell topology, show that under a forced uniform upstream electrolyte flow, such a graded structure induces a high lateral velocity component in the direction normal to and away from the diaphragm. This work is therefore an invitation to start considering PEM-like cell designs for alkaline water electrolysis as well, in particular the use of square or rectangular electrodes in flow-through type electrochemical cells.

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Introduction.

The increasingly extreme effects of anthropogenic climate interference necessitate urgent actions to limit the rise in global average temperature 1 . To address these threats, the international community has united for more than 3 decades under several climate agreements 2 , 3 , 4 trying to impose a transition from fossil fuels to renewable energy sources 5 . While electricity is projected to become the primary energy carrier in a net-zero society, there are energy needs for which electricity cannot easily or economically replace fossil fuels 6 . In this context, hydrogen and hydrogen-based fuels emerge as promising alternatives to meet specific needs 6 . Hydrogen exhibits remarkable flexibility, as it can be converted to electricity through fuel cells 7 or gas turbines 8 , and to heat through burners 9 . Moreover, hydrogen can be converted to other fuels, such as ammonia through the Haber-Bosch process 10 or synthetic kerosene through Fischer-Tropsch reactions 11 . As a result, hydrogen and hydrogen-based fuels are expected to play a crucial role in transport to power long‐haul heavy‐duty trucks 6 , ships 12 and airplanes 6 . Additionally, since most renewable energy sources generate energy intermittently, hydrogen also offers a solution for long-term energy storage and load balancing 13 , 14 . Furthermore, hydrogen can contribute as well to the decarbonization of industrial sectors, like replacing coal as a reducing agent in steel factories 15 or substituting fossil fuels in the provision of high-temperature heat as in the cement industry 6 .

Water electrolysis driven by renewable electricity stands as the most sustainable approach to hydrogen production 16 . At present, there are two commercially available technologies: alkaline and proton exchange membrane (PEM) 17 . The former presents the lowest capital cost per kilowatt of electricity 18 but suffers from the lowest hydrogen production rate per electrode area 19 . PEM electrolysis can achieve much higher production rates, but depends on scarce and expensive electrocatalytic materials, such as iridium and platinum 19 . The ideal electrolyser should combine the advantages of both systems, presenting high production rates while using inexpensive and abundant electrode materials 20 .

In this respect, the major novelty of the current work is that we have been able to demonstrate that for water electrolysis, efforts in tailoring the electrode’s topology towards enhanced bubble evacuation has the potential of a similar performance improvement than when merely optimising its electro-catalytic composition. This opens up a new and yet largely unexplored degree of freedom in the design of highly performing electrodes for use in next generation high-rate electrolysers. Since these are made to operate at ever increasing current densities (>1.0 A·cm −2 ), bubble removal efficiency (mass transfer) can be expected to become as important as the electrochemical reaction itself (electron transfer). In particular, by a combined experimental and computational fluid dynamics (CFD) approach, we have identified the electrode’s 3-D structural parameters that allow to tune its performance towards enhanced bubble evacuation. We also demonstrated that integrating such flow-engineered 3-D electrodes into a laterally-graded cell configuration allows to significantly boost the performance of alkaline water electrolysis up to 2 A·cm −2 at <2 V cell voltage, even when keeping a state-of-the-art Zirfon diaphragm. The improved performance was shown to be the synergistic effect of 3 factors, as illustrated in Fig.  1 : (1) the use of a forced high upstream electrolyte flow in a dedicated zero-gap flow-through cell, as opposed to the serpentine flow fields often used in the literature; (2) the integration of 3-D electrodes using a bi-layer configuration consisting of a fine and thin foam in contact with a coarse and thick foam, the combination being flow-engineered to enhance gas removal; (3) the application of a Ni-based coating on the fine foam and its activation into Raney Ni in both the anodic and cathodic parts, resulting in catalytically highly active electrodes free of Pt group metals that are able to maintain their activity at high current densities thanks to the improved bubble evacuation.

a Flow and pumping direction of both anolyte and catholyte and the position of the bi-layer foam electrodes. The area of the rectangular 4.5 × 9.5 cm 2 Zirfon diaphragm exposed to the electrolyte is enclosed by dashed lines. It is larger than the fluid domain (2 cm width) to prevent leakage; b integration of 3-D electrodes using a bi-layer configuration consisting of a fine, 1.6 mm thin 450 µm pore size foam in contact with a coarse, 4 mm thick 3000 µm pore size foam as porous transport layer, the combination being flow-engineered to enhance gas removal; c application of a Ni-based coating on the fine foam and its activation into Raney Ni in both the anodic and cathodic parts, resulting in catalytically highly active electrodes free of Pt group metals. The coating exposed surface is facing the 500 µm thick Zirfon diaphragm.

We are aware that quite a bit of prior art, both in PEM fuel cells and electrolysers, already reported on the use of so-called 3-D porous electrodes, including foams and meshes 21 , 22 , 23 . However, in most of these configurations, these were rather used as porous transport layers (PTL) only allowing for and not necessarily enhancing bubble evacuation. Such PTL are then typically used in combination with electrocatalytic elements like perforated plates. However, the latter rather serve as 2-D electrodes, not as 3-D porous electrodes, in the sense that no upstream electrolyte flow can pass through these electrodes. In this work, we report on the use of a flow-through bi-layer 3-D foam configuration and demonstrate how such a configuration induces particular electrolyte flow characteristics thanks to a pressure gradient in the direction normal to and away from the diaphragm that allow to enhance bubble removal.

Results and discussion

Pem-like alkaline water electrolysis using a state-of-the-art zirfon diaphragm.

The synergy demonstrated in this work by the use of 3-D electrodes in a flow-engineered bi-layer zero-gap cell geometry can be best understood and appreciated when comparing to available literature data. Several recent papers on alkaline water electrolysis under similar conditions (temperature 70–80 °C, KOH concentration 24–30 wt%) and using a Zirfon gas separator and Ni-based electrode materials are summarised in Table  1 . Note that a state-of-the-art Zirfon diaphragm was chosen in this work in order to have the largest possible consistent comparative data set from the literature. A further performance improvement can be expected using our same 3-D electrode and cell configuration but replacing the Zirfon diaphragm with more conductive alkaline-based membranes 19 , 24 .

Representative polarisation curves are shown in Fig.  2 for two upstream flow velocities for a pure Ni bi-layer (a) and a bi-layer with a catalytic Raney Ni coating (b). The available literature range is indicated as well. In order to gain more insight into the significant performance enhancement that was obtained in both cases, all polarisation curves within the literature range, which are shown in full detail in Supplementary Fig.  S1 , have been fit using the following fundamental equation 25 :

with E cell the measured cell voltage, E eq the equilibrium cell voltage, and j the imposed current density. The free fitting parameters in the above equation are b , i.e. the sum of the Tafel slopes for the cathodic and anodic reaction, the non-linear average j 0 of their respective exchange current densities, and the total Ohmic resistance R total . The latter consists of the resistance of the Zirfon diaphragm and any remaining electrolyte resistance, potentially influenced by non-evacuated bubbles, that is not shunted by the zero-gap configuration. As illustrated in Supplementary Fig.  S1 , R Zirfon can then be subtracted out based on known data from the literature, thereby revealing the specific contribution of non-evacuated bubbles as R total - R Zirfon .

a A pure Ni 450/3000 µm bi-layer foam and b a bi-layer with a catalytic Raney Ni coating on the 450 µm foam. A structural representation of the bi-layer as obtained from X-ray micro-computed tomography is included within the figure. Lines are measured from cyclic voltammetry, with bullet points from galvanostatic experiments being superimposed. Hatched zones represent the range of literature data, for which all polarisation curves are shown in Supplementary Fig.  S1 .

All fitting results, including standard deviations, have been summarised in Supplementary Table  S1 , and are shown as cumulative normal distributions in Fig.  3 for the Tafel slope b (top) and R total - R Zirfon (bottom), respectively. For more details on the interpretation of such a data representation in the form of a cumulative normal distribution, we refer to Supplementary Fig.  S3 in SI. Two important observations can be made from these graphs. First of all, the use of a catalytic Raney Ni coating results in a significant decrease in Tafel slope, and hence an improved electrochemical performance, as is already well-documented in the literature. However, fitting results for our own polarisation curves do not differ markedly from literature data: the b -values for both our pure Ni and Raney Ni bi-layers fall on the same cumulative normal distribution as the respective literature data. Therefore, our observed performance enhancement cannot be ascribed to the use of Raney Ni alone.

a Tafel slopes and b area-specific Ohmic resistance minus Zirfon resistance. Raw data are compiled in Supplementary Table  S1 and obtained by fitting all polarisation curves by Eq. ( 1 ). Data from this work are taken at 70 °C and 30 wt% KOH. Error bars represent the standard deviation on the free fitting parameters of Eq. ( 1 ). The red dotted line in ( b ) is simply a guide to the eye.

Secondly, the Zirfon-corrected part of the Ohmic resistance ( R total - R Zirfon ) for the polarisation curves obtained in this work clearly do fall on a different cumulative normal distribution, and the obtained values are also significantly lower than previously reported literature values. This indicates that, although applying a Raney Ni coating is a necessary contribution to the reported performance enhancement, it is the lowering of the contribution of non-evacuated bubbles to the Ohmic resistance in a zero-gap cell configuration that allows to make the difference. This is explicitly confirmed by the fact that, from Supplementary Table  S1 and Fig.  3b , no significant difference in Ohmic resistance was observed after coating: the value of R tot – R Zirfon at 0.35 l·min −1 and 1.60 l·min −1 was 152.3 ± 0.7 mΩ·cm 2 and 130.4 ± 0.5 mΩ·cm 2 , respectively for our pure Ni foams, and 145.9 ± 0.3 mΩ·cm 2 and 128.4 ± 0.2 mΩ·cm 2 , respectively for our Raney Ni foams. We will show below through detailed CFD simulations that it is rather the use of a flow-engineered bi-layer configuration, combined with applying a forced upstream electrolyte flow that allows for the enhanced bubble evacuation.

Finally, we are well aware that Anion Exchange Membrane Water Electrolysis (AEMWE) has already been shown to provide current densities easily surpassing 2 A·cm −2 at 2 V cell voltage 26 . However, the main novelty of the current paper is that the integration of well-known electrocatalysts (Raney Ni) and a state-of-the-art durable gas separator (Zirfon diaphragm) into a well-thought flow-engineered cell design based on a laterally-graded bi-layer 3-D foam configuration has the potential to significantly boost the performance of standard alkaline water electrolysers towards PEM-like behaviour (even without the need for new catalyst or membranes). Raney Ni electrodes and a Zirfon diaphragm were simply considered in this paper as well-known bench-mark components, allowing to demonstrate and isolate the effect of the flow-engineered bi-layer cell design.

Forced uniform upstream electrolyte flow

From Fig.  2 and Supplementary Table S1 , it can be seen that an increase in electrolyte flow rate from the minimum to the maximum value (i.e. from 0.35 to 1.6 l·min −1 ) caused a decrease of 12.0 ± 0.2% and 14.4 ± 0.5% in Ohmic resistance for the coated and the non-coated foam, respectively. The observed effect is attributed to enhanced bubble removal caused by the forced upstream electrolyte flow. Before the start of hydrogen and oxygen production, both the electrode surface and the electrolyte are free of bubbles. As soon as gas evolution starts, bubbles start to nucleate and grow on the electrode surface and pass into the electrolyte once a critical size is achieved. Any adhering insulating bubbles decrease the electrochemically active electrode surface (screening effect) 27 , while detached bubbles tend to lower the electrolyte conductivity (voidage effect) 28 . An increase in upstream flow rate (i.e. in the direction of gas evacuation) is capable of shortening the bubble growth time and decrease its maximum size, thereby lowering the voltage loss in traditional gap-cells 29 . In the case of a zero-gap cell configuration however, Haverkort 30 convincingly showed that any effect of non-evacuated bubbles on the Ohmic resistance is restricted to electrolyte areas that are not shunted by the much more conductive 3-D electrodes. This may include their trapping inside the micro-porous Zirfon diaphragm or in the small gap (typically 50–100 µm thick 25 ) that usually remains between the Zirfon and the electrode area facing the diaphragm (see also Fig.  1c ). In particular, ref. 30 demonstrated, both theoretically and experimentally, that the frontal area of expanded metal electrodes, i.e. the area facing the diaphragm, was not electrically active in a zero-gap cell using a Zirfon separator. This was attributed to the fact that gas bubbles either enter the separator or block the electrode surface in the region between the electrode and the separator, hence creating a screening effect which increases the Ohmic resistance. The fact that our R tot – R Zirfon values are significantly lower than the literature is therefore an indication of a much better bubble removal efficiency in our flow-engineered bilayer zero-gap cell, not only in the interior of the 3-D electrode itself (which in the case of zero-gap cells is not measurable via the Ohmic resistance) but also in the small region remaining between the 3-D electrode and the separator. Also note in Fig.  2 how the polarisation curves at minimum flow show many irregularities, indicative of poor bubble evacuation, while it becomes much smoother when increasing the flow rate.

Looking first at the data for our pure Ni electrodes, their performance clearly outperforms the ones previously reported in the literature. At a constant current density of 1.8 A·cm −2 , the best result from the literature was a cell voltage of 2.6 V 24 , whereas our own study achieved a significantly lower voltage of only 2.1 V. Both studies used 2 × 2 cm 2 Ni foam electrodes, but instead of using a coarse porous transport foam as we did, ref. 24 used a serpentine-like flow plate at a flow rate of only 0.05 l·min −1 , which is 7 times lower than the minimum flow used in our work. In terms of Zirfon corrected Ohmic resistance ( R total - R Zirfon ), our work achieved a minimum value of 130 ± 1 mΩ·cm 2 for pure Ni electrodes, which is lower than the lowest literature value of 143 ± 8 mΩ·cm 2 , reported in ref. 31 at a pressure of 30 bar. While it is well-known that at higher pressures, the bubble size decreases thereby reducing their contribution to the overall cell voltage, our flow-engineered 3-D electrodes proved to be superior even under atmospheric conditions.

Secondly, as to the coating effect, in ref. 19 the Ohmic resistance doubled when going from pure Ni to Raney Ni, reaching 297 ± 5 mΩ·cm 2 . This indicates that in that work, the use of serpentine-like flow plates was unable to efficiently remove the higher amount of gas bubbles produced at the Raney Ni electrodes. In ref. 32 2-D perforated coated Raney Ni plates were used as electrodes, but again in the presence of non-optimised flow fields and a low flow rate of 0.05 l·min −1 . As a result, their Ohmic resistance was 270 ± 4 mΩ·cm 2 , twice as high as in our work. Refs. 33 , 34 compared different Raney Ni-based foams as electrodes. The electrolyte flow rate in these studies was set at 0.45 l·min −1 , but since the electrode area was 34.56 cm 2 , almost 9 times larger than in our case, this corresponds to a mere 0.05 l·min −1 as well when normalised to the same electrode area of 4 cm 2 . In ref. 34 , the nature of the porous transport layer was not specified, but in ref. 33 a single serpentine flow field was used. The result was an Ohmic resistance of 300 ± 10 mΩ·cm 2 , more than double the value we obtained by our flow-engineered bi-layer configuration under high upstream flow.

Based on the above comparison with literature data, we can already conclude that the serpentine flow fields often used in the literature are not the best way to enhance bubble removal. It seems that they have been simply adopted without further question from transport studies related to fuel cells 35 , 36 . However, mass transport in fuel cells involves the transport of gases as reactants away from the flow field into the catalyst layer. This is fundamentally different from the basic functioning of a water electrolyser, where the main transport issue is the evacuation of gas bubbles as the reaction product away from the electrode. Our own cell design and electrode configuration also mitigate the negative effects of high electrolyte flows, most notably the occurrence of jet-like, unstable flows due to small inlet diameters. Instead, in our dedicated flow cell, the flow is uniformly distributed over the entire width of the bi-layer electrode. This results in an enhanced transport of bubbles away from the catalytic foam, either directly to the outlet or laterally towards the coarse porous transport foam. This will be demonstrated in the next section by both single and 2-phase simulations that explicitly take into account the 3-D bi-layer electrode topology.

Serpentine flow fields rather impede such a bubble extraction. This is illustrated in Supplementary Fig.  S2 by comparing its so-called Cumulative Distribution Function (CDF) and Residence Time Distribution (RTD) function to the one for our bi-layer configuration. As to the CDF (Supplementary Fig.  S2e ), it can be seen that the time required for the fraction of tracers to reach 100% of the exit stream is significantly lower for the flow-engineered bi-layer configuration. This already suggests that our bi-layer configuration will lead to a faster bubble extraction as well. The latter is confirmed by the RTD functions in Supplementary Fig.  S2d : the major fraction of tracers leaving the cell spend less time in our bi-layer configuration than when using the serpentine flow field, resulting in a much smaller mean residence time. Moreover, the RTD function of the bi-layer configuration also has a narrower distribution, meaning that passive scalars convected by the electrolyte flow leave the cell after having spent similar residence times. This indicates that our bi-layer configuration also favours electrolyte and bubble flow directly towards the outlet of the cell. In the case of a serpentine flow field, bubble extraction is rather impeded due to back-and-forth flow circulations at the interface between the catalytic region and the flow channels, as illustrated by the velocity streamlines in Supplementary Fig.  S2c,d . This not only leads to a higher mean residence time, but also to a much broader residence time distribution.

An important remark relates to the electrochemical methodology that was used to extract the above-discussed data. It was based on fitting of polarisation curves according to electrochemical Eq. ( 1 ), a well-documented practice in the literature 25 and the only available way to consistently compare our own results to data from the literature. However, by using Eq. ( 1 ), an important assumption was made, namely that the value of the Ohmic resistance R tot obtained from fitting can be considered independent of current density. If we associate this value (and therefore also the value of R tot – R Zirfon ) with non-evacuated gas bubbles, it is not unlikely that it might change (increase) upon increasing current density, as a result of an increased gas production rate. Therefore, we also performed Electrochemical Impedance Spectroscopy (EIS) measurements at 2 different flow rates on our pure Ni bi-layers, and compared the obtained results for the high frequency resistance at different current densities in the range 0.01–2 A·cm −2 to the (single) value of R tot obtained from fitting the entire polarisation curve to Eq. ( 1 ). Results are included in SI as Supplementary Fig.  S4 . A first important observation is that for both flow rates, the EIS-derived high frequency Ohmic resistance shows very little variation with imposed current density: for an electrolyte flow rate of 0.35 l·min −1 and 1.2 l·min −1 , it ranges between 0.24 to 0.30 Ω·cm 2 and 0.23 and 0.27 mΩ·cm 2 , respectively, with an average value of 0.26 ± 0.02 Ω·cm 2 and 0.24 ± 0.02 Ω·cm 2 , respectively. Moreover, these average values (with a relative error in both cases of less than 10%) are also statistically equal to the polarisation curve-fitted values for R tot of 0.27 Ω·cm 2 and 0.24 Ω·cm 2 , respectively, already reported in Supplementary Table  S1 . Both of these findings give further confidence to the R tot – R Zirfon data from our own polarisation curves as presented in Fig.  3b , and in the validity of the associated conclusions related to enhanced bubble removal. They also allow to exclude any significant contribution of current inhomogeneities, which was estimated in ref. 25 as 0.02 Ω·cm 2 in the case of a perforated 2-D plate electrode, much smaller than our reported R tot – R Zirfon values.

A related comment can be made on the magnitude of the error bars on the R tot – R Zirfon data, as reported in Supplementary Table  S1 and included in Fig.  3b as well. As to the latter, they are barely visible for our own data set (red symbols), simply because the relative error was always very small, much smaller than the error on the black and blue literature data. This is an important additional observation that can also be fully understood from the additional EIS measurements discussed above. Indeed, the small error on our own curve fitted Ohmic resistance data can be taken as indicative for the fact that R tot shows very little variation with current density, a statement which was explicitly confirmed by EIS. Moreover, since current density is proportional to bubble production rate, a current density in-dependent value of R tot can also be taken as indirect proof for the high bubble removal efficiency of our own flow-engineered cell configuration. Indeed, all bubbles generated within the catalytic foam will be driven to the PTL and evacuated out, giving the same low value of R tot at low and high current density, i.e. at low and high bubble production rate.

Laterally-graded bi-layer zero-gap cell configuration

The integration of our 3-D bi-layer foam electrodes into a zero-gap cell configuration has been optimized for enhanced bubble evacuation with the help of both single-phase and 2-phase CFD simulations. Particular attention was given to the understanding of the dynamics of electrolyte flow through macro-porous 3-D electrodes. Traditionally, flow in such porous media has been simulated by averaged equations based on the Representative Elementary Volume theory 37 . Progress in computational power has now made it possible to tackle the problem by explicitly describing the full topology of the porous electrode. The latter involves the use of X-ray micro-computed tomography to obtain high-resolution scanning data of the foam topology 38 , 39 , 40 . Based on these scanned data, a meshing workflow was then applied as described in Supplementary Fig. S5 of the SI to obtain computationally ready meshes for our electrode foams, similarly as in refs. 41 , 42 . The detailed flow characteristics are then investigated, with a particular focus on the interface dynamics between a fine catalytic foam acting as a gas production layer and a coarse porous transport foam (PTF) acting as a bubble extraction layer.

In a first step, we have based our initial bi-layer configuration on the experimental results of bubble evacuation efficiency as a function of pore size that we already reported in previous work 43 . In that paper, a drastic increase in available surface fraction, indicative of enhanced bubble removal, was reported from 1000 µm pore sizes onwards. Therefore, our initial bi-layer configuration consisted of a fine, high surface 450 µm foam as a gas production layer, combined with a coarse 3000 µm porous transport foam used as a bubble extraction layer. In order to quantify the bubble extraction capability of our bi-layer configuration, we then extracted from our simulations the y-velocity component averaged over x-z planes for each position normal to the diaphragm. This scalar quantity can be taken as indicative of mass transfer in the lateral y-direction, i.e. in the direction normal to and away from the diaphragm. Figure  4 shows that already for the single-phase simulations, a clear velocity increase is observed as we move towards the coarse bubble evacuation layer, with peaks that depend on the local foam morphology. Its driving mechanism is the pressure discontinuity on the interface between both foams. Indeed, the fine 450 µm catalytic foam acting as the gas production layer exhibits a much higher flow resistance as compared to the coarse 3000 µm porous transport foam (PTF). For example, for an inlet velocity of 0.22 m·s −1 , the upstream z-velocity through the coarse PTF is 0.30 m·s −1 while for the fine gas production layer it is only 0.06 m·s −1 . As a consequence, bubbles that are produced within the fine catalytic foam will be evacuated laterally into the porous transport foam, so that the high catalytically active surface area of the former no longer suffers from bubble entrapment. More details on the simulated flow-induced pressure gradients and their relation to the expected buoyancy forces have been included in SI (Supplementary Fig.  S6 ).

The upstream electrolyte inlet velocity was set at 0.22 m·s −1 . The fine 450 µm pore size catalytic foam is indicated by the dashed region. The inserted picture is the structural representation of the pure Ni 450/3000 µm bi-layer foam used for the CFD simulations, as obtained from X-ray micro-computed tomography.

The above conclusions derived from single-phase simulations are further confirmed (and even enhanced) when considering 2-phase flow, as shown in Fig.  4 as well. Our 2-phase modelling approach, which was based on a simple mixture model with hydrodynamic dispersion, was to start with a stagnant gas mixture inlet with different gas fractions α at the interface of the catalytic foam and the diaphragm (i.e. at distance y = 0 in Fig.  4 ) and then see the variations in the lateral velocity profiles as a function of gas fraction when moving in the direction normal to the diaphragm. Three important and interrelated observations can be made on Fig.  4 when comparing these 2-phase simulations to the single-phase ones. First of all, a significantly higher lateral velocity is obtained for the 2-phase mixtures, in particular at y = 0, i.e. at the interface of the diaphragm and the fine catalytic foam. This is also the location where in our simulations the gas fraction was localised initially. Secondly, this increase in lateral velocity is correlated with an increase in the negative direction of the lateral velocity component in the zone neighbouring the diaphragm. This is indicative for electrolyte flow being directed towards the diaphragm near its interface. Note that in the single-phase simulations, this negative lateral velocity component was present as well, but with a value of only −0.0002 m·s −1 , and hence barely visible on the ordinate scale of Fig.  4 . Finally, the lateral velocity obtained from our simulations for 2-phase mixtures is now significantly higher over the entire thickness of the fine catalytic foam (where bubble generation is being localised) as compared to the single-phase case. In other words, bubble evacuation from the catalytic foam into the coarse PTL foam can be expected to be even more enhanced as compared to the single-phase simulations. We associate this enhanced lateral velocity of the 2-phase mixture with an increased fraction of upstreaming electrolyte being sucked into the catalytic foam, as a result of a less dense mixture situated near the diaphragm (i.e. at y = 0 in our simulations). The latter is shown more clearly in Fig.  5 , which compares the velocity vectors for the single-phase and 2-phase simulations (the latter for α = 0.4). By looking in detail at the entrance section of the bi-layer region (bottom figures), we can see that for the single-phase the largest fraction of upstream electrolyte flow deviates into the coarse 3000 μm foam, while for the 2-phase simulations a significant fraction of electrolyte also enters the fine 450 µm catalytic foam. These figures also demonstrate the importance of performing 2-phase simulations using the explicit topological description of the 3-D foam bilayer configuration. Indeed, the negative lateral velocities observed in Fig.  4 are directly associated with the local morphological details of the small pore sized catalytic foam at the entrance section of the bi-layer region.

The electrolyte upstream inlet velocity was set at 0.22 m·s −1 , as in Fig.  4 . The bottom figures are details at the entrance section of the bi-layer region. The fine catalytic foam is on the left, with a thickness of 1.6 mm. The top figures cover the entire cell width of 5.6 mm.

In a second step, our simulations then revealed that the 3000 μm foam is a more suitable choice as PTF as compared to other available coarse foams, such as the 2200 µm one (see Supplementary Fig.  S7a in SI). It indeed exhibits a lower resistance to the upstream flow, thus creating a higher pressure difference between the catalytic and the porous transport foam. At the same time, as compared to the use of a 450 µm foam in a gap-type cell without any rigid foam-based PTL (corresponding to the “void” case in Supplementary Fig.  S7a ), it offers a more rigid structure to electrically connect the electrode to the flat bipolar plate and to press the catalytic foam against the separator, thereby allowing to maintain the structural and geometrical stability of our zero-gap configuration under high upstream flow conditions. In this respect, the fact that no patterning or profiling is needed of the flat bi-polar plates to improve the electrolyte flow distribution can be seen as another major advantage of our foam-based bi-layer configuration.

We also simulated the flow behaviour for different PTF thicknesses, keeping the catalytic foam thickness fixed at 1.6 mm. Since a 3000 μm sized foam was selected as the optimal PTF, a thickness of at least 4 mm is needed in order to ensure structural stability along the lateral y-direction with at least one pore per thickness. For thicknesses larger than 4 mm, it was found that the high upstream electrolyte flow may generate vortical flow instabilities and recirculation zones in the PTF. This is revealed in Supplementary Fig.  S7b by the negative lateral velocity values in the PTF near the bipolar plate, causing an unwanted electrolyte backflow towards the catalytic foam. As a result, the PTF thickness was kept at 4 mm.

Finally, at the explicit request of one of the reviewers, we also performed a number of flow sensitivity experiments comparing our optimised pure Ni bi-layer foam configuration to the use of a single 450 µm foam combined with a pure Ni knitted mesh-type spacer as PTL, as is often used in industrial-scale systems. These measurements were done under galvanostatic conditions in 30 wt.% KOH but at room temperature. The reason is that, as already described in detail in ref. 44 , the cell temperature in our experimental set-up is precisely controlled by the electrolyte temperature in the two separate anolyte and catholyte reservoirs. Testing at room temperature then allows to mimimise the risk for any unexpected convective effects due to a possible difference in heat transfer between both cell compartments. It also allows to have an additional reference point at zero flow (i.e. at natural convection). In order to limit the cell voltage for these room temperature experiments to <2.5 V, the applied current density was limited to 0.5 A/cm 2 . The raw galvanostatic data are shown in SI as Supplementary Fig.  S8 . A significantly better flow sensitivity can be observed for the bi-layer foam configuration, not only from its much more pronounced reduction in cell voltage, but also from the larger reduction in noise when increasing the flow rate. A quantitative analysis of these data is provided in Supplementary Fig.  S9a , showing that the relative reduction in cell overpotential upon increasing the flow is twice as high for the bi-layer foam configuration. These results are also confirmed by the additional CFD simulation shown in Supplementary Fig.  S9b , where the lateral velocity profile in the y-direction away from the diaphragm has been compared for both configurations. These profiles clearly indicate that replacing the 3000 µm foam by a knitted mesh-type spacer as PTL will fundamentally change the flow behaviour in the cell. Even though the latter offers a higher lateral velocity within the 450 µm foam itself, it can be seen that within the knitted mesh PTL itself, this velocity flattens off. Therefore, the lateral y-velocity away from the diaphragm is significantly lower when considered over the entire width of the cell. This can then be expected to result in a much less pronounced bubble evacuation capacity when combining the 450 µm foam with a knitted mesh-type PTL.

Raney Ni coating and activation

As was already shown in Fig.  3a , the performance improvement by applying a thermal spray coating of Raney Ni to the fine 450 µm pore size foam was in line with existing literature data, and therefore does not represent any real novelty as such. Nonetheless, this is the first time that the application of such a Raney Ni coating and its subsequent activation has been successfully demonstrated on small pore sized foams 45 . Scanning Electron Micrographs (SEM) of both cathode and anode foams before and after coating are shown in Supplementary Fig.  S9 . More quantitative surface area data have been extracted as well from high-resolution X-ray tomography analysis and presented in Supplementary Fig.  S10 . The higher electrochemical activity of the Raney Ni bi-layer electrodes as compared to pure Ni resulted in a significantly lower Tafel slope: 49 ± 7 mV·dec −1 vs. 180 ± 15 mV·dec −1 for Raney Ni and pure Ni, respectively. At the same time, from Supplementary Table  S1 and Fig.  3b , no significant difference in Ohmic resistance was observed after coating, resulting in similar values for R tot – R Zirfon for our pure Ni and Raney Ni foams, respectively. This indicates that, despite its significantly increased ECSA as compared to the pure Ni foam (more than a factor 200, cfr. Methods section) and possibly different microstructural changes during operation, e.g. related to a different formation mechanism of Ni-based (oxy) hydroxides 46 , 47 , the Raney Ni coating does not affect the bubble evacuation capacity of our flow-engineered bi-layer zero-gap cell configuration.

We acknowledge that concerns may still exist as to the durability of the Raney Ni coating, especially when used on 3-D electrodes under forced electrolyte flow. Initial results of 500 h galvanostatic testing are shown as SI in Supplementary Fig.  S11 . Although the data show no performance loss over time, it should be acknowledged that they were not taken on the same dedicated flow-through cell that we used for the CV and EIS measurements. In other words, any possible degradation effect resulting from the relatively high flow rates could not be addressed in the current paper. Nonetheless, a dedicated literature review on the reliability of Raney Ni coatings was able to retrieve some encouraging long-term reliability data 48 , 49 , 50 . For instance, in ref. 48 , Zirfon separators were evaluated in a zero-gap electrolyser using plasma-sprayed Raney Ni electrodes 250 cm 2 in size. During a test period of 2800 h, an excellent electrochemical stability was reported. As a more generic comment, we believe our results to remain valid as well when using any other durable catalytic 3-D electrode in a flow-through bi-layer zero-gap cell configuration, either obtained by applying a catalytic coating on a pure Ni foam, or by using a catalytically more active Ni-alloy electrode itself (like Ni-Fe or Ni-Cu). Although an optimal selection of the ultimate and most durable electrocatalyst (coating) is not the core topic of our manuscript, we can point to refs. 51 , 52 , 53 as a source of inspiration for any other future work.

Perspectives and strategies for future alkaline water electrolyser developments

The performance improvement for alkaline water electrolysis demonstrated in this work, with cell voltages less than 2 V at 2 A·cm −2 , has a number of important positive consequences for future electrolyser developments. First of all, it allows for a significant process intensification, meaning less alkaline cells are needed for a given hydrogen output. With a significantly increased power density of 2 V·2 A·cm −2  = 4 W·cm −2 , a 1 MW electrolyser would now only require 25 m 2 of electrode surface, which is 5 to 10 times less than alkaline water electrolysers producing 0.2–0.4 A·cm −2 at 2 V. Moreover, with today’s alkaline electrodes being circular with a diameter on the order of 1 m, they typically require 160–320 cells for a 1 MW stack. Given that the more compact design allowed by our flow-engineered 3-D electrodes also allows for an easier pressurisation and sealing, and hence the use of square electrodes (as is the case for PEM), a 1 MW alkaline stack would now only require 25 cells consisting of 1 × 1 m 2 squared electrodes.

Obviously, the associated need to pump electrolyte at relatively high upstream velocities uniformly over the entire electrode surface within each cell comes at the expense of an additional energy consumption. In this respect, we had confirmation from a major industrial player that similarly high cell-level flow rates (in l·min −1 ) are already being used in their commercial 2-stack 1 MW alkaline system. Moreover, as to the additional energy consumption required for pumping the electrolyte at such high flow rates, estimations based on confidential data from the same industrial player indicated that this would only add about 1.6 kWh·kg −1 per stack. This is less than 2% of the 47.9 kWh·kg −1 electrical energy consumed by the stack for H 2 production. However, there still remains a design challenge to assure that during scale-up these similar flow rates also result in similarly high and uniform superficial upstream flow velocities (in m·s −1 ) over the entire electrode surface. In this respect, the conversion from circular to square electrodes (as is already the case for PEM) might open up new electrolyte flow design pathways in alkaline water electrolyser cells as well.

One may then also question the impact of such an increased electrolyte flow rate on the shunt current when implementing our 3-D bi-layer electrodes in an industrial-scale electrolyser stack. It represents the ionic current that passes through the electrolyte manifold, hence by-passing the cells in parallel. A most recent and comprehensive analysis of shunt currents has been reported by Sakas et al. 54 , who considered an industrial 2-stack 3 MW alkaline system, each stack containing 163 cells operating in series at 1.9 V and 0.23 A·cm −2 in 25 wt.% KOH at 70 °C, with an electrolyte flow rate of 985 l·min −1 (i.e. 985/163 = 6.1 l·min −1 per cell). The same study also included a detailed quantitative sensitivity analysis to various process conditions, like electrolyte flow rate and supplied current. With respect to the effect of increasing electrolyte flow, this was shown to result in a significant increase in shunt current, in line with earlier literature reports 55 , 56 . Extrapolating the data from ref. 54 for their reference flow rate to the one that would be needed to align with the highest flow velocity of 0.22 m·s −1 that we used at the cell level, this would result to a 2.2-fold increase in shunt current. On the other hand, the shunt current can also be expected to decrease with the total current supply 55 . In the case of our flow-engineered 3-D electrodes, the higher electrolyte flow rate was shown to allow for a much higher current density as compared to the reference case used in ref.  54 : 1.70 A·cm −2 at 1.9 V, instead of 0.23 A·cm −2 . So if the number of cells, the cell area and the cell voltage are held constant, a 7.4-fold increase in total current can be imposed. Extrapolating the data from ref. 54 , one then obtains a 1.8-fold reduction in shunt current. This 1.8-fold reduction in shunt current resulting from the increased total current supply is of the same order as the 2.2-fold increase in shunt current estimated from the higher electrolyte flow rate. Even more interestingly, for the same flow-induced increase in current density, one could also choose to keep the total current constant and rather decrease the total electrode area by decreasing the number of cells. In that case, the net effect can be expected to be an even more significant decrease in shunt current as a result of a decrease in its pathlength 55 . Since this effect is known to scale with the square root of the number of cells 57 , it leads to a (7.4) ½  = 2.7-fold reduction in shunt current, thereby fully compensating for the projected 2.2- fold increase resulting from the higher flow rate.

The technical details included in the above-cited work of Sakas for an industrial-scale alkaline water electrolyser also allow us to come back to the issue of increased electrolyte flow. We already stated above that in our own lab-scale flow-through cell, a much higher current density was obtained: instead of 0.23 A·cm −2 at 1.9 V as cited in ref. 54 , we arrived at 1.5 A·cm −2 and 1.7 A·cm −2 at our lowest and highest upstream electrolyte velocity of 5 and 22 cm·s −1 , respectively (cfr. Figure  4 ). So if we now decide to keep, besides the cell voltage and the total current, also the number of cells constant (rather than the cell area), a decrease in cell area of a factor 1.5/0.23 = 6.5 to 1.7/0.23 = 7.4 can be realised. Since the cells referenced in ref. 54 have a diameter of 1.6 (hence a cell area of 2.0 m 2 ), the use of our bi-layer electrodes would allow to reduce the cell area down to 2.0/6.5 = 0.31 m 2 or 2.0/7.4 = 0.27 m 2 at our lowest and highest upstream electrolyte velocity, respectively. In the case of square electrodes, this then corresponds to respectively 55*55 cm 2 and 52*52 cm 2 . If we assume a cell thickness of 0.56 cm (as in our own cell in order to perfectly fit the bi-layer foams), the industrially imposed electrolyte flow rate of 6.1 l·min −1 per cell would then correspond to 6100/(60*55*0.56) = 3.3 cm·s −1 , not so far from the lowest velocity of 5 cm·s −1 that we used in our own flow-through cell. To increase this upstream flow velocity even further in the industrial system, one could also consider the use of rectangular electrodes that are larger in height than in width. For instance, to arrive at the same upstream value of 5 cm·s −1 , this would only require a 36*84 cm 2 rectangular electrode, without the need to increase the cell-level electrolyte flow rate of 985/163 = 6.1 l·min −1 that is already being used in industry today. As a matter of fact, the use of similarly sized 0.30 m 2 square or rectangular electrodes in flow-through type cells is already quite common in other electrochemical engineering applications, as extensively reviewed in refs. 58 , 59 .

Finally, from an economical point of view, the contribution of our Ni-based bi-layer foam electrodes to the estimated cost per kW of hydrogen produced is the lowest among any other electrolyser technology. This is illustrated in Supplementary Fig.  S12 of the SI, where polarisation curves for different water electrolysis technologies expressing cell voltage as a function of current density (in A·cm −2 ) have been re-considered by dividing the latter by the electrode cost (in €·m −2 ). This then allows to express the cell voltage as a function of current per € invested. On such plots, PEM electrolysers clearly come out to be the worst as a result of the use of expensive and scarce catalyst like Pt and Ir, resulting in an electrode cost up to 15,000 €·m −2 . Interestingly, both our pure Ni and Raney Ni foam-based bi-layers, with electrode costs estimated at about 400 and 1000 €·m −2 , respectively, economically outperform any other electrolyser technology. Their low electrode cost is not only related to the use of Ni as electrode material, but also to the high porosity (>90%) and associated decrease in electrode material mass that is needed when implemented as macro-porous 3-D foams.

In conclusion, we have shown that for next generation high rate alkaline water electrolysers, minimising Ohmic losses through efficient gas bubble evacuation away from the active electrode can become as important as minimising activation losses by improving the electrocatalytic performance of the electrode itself. In particular, by a combined experimental and computational fluid dynamics (CFD) modelling approach, we demonstrated that integrating flow-engineered 3-D Ni-based bi-layer foam electrodes into a laterally-graded zero-gap cell configuration allows the electrochemical performance of alkaline water electrolysis to become PEM-like (2 A·cm −2 at <2 V cell voltage), even when keeping a state-of-the-art Zirfon diaphragm. Under uniform high upstream electrolyte flow conditions in the range 5–22 cm·s −1 , such a graded structure was shown to induce a high lateral velocity component in the direction normal to and away from the diaphragm. As a result, gas bubbles, once formed on the electrode surface, are evacuated much more efficiently, so that the electrode surface can maintain its high electrochemical activity even at high current densities. Such a performance improvement allows for a significant process intensification: a 1 MW stack would now only require 25 m 2 of electrode surface, which is 5–10 times less than what is needed in current alkaline water electrolysers. Moreover, the contribution of our Ni-based flow-engineered 3-D electrodes to the estimated cost per kW of hydrogen produced is the lowest among any other electrolyser technology. The PEM-like performance demonstrated in this work is therefore an invitation to start considering PEM-like cell designs for alkaline water electrolysers as well, in particular the use of squared or rectangular electrodes in flow-through type electrochemical cells.

Electrode preparation

The 3-D electrodes were prepared by cutting 2 × 2 cm 2 of a pure Ni foam (from Alantum) with a characteristic pore size of 450 µm and 3000 µm, having a thickness of 1.6 mm and 4 mm, respectively. Their electrochemically active surface area (ECSA) was already determined in a previous publication as 42.4 ± 0.5 cm 2 ·cm −3 and 9.9 ± 0.1 cm 2 ·cm −3 43 , leading to a total active surface area in our cell of 27.1 and 15.8 cm 2 for the 450 µm and 3000 µm foam, respectively. The fine foam was then coated using the same thermal process already reported elsewhere 60 . The initial coating composition before activation was (Ni-57, Al-43) and (Ni-41, Al-41, Mo-18) for anode and cathode respectively, Mo being added to the cathode to improve the kinetics of the hydrogen evolution reaction 61 , 62 . Activation was carried out by separately immerging the electrodes into a solution of 30 wt% KOH (>85%, VWR chemicals) and 10 wt% potassium-sodium-tartrate-tetrahydrate (>99%, Carl Roth GmbH) at 80 °C during 24 h. During this process, Al and Mo are leached out (see Supplementary Table  S2 ) and what remains is a micro-porous Ni skeleton with a high surface area. More details on the kinetics of the activation process can be found in Supplementary Fig.  S13 . ECSA measurements on our Raney Ni coated and activated 450 µm foam, according to the same procedure already described in ref. 43 , resulted in a value of 9000 ± 200 cm 2 ·cm −3 . Since this is more than 200 times higher than for the 450 µm pure Ni foam, the contribution of the 3000 µm pure Ni foam to the total ECSA of our Raney Ni bi-layer almost completely vanishes, being a mere 0.3%.

Electrochemical testing

The electrolyte was prepared by dissolving 30 wt% of potassium hydroxide pellets (>85%, VWR chemicals) in deionized water (18.2 MΩ·cm, Sartorius Arium 611), and always used within a few hours after preparation. The cell was prepared by stacking the following components: two end plates with fittings for the inlet and outlet of the anolyte and catholyte, respectively; two flat 2 mm thick pure Ni end-plates (>99.5%, Alfa Aesar) without any profiling or structuring as current collectors; two 4 mm thick polytetrafluoroethylene electrode housings; and a 500 μm thick Zirfon Perl UTP 500 diaphragm (from Agfa). An ethylene propylene diene monomer layer was inserted between each of these parts, and the cell was pressed using six screws with a torque of 3 N·m each to ensure sealing. Two gear pumps (GJ-N25.FF3S.A, Micropump) were used to control the catholyte and anolyte flow independently, the flow itself being monitored by flow transmitters (Honsberg, model LABO-MID1-I/U/F/C). In our setup, the electrolyte was heated at the reservoir level (made of 316 l stainless steel). As a consequence, a minimum level of electrolyte flow is needed to maintain the temperature in the cell at 70 °C. The minimum and maximum electrolyte flow rate was 0.35 l·min −1 and 1.6 l·min −1 , respectively, corresponding in our flow-through zero-gap cell to an upstream electrolyte velocity of 0.05 m·s −1 and 0.22 m·s −1 , respectively. The electrochemical measurement protocol started with three current scans from 0 to 8 A (0 to 2 A·cm −2 ) at 0.1 A·s −1 , using a Solartron Modulab XM potentiostat equipped with a 20 A booster. These were followed by 5 min of galvanostatic testing at 0.5, 1.0, 1.5, and 2.0 A·cm −2 , respectively, and then by three final consecutive current scans from 0 to 8 A (0 to 2 A·cm −2 ). There can be a difference between the measured performance during current scans and galvanostatic measurements, as the first is transient and the second steady 63 . Consequently, we verified that both curves superimposed, justifying the use of the data from current scans. More details about the experimental procedure are discussed in Supplementary Fig.  S14 of the supplementary information. Galvanostatic electrochemical impedance spectroscopy (EIS) measurements were performed as well in order to have an independent determination of the high frequency Ohmic resistance. The frequency range was set between 10,000 and 0.1 Hz with 3 points per decade for current densities up to 500 mA·cm −2 , and between 2500 and 0.1 Hz with 3 points per decade for current densities above 500 mA·cm −2 . The AC amplitude was 10% of the DC.

Electrode structural characterization

Both foam electrode samples (450 μm and 3000 μm) were imaged with X-ray micro-computed tomography (µCT), using a Phoenix Nanotom M (GE Measurement and Control Solutions, Germany) equipped with a 180 kV/15 W energy nanofocus X-ray tube and a diamond-coated tungsten target. The acquisition and reconstruction parameters are listed in Supplementary Table  S3 . All µCT datasets were reconstructed with the Datos|x software (GE Measurement and Control Solutions, Germany) and exported as XY slices (.tiff). Besides micro-computed tomography, scanning electron micrographs of the foams before and after coating were taken as well.

Computational fluid dynamics (CFD) simulations

After X-ray micro-computed tomography scanning, an initial surface model of our foam samples was then created through the scanned images. The resulting triangulated surface is not suited for numerical simulations, as it has geometrical inconsistencies as well as having a highly detailed resolution, due to the topological complexity of the model and the inherent difficulties arising in the treatment of arbitrary 3-D surfaces. As further detailed in Supplementary Fig.  S5 of the SI, surface reconstruction is performed with a variation of the alpha shape algorithm 64 on the tetrahedral mesh obtained by the point cloud of the initial surface, using custom-made software based on Gmsh 65 . The final volume mesh is created with the snappyHexMesh utility of OpenFOAM. Due to restrictions on the computational size (especially for the fine 450 μm foam), we choose to mesh a representative domain of the electrode with a size of 6 × 6 × 5.6 mm 3 . The thickness of the gas production and porous transport foam was kept at 1.6 mm and 4 mm, respectively. In a first stage, a single-phase model was used to simulate the electrolyte flow within the bi-layer electrodes, using the pimpleFoam solver to solve the incompressible Navier-Stokes equations within the computational domain, based on the Finite Volume Method implemented in OpenFOAM 66 . Then, in order to simulate 2-phase flow, a mixture model formulation was used and implemented on the driftFluxFoam solver available in OpenFOAM. This solver solves the continuity and momentum equations for the gas-liquid mixture by weight-averaging velocity, density and viscosity as a function of the gas fraction α and the properties of each phase. In the simulations, a stagnant gas mixture inlet (considering different values of α) was imposed at the interface of the catalytic foam and the diaphragm, with zero drift velocity. In both single and 2-phase simulations, the upstream inlet velocity was fixed to 0.22 m·s −1 , and the kinematic viscosity was set at 8.28·10 −7 m 2 ·s −1 for 30 wt.% KOH at 70 °C 67 . For the 2-phase simulations, the density of hydrogen was set to 0.08 kg·m −3 and the dynamic viscosity to 0.88·10 −5  Pa·s.

Polarisation curve fitting

In Eq. ( 1 ) used to fit all polarisation curves, the reversible cell voltage E eq was estimated as 68 :

with T the temperature in Kelvin, R the universal gas constant (8.3145 J·K −1 ·mol −1 ), F the Faraday constant (96,485 s·A·mol −1 ), P the total pressure in bar (assumed to be equal to the atmospheric pressure, 1.01325 bar), P w the vapour pressure of the electrolyte in bar, \({P}_{w}^{*}\) the vapour pressure of pure water in bar, and z is the number of electrons exchanged during the reaction (=2). The partial and vapour pressures under standard conditions \({P}_{{H}_{2}}^{\circ }\) , \({P}_{{O}_{2}}^{\circ }\) , \({P}_{w}^{ {*} {\circ} }\) and \({P}_{w}^{\circ }\) are equal to 1 bar. The standard equilibrium cell voltage was estimated as a function of temperature by the following expression 68 :

with the temperature in Kelvin. The vapour pressures of water were estimated using the following expressions 68 :

with m the molality in mol·kg −1 .

The Zirfon resistivity was estimated as 25 :

where \({\rho }_{s}\) is the electrolyte resistivity in Ω·cm and N m the dimensionless MacMullin number set at 2.82 as proposed in ref. 25 . The electrolyte resistivity was estimated according to 69 :

with M the molarity in mol·l −1 .

Finally, the area resistance of the Zirfon diaphragm R Zirfon , expressed in Ω·cm 2 , can be obtained by multiplying the Zirfon Ohmic resistivity by the thickness of the diaphragm (i.e. 0.05 cm).

Data availability

The data that support the findings of this study are available at http://hdl.handle.net/2078.1/285920 . Source data are provided with this paper.

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Acknowledgements

We gratefully acknowledge financial support from the European Commission through the H2020 Project “NexTAEC” (Materials for Next Generation Alkaline Electrolysers), under contract n°862509. X.P. and J.P. acknowledge additional financial support from the Walloon Region Win4Excellence projet “TiNTHyN” under contract n°2310142, granted by SPW-Economie Emploi Recherche and supported by the Plan de Relance de la Wallonie. J.P. also cordially thanks the Japanese Society for the Promotion of Science (JSPS) for a short-term invitational fellowship for research in Japan. This work is dedicated in memory of ing. Marc Sinnaeve, who contributed invaluably to its experimental success through his unprecedented technical skills, rigour and devotion.

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Division of Materials and Process Engineering, Université catholique de Louvain, Louvain-la-Neuve, Belgium

Fernando Rocha, Christos Georgiadis, Kevin Van Droogenbroek, Renaud Delmelle, Xavier Pinon, Grzegorz Pyka, Greet Kerckhofs & Joris Proost

Institute of Engineering Thermodynamics, German Aerospace Center, Stuttgart, Germany

Franz Egert, Fatemeh Razmjooei & Syed-Asif Ansar

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Franz Egert

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Shigenori Mitsushima

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F.R., R.D. and J.P. conceived the idea and designed the experiments. F.R., R.D. and X.P. built the electrochemical systems and performed the electrochemical experiments. S.M. assisted in the design and interpretation of the EIS measurements. C.G. and K.V. developed and performed the CFD simulations. G.P. performed the X-ray micro-computed tomography, and contributed with G.K. to its data analysis and interpretation. F.E. and F.R. developed and performed the Raney Ni coating process, and contributed with S.A. to its evaluation. F.R., R.D., C.G., K.V. and J.P. performed data analysis, interpretation of the results, and preparation of the manuscript. All authors contributed to reviewing and editing of the manuscript.

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Rocha, F., Georgiadis, C., Van Droogenbroek, K. et al. Proton exchange membrane-like alkaline water electrolysis using flow-engineered three-dimensional electrodes. Nat Commun 15 , 7444 (2024). https://doi.org/10.1038/s41467-024-51704-z

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Calcworkshop

Experimental Design in Statistics w/ 11 Examples!

// Last Updated: September 20, 2020 - Watch Video //

A proper experimental design is a critical skill in statistics.

Jenn, Founder Calcworkshop ® , 15+ Years Experience (Licensed & Certified Teacher)

Without proper controls and safeguards, unintended consequences can ruin our study and lead to wrong conclusions.

So let’s dive in to see what’s this is all about!

What’s the difference between an observational study and an experimental study?

An observational study is one in which investigators merely measure variables of interest without influencing the subjects.

And an experiment is a study in which investigators administer some form of treatment on one or more groups?

In other words, an observation is hands-off, whereas an experiment is hands-on.

So what’s the purpose of an experiment?

To establish causation (i.e., cause and effect).

All this means is that we wish to determine the effect an independent explanatory variable has on a dependent response variable.

The explanatory variable explains a response, similar to a child falling and skins their knee and starting to cry. The child is crying in response to falling and skinning their knee. So the explanatory variable is the fall, and the response variable is crying.

Explanatory Vs Response Variable In Everyday Life

Let’s look at another example. Suppose a medical journal describes two studies in which subjects who had a seizure were randomly assigned to two different treatments:

• No treatment.
• A high dose of vitamin C.

The subjects were observed for a year, and the number of seizures for each subject was recorded. Identify the explanatory variable (independent variable), response variable (dependent variable), and include the experimental units.

The explanatory variable is whether the subject received either no treatment or a high dose of vitamin C. The response variable is whether the subject had a seizure during the time of the study. The experimental units in this study are the subjects who recently had a seizure.

Okay, so using the example above, notice that one of the groups did not receive treatment. This group is called a control group and acts as a baseline to see how a new treatment differs from those who don’t receive treatment. Typically, the control group is given something called a placebo, a substance designed to resemble medicine but does not contain an active drug component. A placebo is a dummy treatment, and should not have a physical effect on a person.

Before we talk about the characteristics of a well-designed experiment, we need to discuss some things to look out for:

• Confounding
• Lurking variables

Confounding happens when two explanatory variables are both associated with a response variable and also associated with each other, causing the investigator not to be able to identify their effects and the response variable separately.

A lurking variable is usually unobserved at the time of the study, which influences the association between the two variables of interest. In essence, a lurking variable is a third variable that is not measured in the study but may change the response variable.

For example, a study reported a relationship between smoking and health. A study of 1430 women were asked whether they smoked. Ten years later, a follow-up survey observed whether each woman was still alive or deceased. The researchers studied the possible link between whether a woman smoked and whether she survived the 10-year study period. They reported that:

• 21% of the smokers died
• 32% of the nonsmokers died

So, is smoking beneficial to your health, or is there something that could explain how this happened?

Older women are less likely to be smokers, and older women are more likely to die. Because age is a variable that influences the explanatory and response variable, it is considered a confounding variable.

But does smoking cause death?

Notice that the lurking variable, age, can also be a contributing factor. While there is a correlation between smoking and mortality, and also a correlation between smoking and age, we aren’t 100% sure that they are the cause of the mortality rate in women.

Lurking – Confounding – Correlation – Causation Diagram

Now, something important to point out is that a lurking variable is one that is not measured in the study that could influence the results. Using the example above, some other possible lurking variables are:

• Stress Level.

These variables were not measured in the study but could influence smoking habits as well as mortality rates.

What is important to note about the difference between confounding and lurking variables is that a confounding variable is measured in a study, while a lurking variable is not.

Additionally, correlation does not imply causation!

Alright, so now it’s time to talk about blinding: single-blind, double-blind experiments, as well as the placebo effect.

A single-blind experiment is when the subjects are unaware of which treatment they are receiving, but the investigator measuring the responses knows what treatments are going to which subject. In other words, the researcher knows which individual gets the placebo and which ones receive the experimental treatment. One major pitfall for this type of design is that the researcher may consciously or unconsciously influence the subject since they know who is receiving treatment and who isn’t.

A double-blind experiment is when both the subjects and investigator do not know who receives the placebo and who receives the treatment. A double-blind model is considered the best model for clinical trials as it eliminates the possibility of bias on the part of the researcher and the possibility of producing a placebo effect from the subject.

The placebo effect is when a subject has an effect or response to a fake treatment because they “believe” that the result should occur as noted by Yale . For example, a person struggling with insomnia takes a placebo (sugar pill) but instantly falls asleep because they believe they are receiving a sleep aid like Ambien or Lunesta.

Placebo Effect – Real Life Example

So, what are the three primary requirements for a well-designed experiment?

• Randomization

In a controlled experiment , the researchers, or investigators, decide which subjects are assigned to a control group and which subjects are assigned to a treatment group. In doing so, we ensure that the control and treatment groups are as similar as possible, and limit possible confounding influences such as lurking variables. A replicated experiment that is repeated on many different subjects helps reduce the chance of variation on the results. And randomization means we randomly assign subjects into control and treatment groups.

When subjects are divided into control groups and treatment groups randomly, we can use probability to predict the differences we expect to observe. If the differences between the two groups are higher than what we would expect to see naturally (by chance), we say that the results are statistically significant.

For example, if it is surmised that a new medicine reduces the effects of illness from 72 hours to 71 hours, this would not be considered statistically significant. The difference from 72 hours to 71 hours is not substantial enough to support that the observed effect was due to something other than normal random variation.

Now there are two major types of designs:

• Completely-Randomized Design (CRD)
• Block Design

A completely randomized design is the process of assigning subjects to control and treatment groups using probability, as seen in the flow diagram below.

Completely Randomized Design Example

A block design is a research method that places subjects into groups of similar experimental units or conditions, like age or gender, and then assign subjects to control and treatment groups using probability, as shown below.

Randomized Block Design Example

Additionally, a useful and particular case of a blocking strategy is something called a matched-pair design . This is when two variables are paired to control for lurking variables.

For example, imagine we want to study if walking daily improved blood pressure. If the blood pressure for five subjects is measured at the beginning of the study and then again after participating in a walking program for one month, then the observations would be considered dependent samples because the same five subjects are used in the before and after observations; thus, a matched-pair design.

Please note that our video lesson will not focus on quasi-experiments. A quasi experimental design lacks random assignments; therefore, the independent variable can be manipulated prior to measuring the dependent variable, which may lead to confounding. For the sake of our lesson, and all future lessons, we will be using research methods where random sampling and experimental designs are used.

Together we will learn how to identify explanatory variables (independent variable) and response variables (dependent variables), understand and define confounding and lurking variables, see the effects of single-blind and double-blind experiments, and design randomized and block experiments.

Experimental Designs – Lesson & Examples (Video)

1 hr 06 min

• Introduction to Video: Experiments
• 00:00:29 – Observational Study vs Experimental Study and Response and Explanatory Variables (Examples #1-4)
• Exclusive Content for Members Only
• 00:09:15 – Identify the response and explanatory variables and the experimental units and treatment (Examples #5-6)
• 00:14:47 – Introduction of lurking variables and confounding with ice cream and homicide example
• 00:18:57 – Lurking variables, Confounding, Placebo Effect, Single Blind and Double Blind Experiments (Example #7)
• 00:27:20 – What was the placebo effect and was the experiment single or double blind? (Example #8)
• 00:30:36 – Characteristics of a well designed and constructed experiment that is statistically significant
• 00:35:08 – Overview of Complete Randomized Design, Block Design and Matched Pair Design
• 00:44:23 – Design and experiment using complete randomized design or a block design (Examples #9-10)
• 00:56:09 – Identify the response and explanatory variables, experimental units, lurking variables, and design an experiment to test a new drug (Example #11)
• Practice Problems with Step-by-Step Solutions
• Chapter Tests with Video Solutions

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Effect of students’ deep learning in virtual venue environment: a meta-analysis based on 45 experiments and quasi-experiments at home and abroad

• Published: 26 August 2024

Cite this article

• Xinyi Wu 1 ,
• Xiaohui Chen 1 ,
• Xingyang Wang 1 , 3 &
• Hanxi Wang   ORCID: orcid.org/0000-0003-4130-6981 2  

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With the application of virtual venues in the field of education, numerous educational empirical studies have examined the impact of deep learning in the learning environment of virtual venues, but the conclusions are not always in agreement. The present study adopted the meta-analysis method and RStudio software to test the overall effect of 45 domestic and foreign experimental and quasi-experimental studies, and eight moderating variables (experiment period, knowledge type, virtual venue type, and feedback strategy) were analyzed. The research results indicated that moderating variables had different degrees of influence on the deep learning effect of students in the learning environment of virtual venues. There were no intra-group differences in the type of virtual venue and the experiment period, while there were intra-group differences in other moderating variables. According to the results of meta-analysis, suggestions were put forward from four aspects (course design of virtual venues, selection and application of feedback strategies, knowledge type design of virtual venues, and empirical research suggestions) to serve as references for strengthening the deep learning impact of students and the scientific design of course content of virtual venues.

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Acknowledgements

The authors thank the reviewers for their valuable comments, and the authors thank the editor for his efforts in this paper. This research was funded by the Project of Province-Ministry Co-constructing Teacher Education Collaborative Innovation Center of Northeast Normal University (No. CITE20200102), the Jilin Province Industrial Independent Innovation Ability Special Project (No. 2019C033), the 2022 Humanities and Social Science Research Planning Foundation of the Ministry of Education (No. 22YJA880007), and the High-level Talent Foundation Project of Harbin Normal University (No. 1305124219). The authors thank the reviewers for their valuable comments, and the authors thank the editor for his efforts in this paper.

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School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China

Xinyi Wu, Xiaohui Chen & Xingyang Wang

School of Geographical Sciences, Harbin Normal University, Harbin, 150025, China

College of Software, Jilin University, Changchun, 130015, China

Xingyang Wang

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Xinyi Wu drafted the initial manuscript, designed the study, analyzed data. Xiaohui Chen carried out the initial analyses, reviewed and revised the manuscript. Xingyang Wang analyzed data, revised the manuscript. Hanxi Wang conceptualized and designed the study, revised the manuscript. Xiaohui Chen and Hanxi Wang provided financial support. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

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Wu, X., Chen, X., Wang, X. et al. Effect of students’ deep learning in virtual venue environment: a meta-analysis based on 45 experiments and quasi-experiments at home and abroad. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12985-5

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8.1 Experimental design: What is it and when should it be used?

Learning objectives.

• Define experiment
• Identify the core features of true experimental designs
• Describe the difference between an experimental group and a control group
• Identify and describe the various types of true experimental designs

Experiments are an excellent data collection strategy for social workers wishing to observe the effects of a clinical intervention or social welfare program. Understanding what experiments are and how they are conducted is useful for all social scientists, whether they actually plan to use this methodology or simply aim to understand findings from experimental studies. An experiment is a method of data collection designed to test hypotheses under controlled conditions. In social scientific research, the term experiment has a precise meaning and should not be used to describe all research methodologies.

Experiments have a long and important history in social science. Behaviorists such as John Watson, B. F. Skinner, Ivan Pavlov, and Albert Bandura used experimental design to demonstrate the various types of conditioning. Using strictly controlled environments, behaviorists were able to isolate a single stimulus as the cause of measurable differences in behavior or physiological responses. The foundations of social learning theory and behavior modification are found in experimental research projects. Moreover, behaviorist experiments brought psychology and social science away from the abstract world of Freudian analysis and towards empirical inquiry, grounded in real-world observations and objectively-defined variables. Experiments are used at all levels of social work inquiry, including agency-based experiments that test therapeutic interventions and policy experiments that test new programs.

Several kinds of experimental designs exist. In general, designs considered to be true experiments contain three basic key features:

• random assignment of participants into experimental and control groups
• a “treatment” (or intervention) provided to the experimental group
• measurement of the effects of the treatment in a post-test administered to both groups

Some true experiments are more complex.  Their designs can also include a pre-test and can have more than two groups, but these are the minimum requirements for a design to be a true experiment.

Experimental and control groups

In a true experiment, the effect of an intervention is tested by comparing two groups: one that is exposed to the intervention (the experimental group , also known as the treatment group) and another that does not receive the intervention (the control group ). Importantly, participants in a true experiment need to be randomly assigned to either the control or experimental groups. Random assignment uses a random number generator or some other random process to assign people into experimental and control groups. Random assignment is important in experimental research because it helps to ensure that the experimental group and control group are comparable and that any differences between the experimental and control groups are due to random chance. We will address more of the logic behind random assignment in the next section.

Treatment or intervention

In an experiment, the independent variable is receiving the intervention being tested—for example, a therapeutic technique, prevention program, or access to some service or support. It is less common in of social work research, but social science research may also have a stimulus, rather than an intervention as the independent variable. For example, an electric shock or a reading about death might be used as a stimulus to provoke a response.

In some cases, it may be immoral to withhold treatment completely from a control group within an experiment. If you recruited two groups of people with severe addiction and only provided treatment to one group, the other group would likely suffer. For these cases, researchers use a control group that receives “treatment as usual.” Experimenters must clearly define what treatment as usual means. For example, a standard treatment in substance abuse recovery is attending Alcoholics Anonymous or Narcotics Anonymous meetings. A substance abuse researcher conducting an experiment may use twelve-step programs in their control group and use their experimental intervention in the experimental group. The results would show whether the experimental intervention worked better than normal treatment, which is useful information.

The dependent variable is usually the intended effect the researcher wants the intervention to have. If the researcher is testing a new therapy for individuals with binge eating disorder, their dependent variable may be the number of binge eating episodes a participant reports. The researcher likely expects her intervention to decrease the number of binge eating episodes reported by participants. Thus, she must, at a minimum, measure the number of episodes that occur after the intervention, which is the post-test .  In a classic experimental design, participants are also given a pretest to measure the dependent variable before the experimental treatment begins.

Types of experimental design

Let’s put these concepts in chronological order so we can better understand how an experiment runs from start to finish. Once you’ve collected your sample, you’ll need to randomly assign your participants to the experimental group and control group. In a common type of experimental design, you will then give both groups your pretest, which measures your dependent variable, to see what your participants are like before you start your intervention. Next, you will provide your intervention, or independent variable, to your experimental group, but not to your control group. Many interventions last a few weeks or months to complete, particularly therapeutic treatments. Finally, you will administer your post-test to both groups to observe any changes in your dependent variable. What we’ve just described is known as the classical experimental design and is the simplest type of true experimental design. All of the designs we review in this section are variations on this approach. Figure 8.1 visually represents these steps.

An interesting example of experimental research can be found in Shannon K. McCoy and Brenda Major’s (2003) study of people’s perceptions of prejudice. In one portion of this multifaceted study, all participants were given a pretest to assess their levels of depression. No significant differences in depression were found between the experimental and control groups during the pretest. Participants in the experimental group were then asked to read an article suggesting that prejudice against their own racial group is severe and pervasive, while participants in the control group were asked to read an article suggesting that prejudice against a racial group other than their own is severe and pervasive. Clearly, these were not meant to be interventions or treatments to help depression, but were stimuli designed to elicit changes in people’s depression levels. Upon measuring depression scores during the post-test period, the researchers discovered that those who had received the experimental stimulus (the article citing prejudice against their same racial group) reported greater depression than those in the control group. This is just one of many examples of social scientific experimental research.

In addition to classic experimental design, there are two other ways of designing experiments that are considered to fall within the purview of “true” experiments (Babbie, 2010; Campbell & Stanley, 1963).  The posttest-only control group design is almost the same as classic experimental design, except it does not use a pretest. Researchers who use posttest-only designs want to eliminate testing effects , in which participants’ scores on a measure change because they have already been exposed to it. If you took multiple SAT or ACT practice exams before you took the real one you sent to colleges, you’ve taken advantage of testing effects to get a better score. Considering the previous example on racism and depression, participants who are given a pretest about depression before being exposed to the stimulus would likely assume that the intervention is designed to address depression. That knowledge could cause them to answer differently on the post-test than they otherwise would. In theory, as long as the control and experimental groups have been determined randomly and are therefore comparable, no pretest is needed. However, most researchers prefer to use pretests in case randomization did not result in equivalent groups and to help assess change over time within both the experimental and control groups.

Researchers wishing to account for testing effects but also gather pretest data can use a Solomon four-group design. In the Solomon four-group design , the researcher uses four groups. Two groups are treated as they would be in a classic experiment—pretest, experimental group intervention, and post-test. The other two groups do not receive the pretest, though one receives the intervention. All groups are given the post-test. Table 8.1 illustrates the features of each of the four groups in the Solomon four-group design. By having one set of experimental and control groups that complete the pretest (Groups 1 and 2) and another set that does not complete the pretest (Groups 3 and 4), researchers using the Solomon four-group design can account for testing effects in their analysis.

Solomon four-group designs are challenging to implement in the real world because they are time- and resource-intensive. Researchers must recruit enough participants to create four groups and implement interventions in two of them.

Overall, true experimental designs are sometimes difficult to implement in a real-world practice environment. It may be impossible to withhold treatment from a control group or randomly assign participants in a study. In these cases, pre-experimental and quasi-experimental designs–which we  will discuss in the next section–can be used.  However, the differences in rigor from true experimental designs leave their conclusions more open to critique.

Experimental design in macro-level research

You can imagine that social work researchers may be limited in their ability to use random assignment when examining the effects of governmental policy on individuals.  For example, it is unlikely that a researcher could randomly assign some states to implement decriminalization of recreational marijuana and some states not to in order to assess the effects of the policy change.  There are, however, important examples of policy experiments that use random assignment, including the Oregon Medicaid experiment. In the Oregon Medicaid experiment, the wait list for Oregon was so long, state officials conducted a lottery to see who from the wait list would receive Medicaid (Baicker et al., 2013).  Researchers used the lottery as a natural experiment that included random assignment. People selected to be a part of Medicaid were the experimental group and those on the wait list were in the control group. There are some practical complications macro-level experiments, just as with other experiments.  For example, the ethical concern with using people on a wait list as a control group exists in macro-level research just as it does in micro-level research.

Key Takeaways

• True experimental designs require random assignment.
• Control groups do not receive an intervention, and experimental groups receive an intervention.
• The basic components of a true experiment include a pretest, posttest, control group, and experimental group.
• Testing effects may cause researchers to use variations on the classic experimental design.
• Classic experimental design- uses random assignment, an experimental and control group, as well as pre- and posttesting
• Control group- the group in an experiment that does not receive the intervention
• Experiment- a method of data collection designed to test hypotheses under controlled conditions
• Experimental group- the group in an experiment that receives the intervention
• Posttest- a measurement taken after the intervention
• Posttest-only control group design- a type of experimental design that uses random assignment, and an experimental and control group, but does not use a pretest
• Pretest- a measurement taken prior to the intervention
• Random assignment-using a random process to assign people into experimental and control groups
• Solomon four-group design- uses random assignment, two experimental and two control groups, pretests for half of the groups, and posttests for all
• Testing effects- when a participant’s scores on a measure change because they have already been exposed to it
• True experiments- a group of experimental designs that contain independent and dependent variables, pretesting and post testing, and experimental and control groups

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Foundations of Social Work Research Copyright © 2020 by Rebecca L. Mauldin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Selective phenolics recovery from aqueous residues of pyrolysis oil through computationally designed green solvent.

1. Introduction

2. literature review on camd, 3. materials and methods, 3.1. materials, 3.2. pyrolysis set-up, 3.3. analytical techniques, 3.4. computer-aided molecular design, 3.4.1. problem specification and design objectives, 3.4.2. solvent screening, 3.5. simulation environment and economic analysis, 4. results and discussion, 4.1. pyrolysis yields, 4.2. gc–ms analysis of pyrolysis oil, 4.3. results of camd.

• Solvent A: Extraction of Quinones
• Solvent B : Extraction of the Undesired compounds from quinones

4.4. Experimental Validation of the Methodology

4.5. process optimization and economic analysis, 5. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Qaisar, A.; Bartolucci, L.; Cancelliere, R.; Chemmangattuvalappil, N.G.; Mele, P.; Micheli, L.; Paialunga, E. Selective Phenolics Recovery from Aqueous Residues of Pyrolysis Oil through Computationally Designed Green Solvent. Sustainability 2024 , 16 , 7497. https://doi.org/10.3390/su16177497

Qaisar A, Bartolucci L, Cancelliere R, Chemmangattuvalappil NG, Mele P, Micheli L, Paialunga E. Selective Phenolics Recovery from Aqueous Residues of Pyrolysis Oil through Computationally Designed Green Solvent. Sustainability . 2024; 16(17):7497. https://doi.org/10.3390/su16177497

Qaisar, Amna, Lorenzo Bartolucci, Rocco Cancelliere, Nishanth G. Chemmangattuvalappil, Pietro Mele, Laura Micheli, and Elisa Paialunga. 2024. "Selective Phenolics Recovery from Aqueous Residues of Pyrolysis Oil through Computationally Designed Green Solvent" Sustainability 16, no. 17: 7497. https://doi.org/10.3390/su16177497

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