an experiment data

• Skip to secondary menu
• Skip to main content
• Skip to primary sidebar

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.

Share this:

Reader Interactions

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?

Comments and Questions Cancel reply

01 Career Opportunities

02 beginner, 03 advanced, 04 training programs, experimental design in data science - design flow, principles & examples, data science with python certification course, introduction, what is experimental design, experimental design flow, principles of experimental design, understanding the experimental design flow, examples of experimental design in data science, experimental design software and tools, challenges in experimental design and how to overcome them, best practices for conducting experimental design, live classes schedule.

About Author

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

Get access to all the courses and over 450 HD videos with your subscription

Monthly and Yearly Plans Available

Get My Subscription Now

Still wondering if CalcWorkshop is right for you? Take a Tour and find out how a membership can take the struggle out of learning math.

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

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

Image attributions

exam scientific experiment by mohamed_hassan CC-0

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.

Share This Book

Introduction to Data Science I & II

Observational versus experimental studies, observational versus experimental studies #.

In most research questions or investigations, we are interested in finding an association that is causal (the first scenario in the previous section ). For example, “Is the COVID-19 vaccine effective?” is a causal question. The researcher is looking for an association between receiving the COVID-19 vaccine and contracting (symptomatic) COVID-19, but more specifically wants to show that the vaccine causes a reduction in COVID-19 infections (Baden et al., 2020) 1 .

Experimental Studies #

There are 3 necessary conditions for showing that a variable X (for example, vaccine) causes an outcome Y (such as not catching COVID-19):

Temporal Precedence : We must show that X (the cause) happened before Y (the effect).

Non-spuriousness : We must show that the effect Y was not seen by chance.

No alternate cause : We must show that no other variable accounts for the relationship between X and Y .

If any of the three is not present, the association cannot be causal. If the proposed cause did not happen before the effect, it cannot have caused the effect. In addition, if the effect was seen by chance and cannot be replicated, the association is spurious and therefore not causal. Lastly, if there is another phenomenon that accounts for the association seen, then it cannot be a causal association. These conditions are therefore, necessary to show causality.

The best way to show all three necessary conditions is by conducting an experiment . Experiments involve controllable factors which are measured and determined by the experimenter, uncontrollable factors which are measured but not determined by the experimentor, and experimental variability or noise which is unmeasured and uncontrolled. Controllable factors that the experimenter manipulates in his or her experiment are known as independent variables . In our vaccination example, the independent variable is receipt of vaccine. Uncontrollable factors that are hypothesized to depend on the independent variable are known as dependent variables. The dependent variable in the vaccination example is contraction of COVID-19. The experimentor cannot control whether participants catch the disease, but can measure it, and it is hypothesized that catching the disease is dependent on vaccination status.

Control Groups #

When conducting an experiment, it is important to have a comparison or control group . The control group is used to better understand the effect of the independent variable. For example, if all patients are given the vaccine, it would be impossible to measure whether the vaccine is effective as we would not know the outcome if patients had not received the vaccine. In order to measure the effect of the vaccine, the researcher must compare patients who did not receive the vaccine to patients that did receive the vaccine. This comparison group of patients who did not receive the vaccine is the control group for the experiment. The control group allows the researcher to view an effect or association. When scientists say that the COVID-19 vaccine is 94% effective, this does not mean that only 6% of people who got the vaccine in their study caught COVID-19 (the number is actually much lower!). That would not take into account the rate of catching COVID-19 for those without a vaccine. Rather, 94% effective refers to having 94% lower incidence of infection compared to the control group.

Let’s illustrate this using data from the efficacy trial by Baden and colleagues in 2020. In their primary analysis, 14,073 participants were in the placebo group and 14,134 in the vaccine group. Of these participants, a total of 196 were diagnosed with COVID-19 during the 78 day follow-up period: 11 in the vaccine group and 186 in the placebo group. This means, 0.08% of those in the vaccine group and 1.32% of those in the placebo group were diagnosed with COVID-19. Dividing 0.08 by 1.32, we see that the proportion of cases in the vaccine group was only 6% of the proportion of cases in the placebo group. Therefore, the vaccine is 94% effective.

Chicago has a population of almost 3,000,000. Extrapolating using the numbers from above, without the vaccine, 39,600 people would be expected to catch COVID-19 in the period between 14 and 92 days after their second vaccine. If everyone were vaccinated, the expected number would drop to 2,400. This is a large reduction! However, it is important that the researcher shows this effect is non-spurious and therefore important and significant. One way to do this is through replication : applying a treatment independently across two or more experimental subjects. In our example, researchers conducted many similar experiments for multiple groups of patients to show that the effect can be seen reliably.

Randomization #

A researcher must also be able to show there is no alternate cause for the association in order to prove causality. This can be done through randomization : random assignment of treatment to experimental subjects. Consider a group of patients where all male patients are given the treatment and all female patients are in the control group. If an association is found, it would be unclear whether this association is due to the treatment or the fact that the groups were of differing sex. By randomizing experimental subjects to groups, researchers ensure there is no systematic difference between groups other than the treatment and therefore no alternate cause for the relationship between treatment and outcome.

Another way of ensuring there is no alternate cause is by blocking : grouping similar experimental units together and assigning different treatments within such groups. Blocking is a way of dealing with sources of variability that are not of primary interest to the experimenter. For example, a researcher may block on sex by grouping males together and females together and assigning treatments and controls within the different groups. Best practices are to block the largest and most salient sources of variability and randomize what is difficult or impossible to block. In our example blocking would account for variability introduced by sex whereas randomization would account for factors of variability such as age or medical history which are more difficult to block.

Observational Studies #

Randomized experiments are considered the “Gold Standard” for showing a causal relationship. However, it is not always ethical or feasible to conduct a randomized experiment. Consider the following research question: Does living in Northern Chicago increase life expectancy? It would be infeasible to conduct an experiment which randomly allocates people to live in different parts of the city. Therefore, we must turn to observational data to test this question. Where experiments involve one or more variables controlled by the experimentor (dose of a drug for example), in observational studies there is no effort or intention to manipulate or control the object of study. Rather, researchers collect data without interfering with the subjects. For example, researchers may conduct a survey gathering both health and neighborhood data, or they may have access to administrative data from a local hospital. In these cases, the researchers are merely observing variables and outcomes.

There are two types of observational studies: retrospective studies and prospective studies. In a retrospective study , data is collected after events have taken place. This may be through surveys, historical data, or administrative records. An example of a retrospective study would be using administrative data from a hospital to study incidence of disease. In contrast, a prospective study identifies subjects beforehand and collects data as events unfold. For example, one might use a prospective study to evaluate how personality traits develop in children, by following a predetermined set of children through elementary school and giving them personality assessments each year.

Baden LR, El Sahly HM, Essink B, Kotloff K, Frey S, Novak R, Diemert D, Spector SA, Rouphael N, Creech CB, McGettigan J. Efficacy and safety of the mRNA-1273 SARS-CoV-2 vaccine. New England journal of medicine. 2020 Dec 30.

What Are The Steps Of The Scientific Method?

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

Learn about our Editorial Process

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.

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:

Science is not just knowledge. It is also a method for obtaining knowledge. Scientific understanding is organized into theories.

The scientific method is a step-by-step process used by researchers and scientists to determine if there is a relationship between two or more variables. Psychologists use this method to conduct psychological research, gather data, process information, and describe behaviors.

It involves careful observation, asking questions, formulating hypotheses, experimental testing, and refining hypotheses based on experimental findings.

How it is Used

The scientific method can be applied broadly in science across many different fields, such as chemistry, physics, geology, and psychology. In a typical application of this process, a researcher will develop a hypothesis, test this hypothesis, and then modify the hypothesis based on the outcomes of the experiment.

The process is then repeated with the modified hypothesis until the results align with the observed phenomena. Detailed steps of the scientific method are described below.

Keep in mind that the scientific method does not have to follow this fixed sequence of steps; rather, these steps represent a set of general principles or guidelines.

7 Steps of the Scientific Method

Psychology uses an empirical approach.

Empiricism (founded by John Locke) states that the only source of knowledge comes through our senses – e.g., sight, hearing, touch, etc.

Empirical evidence does not rely on argument or belief. Thus, empiricism is the view that all knowledge is based on or may come from direct observation and experience.

The empiricist approach of gaining knowledge through experience quickly became the scientific approach and greatly influenced the development of physics and chemistry in the 17th and 18th centuries.

Step 1: Make an Observation (Theory Construction)

Every researcher starts at the very beginning. Before diving in and exploring something, one must first determine what they will study – it seems simple enough!

By making observations, researchers can establish an area of interest. Once this topic of study has been chosen, a researcher should review existing literature to gain insight into what has already been tested and determine what questions remain unanswered.

This assessment will provide helpful information about what has already been comprehended about the specific topic and what questions remain, and if one can go and answer them.

Specifically, a literature review might implicate examining a substantial amount of documented material from academic journals to books dating back decades. The most appropriate information gathered by the researcher will be shown in the introduction section or abstract of the published study results.

The background material and knowledge will help the researcher with the first significant step in conducting a psychology study, which is formulating a research question.

This is the inductive phase of the scientific process. Observations yield information that is used to formulate theories as explanations. A theory is a well-developed set of ideas that propose an explanation for observed phenomena.

Inductive reasoning moves from specific premises to a general conclusion. It starts with observations of phenomena in the natural world and derives a general law.

Step 2: Ask a Question

Once a researcher has made observations and conducted background research, the next step is to ask a scientific question. A scientific question must be defined, testable, and measurable.

A useful approach to develop a scientific question is: “What is the effect of…?” or “How does X affect Y?”

To answer an experimental question, a researcher must identify two variables: the independent and dependent variables.

The independent variable is the variable manipulated (the cause), and the dependent variable is the variable being measured (the effect).

An example of a research question could be, “Is handwriting or typing more effective for retaining information?” Answering the research question and proposing a relationship between the two variables is discussed in the next step.

Step 3: Form a Hypothesis (Make Predictions)

A hypothesis is an educated guess about the relationship between two or more variables. A hypothesis is an attempt to answer your research question based on prior observation and background research. Theories tend to be too complex to be tested all at once; instead, researchers create hypotheses to test specific aspects of a theory.

For example, a researcher might ask about the connection between sleep and educational performance. Do students who get less sleep perform worse on tests at school?

It is crucial to think about different questions one might have about a particular topic to formulate a reasonable hypothesis. It would help if one also considered how one could investigate the causalities.

It is important that the hypothesis is both testable against reality and falsifiable. This means that it can be tested through an experiment and can be proven wrong.

The falsification principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory to be considered scientific, it must be able to be tested and conceivably proven false.

To test a hypothesis, we first assume that there is no difference between the populations from which the samples were taken. This is known as the null hypothesis and predicts that the independent variable will not influence the dependent variable.

Examples of “if…then…” Hypotheses:

• If one gets less than 6 hours of sleep, then one will do worse on tests than if one obtains more rest.
• If one drinks lots of water before going to bed, one will have to use the bathroom often at night.
• If one practices exercising and lighting weights, then one’s body will begin to build muscle.

The research hypothesis is often called the alternative hypothesis and predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Although one could state and write a scientific hypothesis in many ways, hypotheses are usually built like “if…then…” statements.

Step 4: Run an Experiment (Gather Data)

The next step in the scientific method is to test your hypothesis and collect data. A researcher will design an experiment to test the hypothesis and gather data that will either support or refute the hypothesis.

The exact research methods used to examine a hypothesis depend on what is being studied. A psychologist might utilize two primary forms of research, experimental research, and descriptive research.

The scientific method is objective in that researchers do not let preconceived ideas or biases influence the collection of data and is systematic in that experiments are conducted in a logical way.

Experimental Research

Experimental research is used to investigate cause-and-effect associations between two or more variables. This type of research systematically controls an independent variable and measures its effect on a specified dependent variable.

Experimental research involves manipulating an independent variable and measuring the effect(s) on the dependent variable. Repeating the experiment multiple times is important to confirm that your results are accurate and consistent.

One of the significant advantages of this method is that it permits researchers to determine if changes in one variable cause shifts in each other.

While experiments in psychology typically have many moving parts (and can be relatively complex), an easy investigation is rather fundamental. Still, it does allow researchers to specify cause-and-effect associations between variables.

Most simple experiments use a control group, which involves those who do not receive the treatment, and an experimental group, which involves those who do receive the treatment.

An example of experimental research would be when a pharmaceutical company wants to test a new drug. They give one group a placebo (control group) and the other the actual pill (experimental group).

Descriptive Research

Descriptive research is generally used when it is challenging or even impossible to control the variables in question. Examples of descriptive analysis include naturalistic observation, case studies , and correlation studies .

One example of descriptive research includes phone surveys that marketers often use. While they typically do not allow researchers to identify cause and effect, correlational studies are quite common in psychology research. They make it possible to spot associations between distinct variables and measure the solidity of those relationships.

Step 5: Analyze the Data and Draw Conclusions

Once a researcher has designed and done the investigation and collected sufficient data, it is time to inspect this gathered information and judge what has been found. Researchers can summarize the data, interpret the results, and draw conclusions based on this evidence using analyses and statistics.

Upon completion of the experiment, you can collect your measurements and analyze the data using statistics. Based on the outcomes, you will either reject or confirm your hypothesis.

Analyze the Data

So, how does a researcher determine what the results of their study mean? Statistical analysis can either support or refute a researcher’s hypothesis and can also be used to determine if the conclusions are statistically significant.

When outcomes are said to be “statistically significant,” it is improbable that these results are due to luck or chance. Based on these observations, investigators must then determine what the results mean.

An experiment will support a hypothesis in some circumstances, but sometimes it fails to be truthful in other cases.

What occurs if the developments of a psychology investigation do not endorse the researcher’s hypothesis? It does mean that the study was worthless. Simply because the findings fail to defend the researcher’s hypothesis does not mean that the examination is not helpful or instructive.

This kind of research plays a vital role in supporting scientists in developing unexplored questions and hypotheses to investigate in the future. After decisions have been made, the next step is to communicate the results with the rest of the scientific community.

This is an integral part of the process because it contributes to the general knowledge base and can assist other scientists in finding new research routes to explore.

If the hypothesis is not supported, a researcher should acknowledge the experiment’s results, formulate a new hypothesis, and develop a new experiment.

We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist that could refute a theory.

Draw Conclusions and Interpret the Data

When the empirical observations disagree with the hypothesis, a number of possibilities must be considered. It might be that the theory is incorrect, in which case it needs altering, so it fully explains the data.

Alternatively, it might be that the hypothesis was poorly derived from the original theory, in which case the scientists were expecting the wrong thing to happen.

It might also be that the research was poorly conducted, or used an inappropriate method, or there were factors in play that the researchers did not consider. This will begin the process of the scientific method again.

If the hypothesis is supported, the researcher can find more evidence to support their hypothesis or look for counter-evidence to strengthen their hypothesis further.

In either scenario, the researcher should share their results with the greater scientific community.

Step 6: Share Your Results

One of the final stages of the research cycle involves the publication of the research. Once the report is written, the researcher(s) may submit the work for publication in an appropriate journal.

Usually, this is done by writing up a study description and publishing the article in a professional or academic journal. The studies and conclusions of psychological work can be seen in peer-reviewed journals such as  Developmental Psychology , Psychological Bulletin, the  Journal of Social Psychology, and numerous others.

Scientists should report their findings by writing up a description of their study and any subsequent findings. This enables other researchers to build upon the present research or replicate the results.

As outlined by the American Psychological Association (APA), there is a typical structure of a journal article that follows a specified format. In these articles, researchers:

• Supply a brief narrative and background on previous research
• Give their hypothesis
• Specify who participated in the study and how they were chosen
• Provide operational definitions for each variable
• Explain the measures and methods used to collect data
• Describe how the data collected was interpreted
• Discuss what the outcomes mean

A detailed record of psychological studies and all scientific studies is vital to clearly explain the steps and procedures used throughout the study. So that other researchers can try this experiment too and replicate the results.

The editorial process utilized by academic and professional journals guarantees that each submitted article undergoes a thorough peer review to help assure that the study is scientifically sound. Once published, the investigation becomes another piece of the current puzzle of our knowledge “base” on that subject.

This last step is important because all results, whether they supported or did not support the hypothesis, can contribute to the scientific community. Publication of empirical observations leads to more ideas that are tested against the real world, and so on. In this sense, the scientific process is circular.

The editorial process utilized by academic and professional journals guarantees that each submitted article undergoes a thorough peer review to help assure that the study is scientifically sound.

Once published, the investigation becomes another piece of the current puzzle of our knowledge “base” on that subject.

By replicating studies, psychologists can reduce errors, validate theories, and gain a stronger understanding of a particular topic.

Step 7: Repeat the Scientific Method (Iteration)

Now, if one’s hypothesis turns out to be accurate, find more evidence or find counter-evidence. If one’s hypothesis is false, create a new hypothesis or try again.

One may wish to revise their first hypothesis to make a more niche experiment to design or a different specific question to test.

The amazingness of the scientific method is that it is a comprehensive and straightforward process that scientists, and everyone, can utilize over and over again.

So, draw conclusions and repeat because the scientific method is never-ending, and no result is ever considered perfect.

The scientific method is a process of:

• Making an observation.
• Forming a hypothesis.
• Making a prediction.
• Experimenting to test the hypothesis.

The procedure of repeating the scientific method is crucial to science and all fields of human knowledge.

Further Information

• Karl Popper – Falsification
• Thomas – Kuhn Paradigm Shift
• Positivism in Sociology: Definition, Theory & Examples
• Is Psychology a Science?
• Psychology as a Science (PDF)

List the 6 steps of the scientific methods in order

• Make an observation (theory construction)
• Ask a question. A scientific question must be defined, testable, and measurable.
• Form a hypothesis (make predictions)
• Run an experiment to test the hypothesis (gather data)
• Analyze the data and draw conclusions
• Share your results so that other researchers can make new hypotheses

What is the first step of the scientific method?

The first step of the scientific method is making an observation. This involves noticing and describing a phenomenon or group of phenomena that one finds interesting and wishes to explain.

Observations can occur in a natural setting or within the confines of a laboratory. The key point is that the observation provides the initial question or problem that the rest of the scientific method seeks to answer or solve.

What is the scientific method?

The scientific method is a step-by-step process that investigators can follow to determine if there is a causal connection between two or more variables.

Psychologists and other scientists regularly suggest motivations for human behavior. On a more casual level, people judge other people’s intentions, incentives, and actions daily.

While our standard assessments of human behavior are subjective and anecdotal, researchers use the scientific method to study psychology objectively and systematically.

All utilize a scientific method to study distinct aspects of people’s thinking and behavior. This process allows scientists to analyze and understand various psychological phenomena, but it also provides investigators and others a way to disseminate and debate the results of their studies.

The outcomes of these studies are often noted in popular media, which leads numerous to think about how or why researchers came to the findings they did.

Why Use the Six Steps of the Scientific Method

The goal of scientists is to understand better the world that surrounds us. Scientific research is the most critical tool for navigating and learning about our complex world.

Without it, we would be compelled to rely solely on intuition, other people’s power, and luck. We can eliminate our preconceived concepts and superstitions through methodical scientific research and gain an objective sense of ourselves and our world.

All psychological studies aim to explain, predict, and even control or impact mental behaviors or processes. So, psychologists use and repeat the scientific method (and its six steps) to perform and record essential psychological research.

So, psychologists focus on understanding behavior and the cognitive (mental) and physiological (body) processes underlying behavior.

In the real world, people use to understand the behavior of others, such as intuition and personal experience. The hallmark of scientific research is evidence to support a claim.

Scientific knowledge is empirical, meaning it is grounded in objective, tangible evidence that can be observed repeatedly, regardless of who is watching.

The scientific method is crucial because it minimizes the impact of bias or prejudice on the experimenter. Regardless of how hard one tries, even the best-intentioned scientists can’t escape discrimination. can’t

It stems from personal opinions and cultural beliefs, meaning any mortal filters data based on one’s experience. Sadly, this “filtering” process can cause a scientist to favor one outcome over another.

For an everyday person trying to solve a minor issue at home or work, succumbing to these biases is not such a big deal; in fact, most times, it is important.

But in the scientific community, where results must be inspected and reproduced, bias or discrimination must be avoided.

When to Use the Six Steps of the Scientific Method ?

One can use the scientific method anytime, anywhere! From the smallest conundrum to solving global problems, it is a process that can be applied to any science and any investigation.

Even if you are not considered a “scientist,” you will be surprised to know that people of all disciplines use it for all kinds of dilemmas.

Try to catch yourself next time you come by a question and see how you subconsciously or consciously use the scientific method.

Stack Exchange Network

Stack Exchange network consists of 183 Q&A communities including Stack Overflow , the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

Q&A for work

Connect and share knowledge within a single location that is structured and easy to search.

Difference between experimental data and observational data?

I'm a novice to data mining and started to read about it. What's the exact difference between experimental data and observation data? Both are obviously data; and many say observation data can lead to errors. But I guess it's not possible to do an experiment for all data sets. I'm really confused, explain me what is experimental data and observation data and say when these should be used?

Thanks in advance.

• data-mining

• 1 $\begingroup$ An example of observational data $\endgroup$ –  whuber ♦ Commented Jul 21, 2011 at 1:33

2 Answers 2

wow, that's a tough one :-)

That question is far more widely relevant than just in data mining. It comes up in medicine and in the social sciences including psychology all the time.

The distinction is necessary when it comes to drawing conclusions about causality, that is, when you want to know if something (e.g. a medical treatment) causes another thing (e.g. recovery of a patient). Hordes of scientists and philosophers debate whether you can draw conclusions about causality from observational studies or not. You might want to look at the question statistics and causal inference? .

So what is an experiment? Concisely, an experiment is often defined as random assignment of observational units to different conditions, and conditions differ by the treatment of observational units. Treatment is a generic term, which translates most easily in medical applications (e.g. patients are treated differently under different conditions), but it also applies to other areas. There are variations of experiments --- you might want to start by reading the wikipedia entries for Experiment and randomized experiment --- but the one crucial point is random assignment of subjects to conditions.

With that in mind, it is definitely not possible to do an experiment for all kinds of hypotheses you want to test. For example, you sometimes can't do experiments for ethical reasons, e.g. you don't want people to suffer because of a treatment. In other cases, it might be physically impossible to conduct an experiment.

So whereas experimentation (controlled randomized assignment to treatment conditions) is the primary way to draw conclusions about causality --- and for some, it is the only way --- people still want to do something empirical in those cases where experiments are not possible. That's when you want to do an observational study.

To define an observational study, I draw on Paul Rosenbaums entry in the encyclopedia of statistics in behavioral science: An observational study is "an empiric comparison of treated and control groups in which the objective is to elucidate cause-and-effect relationships [. . . in which it] is not feasible to use controlled experimentation, in the sense of being able to impose the procedures or treatments whose effects it is desired to discover, or to assign subjects at random to different procedures." In an observational study, you try to measure as many variables as possible, and you want to test hypotheses about what changes in a set of those variables are associated with changes in other sets of variables, often with the goal of drawing conclusions about causality in these associations (see Under what conditions does correlation imply causation

In what ways can observational studies lead to errors? Primarily if you want to draw conclusions about causality. The issue that arises is that there might always be the chance that some variables you did not observe are the "real" causes (often called "unmeasured confounding"), so you might falsely assume that one of your measured variables is causing something, whereas "in truth" it is one of the unmeasured confounders. In experiments, the general assumption is that by random assignment potential confounders will get canceled out.

If you want to know more, start by going through the links provided, and look at publications from people like Paul Rosenbaum or the book-link provided by iopsych : Experimental and Quasi-Experimental Designs for Generalized Causal Inference (Shadish, Cook, and Campbell, (2002)

• 1 $\begingroup$ nice and cool answer.. this will surely help me.. thanks a lot :D $\endgroup$ –  Ant's Commented Jul 21, 2011 at 16:29

Very much in a nutshell: only data for which you have all covariates under control, and have either randomization over possible confounders or enough information on them to properly account for them, can be truly called experimental. This could e.g. be the case in plant research where genetically identical and similarly grown plants are feasible: you can then make sure that only your variable of interest differs between groups of interest.

The place where (in statistically correct research) this matters most, is in trying to find a causal relation. A classical example is people taking aspirin, and the effect it has on heart disease: if you pick 100 people who take aspirin and 100 people who don't, and then somehow measure their heart condition, then even if the aspirin takers are at a lower risk frmo this research, you cannot conclude that people should all take aspirin: perhaps the aspirin taking and heart 'improvement' are both consequences of 'better living' or similar.

So, basically (since in reality we almost always want to show that A is a consequence f B): if it is available/attainable: prefer experimental data.

Your Answer

Sign up or log in, post as a guest.

Required, but never shown

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy .

Not the answer you're looking for? Browse other questions tagged dataset data-mining causality or ask your own question .

• Featured on Meta
• We've made changes to our Terms of Service & Privacy Policy - July 2024
• Bringing clarity to status tag usage on meta sites

Hot Network Questions

• Soft links on Python
• What's the proper way to shut down after a kernel panic?
• Do we need to be translating the Hebrew form and meaning of the word hate in Romans 9:13?
• Will fire still burn well in my atmosphere?: concentration vs partial pressure
• How much knowledge of Scripture is enough?
• Opamp Input Noise Source?
• High CPU usage by process with obfuscated name on Linux server – Potential attack?
• Inconsistent “unzip -l … | grep -q …” results with pipefail
• How do you hide an investigation of alien ruins on the moon during Apollo 11?
• If physics can be reduced to mathematics (and thus to logic), does this mean that (physical) causation is ultimately reducible to implication?
• Vector of integers such that almost all dot products are positive
• Difference between 頁 and ページ
• Unexpected behavior of SetDelayed and Derivative
• Melee Opportunist--making opportunity attacks have some bite for melee characters
• Are automorphisms of matrix algebras necessarily determinant preservers?
• My enemy sent me this puzzle!
• "Authorized ESTA After Incorrectly Answering Criminal Offense Question: What Should I Do?"
• Please help me to identify specific house plant
• Sources for Raba bar bar Hana legends explanations
• When was this photo taken?
• Perpendicularity problem about squares drawn externally on a triangle
• What are the limits of Terms of Service as a legal shield for a company?
• Is there racial discrimination at Tbilisi airport?
• Cannot remove old solder

Member-only story

Experimentation in Data Science

When ab testing doesn’t cut it.

Daniel Foley

Towards Data Science

Today I am going to talk about experimentation in data science, why it is so important and some of the different techniques that we might consider using when AB testing is not appropriate. Experiments are designed to identify causal relationships between variables and this is a really important concept in many fields and particularly relevant for data scientists today. Let’s say we are a data scientist working in a product team. In all likelihood, a large part of our role will be to identify whether new features will have a positive impact on the metrics we care about. i.e. if we introduce a new feature making it easier for users to recommend our app to their friends, will this improve user growth? These are the types of questions that product teams will be interested in and experiments can help provide an answer. However, causality is rarely easy to identify and there are many situations where we will need to think a bit deeper about the design of our experiments so we do not make incorrect inferences. When this is the case, we can use often use techniques taken from econometrics and I will discuss some of these below. Hopefully, by the end, you will get a better understanding of when these techniques apply and also how to use them effectively.

Most people reading this have probably heard of AB testing as it is an extremely common method of experimentation used in industry to understand the impact changes we make to our product. It…

Written by Daniel Foley

Data Scientist: https://www.linkedin.com/in/daniel-foley-1ab904a2/ Feel free to visit my Personal Website: https://www.datascientistguide.com/

Text to speech

Stack Exchange Network

Stack Exchange network consists of 183 Q&A communities including Stack Overflow , the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

Q&A for work

Connect and share knowledge within a single location that is structured and easy to search.

experiment data vs experimental data vs testing data

As said like the title, this question haunted me for a long time, I would usually use the phrase experimental data, but I have ever been told experiment data has the identical meaning when referring to the data which was obtained by doing some experiments, I am not sure which one is right and Does testing data have any different meaning than experimental data？

• word-choice
• phrase-choice
• terminology

• The answer depends on the context. "Experimental data" might be fine, or you might be looking for "empirical" data if it comes from real world studies. "Experiment data" doesn't seem right at all. According to thoughtco.com/scientific-method-vocabulary-terms-to-know-609098 , it's just "data". So you'd say "the data that you obtained from the experiment", or "study data", or "trial data". –  therightstuff Commented Feb 22, 2019 at 9:21

The usual expression, if the data comes form an experiment, is experimental data . Experimental here is an adjective; as such, it has three main meanings, and the one we are interested in is "of, for, from, or related to an experiment". The others include "serving as an experiment", which can lead to confusion in the phrase experimental equipment , but we shan't worry about that now.

Experiment data makes sense, seeing experiment as an attributive noun, but I've never come across that in British English, nor American English (though I have had less exposure to that). It sounds unnatural to me. However, study data , that produced in a study (used more often in social science and medical research, in my experience), follows that pattern.

A comment has noted the alternative empirical data . In modern English, this means data that comes from real-world observations rather than that produced in a deliberate experiment. In an experiment, the experimenter controls conditions. In an empirical study, they record the conditions and the results, but do not control them. In archaic usage, experiment referred to both, and to more besides. To know something experimentally meant to know it from experience.

Finally, you ask about testing data . This is equivalent to experimental data , but produced in a test rather than an experiment. A test in this case is where you are testing some process, equipment or otherwise to make sure it behaves as expected/intended. The closely related test data is the data used in such a test, often used in software systems, such as for regression testing , making sure that a new version of software behaves the same as the old version on the same data.

You must log in to answer this question.

Not the answer you're looking for browse other questions tagged word-choice phrase-choice terminology ..

• Featured on Meta
• Bringing clarity to status tag usage on meta sites
• We've made changes to our Terms of Service & Privacy Policy - July 2024
• Announcing a change to the data-dump process

Hot Network Questions

• concrete slab for shed base
• Are there any rockets that leave extra fuel as a backup?
• Will this be the first time that there are more people aboad the ISS than seats in docked spacecraft?
• Comparing force-carrying particles
• Do we need to be translating the Hebrew form and meaning of the word hate in Romans 9:13?
• If more collisions happen with more resistance, why less heat is generated?
• Do mini-humans need a "real" Saturn V to reach the moon?
• Why did Groves evade Robert Oppenheimer's question here?
• What food plants/algae are viable to grow 9,000 meters beneath the sea, if given plenty of light and air?
• Aligning columns with multicolumn header
• Meaning of “ ’thwart” in a 19th century poem
• Is it possible to do physics without mathematics?
• Melee Opportunist--making opportunity attacks have some bite for melee characters
• Why are volumes of revolution typically taught in Calculus 2 and not Calculus 3?
• My enemy sent me this puzzle!
• "Authorized ESTA After Incorrectly Answering Criminal Offense Question: What Should I Do?"
• Antenna speed: slow and fast tracking( explanation of a parameter in comparison table)
• Create k nodes evenly spaced below another node
• Why do we reduce a body to its center of mass when calculating gain/loss of gravitational potential energy?
• How do I safely remove a mystery cast iron pipe in my basement?
• What's the proper way to shut down after a kernel panic?
• Did US troops insist on segregation in British pubs?
• Is it possible to create a board position where White must make the move that leads to stalemating Black to avoid Black stalemating White?
• is is correct sharepoint API is selecting all items using this query

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• Data Collection Methods | Step-by-Step Guide & Examples

Data Collection Methods | Step-by-Step Guide & Examples

Published on 4 May 2022 by Pritha Bhandari .

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental, or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem .

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

• The  aim of the research
• The type of data that you will collect
• The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

Table of contents

Step 1: define the aim of your research, step 2: choose your data collection method, step 3: plan your data collection procedures, step 4: collect the data, frequently asked questions about data collection.

Before you start the process of data collection, you need to identify exactly what you want to achieve. You can start by writing a problem statement : what is the practical or scientific issue that you want to address, and why does it matter?

Next, formulate one or more research questions that precisely define what you want to find out. Depending on your research questions, you might need to collect quantitative or qualitative data :

• Quantitative data is expressed in numbers and graphs and is analysed through statistical methods .
• Qualitative data is expressed in words and analysed through interpretations and categorisations.

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data.

If you have several aims, you can use a mixed methods approach that collects both types of data.

• Your first aim is to assess whether there are significant differences in perceptions of managers across different departments and office locations.
• Your second aim is to gather meaningful feedback from employees to explore new ideas for how managers can improve.

Prevent plagiarism, run a free check.

Based on the data you want to collect, decide which method is best suited for your research.

• Experimental research is primarily a quantitative method.
• Interviews , focus groups , and ethnographies are qualitative methods.
• Surveys , observations, archival research, and secondary data collection can be quantitative or qualitative methods.

Carefully consider what method you will use to gather data that helps you directly answer your research questions.

When you know which method(s) you are using, you need to plan exactly how you will implement them. What procedures will you follow to make accurate observations or measurements of the variables you are interested in?

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design .

Operationalisation

Sometimes your variables can be measured directly: for example, you can collect data on the average age of employees simply by asking for dates of birth. However, often you’ll be interested in collecting data on more abstract concepts or variables that can’t be directly observed.

Operationalisation means turning abstract conceptual ideas into measurable observations. When planning how you will collect data, you need to translate the conceptual definition of what you want to study into the operational definition of what you will actually measure.

• You ask managers to rate their own leadership skills on 5-point scales assessing the ability to delegate, decisiveness, and dependability.
• You ask their direct employees to provide anonymous feedback on the managers regarding the same topics.

You may need to develop a sampling plan to obtain data systematically. This involves defining a population , the group you want to draw conclusions about, and a sample, the group you will actually collect data from.

Your sampling method will determine how you recruit participants or obtain measurements for your study. To decide on a sampling method you will need to consider factors like the required sample size, accessibility of the sample, and time frame of the data collection.

Standardising procedures

If multiple researchers are involved, write a detailed manual to standardise data collection procedures in your study.

This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorise observations.

This helps ensure the reliability of your data, and you can also use it to replicate the study in the future.

Creating a data management plan

Before beginning data collection, you should also decide how you will organise and store your data.

• If you are collecting data from people, you will likely need to anonymise and safeguard the data to prevent leaks of sensitive information (e.g. names or identity numbers).
• If you are collecting data via interviews or pencil-and-paper formats, you will need to perform transcriptions or data entry in systematic ways to minimise distortion.
• You can prevent loss of data by having an organisation system that is routinely backed up.

Finally, you can implement your chosen methods to measure or observe the variables you are interested in.

The closed-ended questions ask participants to rate their manager’s leadership skills on scales from 1 to 5. The data produced is numerical and can be statistically analysed for averages and patterns.

To ensure that high-quality data is recorded in a systematic way, here are some best practices:

• Record all relevant information as and when you obtain data. For example, note down whether or how lab equipment is recalibrated during an experimental study.
• Double-check manual data entry for errors.
• If you collect quantitative data, you can assess the reliability and validity to get an indication of your data quality.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
• You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Operationalisation 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, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Bhandari, P. (2022, May 04). Data Collection Methods | Step-by-Step Guide & Examples. Scribbr. Retrieved 26 August 2024, from https://www.scribbr.co.uk/research-methods/data-collection-guide/

Is this article helpful?

Pritha Bhandari

Other students also liked, qualitative vs quantitative research | examples & methods, triangulation in research | guide, types, examples, what is a conceptual framework | tips & examples.

What Is an Experiment? Definition and Design

The Basics of an Experiment

• Chemical Laws
• Periodic Table
• Projects & Experiments
• Scientific Method
• Biochemistry
• Physical Chemistry
• Medical Chemistry
• Chemistry In Everyday Life
• Famous Chemists
• Activities for Kids
• Abbreviations & Acronyms
• Weather & Climate
• Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
• B.A., Physics and Mathematics, Hastings College

Science is concerned with experiments and experimentation, but do you know what exactly an experiment is? Here's a look at what an experiment is... and isn't!

Key Takeaways: Experiments

• An experiment is a procedure designed to test a hypothesis as part of the scientific method.
• The two key variables in any experiment are the independent and dependent variables. The independent variable is controlled or changed to test its effects on the dependent variable.
• Three key types of experiments are controlled experiments, field experiments, and natural experiments.

What Is an Experiment? The Short Answer

In its simplest form, an experiment is simply the test of a hypothesis . A hypothesis, in turn, is a proposed relationship or explanation of phenomena.

Experiment Basics

The experiment is the foundation of the scientific method , which is a systematic means of exploring the world around you. Although some experiments take place in laboratories, you could perform an experiment anywhere, at any time.

Take a look at the steps of the scientific method:

• Make observations.
• Formulate a hypothesis.
• Design and conduct an experiment to test the hypothesis.
• Evaluate the results of the experiment.
• Accept or reject the hypothesis.
• If necessary, make and test a new hypothesis.

Types of Experiments

• Natural Experiments : A natural experiment also is called a quasi-experiment. A natural experiment involves making a prediction or forming a hypothesis and then gathering data by observing a system. The variables are not controlled in a natural experiment.
• Controlled Experiments : Lab experiments are controlled experiments , although you can perform a controlled experiment outside of a lab setting! In a controlled experiment, you compare an experimental group with a control group. Ideally, these two groups are identical except for one variable , the independent variable .
• Field Experiments : A field experiment may be either a natural experiment or a controlled experiment. It takes place in a real-world setting, rather than under lab conditions. For example, an experiment involving an animal in its natural habitat would be a field experiment.

Variables in an Experiment

Simply put, a variable is anything you can change or control in an experiment. Common examples of variables include temperature, duration of the experiment, composition of a material, amount of light, etc. There are three kinds of variables in an experiment: controlled variables, independent variables and dependent variables .

Controlled variables , sometimes called constant variables are variables that are kept constant or unchanging. For example, if you are doing an experiment measuring the fizz released from different types of soda, you might control the size of the container so that all brands of soda would be in 12-oz cans. If you are performing an experiment on the effect of spraying plants with different chemicals, you would try to maintain the same pressure and maybe the same volume when spraying your plants.

The independent variable is the one factor that you are changing. It is one factor because usually in an experiment you try to change one thing at a time. This makes measurements and interpretation of the data much easier. If you are trying to determine whether heating water allows you to dissolve more sugar in the water then your independent variable is the temperature of the water. This is the variable you are purposely controlling.

The dependent variable is the variable you observe, to see whether it is affected by your independent variable. In the example where you are heating water to see if this affects the amount of sugar you can dissolve , the mass or volume of sugar (whichever you choose to measure) would be your dependent variable.

Examples of Things That Are Not Experiments

• Making a model volcano.
• Making a poster.
• Changing a lot of factors at once, so you can't truly test the effect of the dependent variable.
• Trying something, just to see what happens. On the other hand, making observations or trying something, after making a prediction about what you expect will happen, is a type of experiment.
• Bailey, R.A. (2008). Design of Comparative Experiments . Cambridge: Cambridge University Press. ISBN 9780521683579.
• Beveridge, William I. B., The Art of Scientific Investigation . Heinemann, Melbourne, Australia, 1950.
• di Francia, G. Toraldo (1981). The Investigation of the Physical World . Cambridge University Press. ISBN 0-521-29925-X.
• Hinkelmann, Klaus and Kempthorne, Oscar (2008). Design and Analysis of Experiments, Volume I: Introduction to Experimental Design (Second ed.). Wiley. ISBN 978-0-471-72756-9.
• Shadish, William R.; Cook, Thomas D.; Campbell, Donald T. (2002). Experimental and quasi-experimental designs for generalized causal inference (Nachdr. ed.). Boston: Houghton Mifflin. ISBN 0-395-61556-9.
• 10 Things You Need To Know About Chemistry
• Chemistry 101 - Introduction & Index of Topics
• How to Clean Lab Glassware
• How To Design a Science Fair Experiment
• Understanding Experimental Groups
• What Is a Control Group?
• Examples of Independent and Dependent Variables
• How to Write a Lab Report
• The Difference Between Control Group and Experimental Group
• Scientific Method Lesson Plan
• Pre-Lab Prep for Chemistry Lab
• Difference Between Independent and Dependent Variables
• Which Is Faster: Melting Ice in Water or Air?
• What Is the Difference Between Hard and Soft Science?
• 5 Top Reasons Why Students Fail Chemistry
• What Is a Dependent Variable?

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 26 August 2024

An environmentally friendly deep eutectic solvent for CO 2 capture

• Ali Asghar Manafpour 1 ,
• Farzaneh Feyzi 1 &
• Mehran Rezaee 1  

Scientific Reports volume  14 , Article number:  19744 ( 2024 ) Cite this article

Metrics details

• Engineering
• Environmental sciences

A leading cause of global warming is the increase of carbon dioxide (CO 2 ) emissions due to anthropogenic activities which prompts an urgent need for substantial reduction. Recently, CO 2 absorption in deep eutectic solvents (DESs) has attracted scientific attention, because of their adaptability compared to traditional ionic liquids and aqueous amine solutions. This study employs the heating method to synthesize DESs using tetrapropylammonium bromide (TPAB) and formic acid (Fa) with molar ratios of TPAB-Fa (1:1) and TPAB-Fa (1:2). Absorption experiments by static method quantified CO 2 solubility in the DESs under varied pressures and temperatures. TPAB-Fa (1:2) at 25.0 °C was the most efficient with the CO 2 solubility of 0.218. Thermodynamic modeling was performed by employing the nonrandom two liquids activity coefficient model and the Peng–Robinson equation of state for the liquid and gas phases, respectively. The Henry’s law constant was determined from experimental data. CO 2 physical absorption was confirmed via nuclear magnetic resonance (NMR) and Fourier-transform infrared (FT-IR) analyses. TPAB-Fa (1:2), as the superior DES, exhibited regeneration efficiency of 99% after five absorption/desorption cycles.

Introduction

Consumption of fossil fuels as the primary energy source significantly affects air pollution and the adverse consequences of climate change 1 , 2 . Carbon capture and storage (CCS) has emerged as a viable approach to mitigate these detrimental effects, including but not limited to the greenhouse effect, global warming, acidification of the oceans, and the spread of diseases and pests 3 , 4 . The established methods for carbon capture include adsorption 5 , absorption 6 , membrane separation 7 , 8 , and chemical capture 9 . The absorption of CO 2 is a promising method due to its effectiveness from an economic and operational point of view. Absorption has better long-term performance and a large processing capacity on the industrial scale 10 , 11 . Aqueous amine solutions are the most frequently used reversible solvents for CO 2 capture in industrial processes 12 . Monoethanolamine (MEA) aqueous solution is extensively utilized in contemporary industries for CO 2 absorption because of its low cost, notable reactivity, high CO 2 capture capacity and significant absorption rate 13 . However, these solvents have some inherent significant drawbacks, such as amine loss due to volatility, environmental issues, high corrosion effects, high energy consumption for the desorption process 14 , 15 , 16 and degradations at high temperatures. Therefore, it is crucial to find environmentally friendly alternatives to aqueous amine-based solvents.

Ionic liquids (ILs) have been the subject of considerable research on CO 2 absorption. This is primarily due to their tunable chemical structure, low vapor pressure, nonflammability, high solvation capacity, thermal stability and potential for utilization at ambient temperature 17 , 18 , 19 . ILs have been found to have applications in various fields, such as organic synthesis, catalysis, separation during extraction and electrochemistry 20 . Numerous subsequent efforts have been devoted to investigating the solubility of CO 2 in ILs. Blanchard et al. 21 conducted the first investigation of CO 2 absorption by 1-butyl-3-methylimidazolium hexafluorophosphate ([Bmim][PF 6 ]) using a high-pressure cell. [Bmim][PF 6 ] absorbed a mole fraction of 0.6 CO 2 at a temperature of 25 °C and a pressure of 8 MPa. Bates et al. 22 suggested a new kind of amino group-functionalized IL with a 0.5 molar uptake of CO 2 per mole of IL at a pressure of 1 atm and temperature of 295 K. Huang et al. 23 documented the presence of a chemical reaction between CO 2 and a basic ionic liquid (ethyltributylphosphonium succinimido ([P4442][Suc])). This reaction usually leads to the absorption of CO 2 with capacities ranging from 0.5 to 1 mol of CO 2 per mole of ionic liquid at a temperature of 25 °C and various partial pressures of CO 2 . However, ILs encountered several limitations that have precluded them from emerging as an optimal candidate for green solvents, including complex and expensive synthesis and the requirement for high purity because the presence of impurities can significantly impair the physicochemical properties of ILs 24 , 25 .

More research is also needed to determine if these solvents are environmentally friendly 26 , 27 , 28 , 29 . To overcome these drawbacks, while maintaining the beneficial characteristics of ILs, a novel class of solvents called deep eutectic solvents (DESs) has been developed.

DESs are formed by combining a hydrogen bond donor (HBD) with a hydrogen bond acceptor (HBA). Since DESs can easily be synthesized, they are practical and economical alternatives to ILs. The first DES was synthesized by Abbott et al. 30 using choline chloride (ChCl) and urea with the molar ratio of 1:2. Both of these components are biodegradable and non-toxic. Some of recent investigations have focused on the corrosion behavior of DES based on choline chloride, representing ammonium quaternary salts. These studies have recognized the high stability of the ammonium salt, which can maintain its stability without decomposition or deactivation under severe electrochemical conditions and in the presence of negative electrical potentials. Consequently, a considerable body of literature exists on the application of choline chloride-based DESs as corrosion inhibitors in aqueous environments 31 . Ammonium quaternary salts are the most commonly employed HBAs due to their accessibility, affordability and low toxicity 32 , 33 . Another advantage of DESs is that HBD and HBA concentrations may be modified to customize their properties for a specific purpose 34 , 35 , 36 , 37 . Also, DESs have emerged as a viable substitute for ILs for CO 2 absorption 38 , 39 , 40 , 41 . Li et al. 42 effectively synthesized several choline-based DESs and utilized them for CO 2 absorption for the first time. They demonstrated that at a pressure of 12.5 MPa and a temperature of 40 °C, ChCl-urea (1:2) absorbed CO 2 with a mole fraction of 0.309. Leron et al. 43 investigated the impact of varying temperatures and pressures on the CO 2 solubility in ChCl-urea (1:2) at temperatures ranging from 303.15 to 343.15 K and pressures of up to 6.0 MPa. They demonstrated that CO 2 solubility in ChCl-urea (1:2) DES increased with increasing pressure and decreased with increasing temperature. Additionally, they examined how the molar ratio of salt and HBD affects the solubility of CO 2 . The solubility of CO 2 in various ammonium and phosphonium DESs was examined by Sarmad et al. 44 at temperatures of 298.15 K and pressures of up to 2 MPa. They reported that the CO 2 solubility of 15 synthesized DESs is higher than that of conventional ILs. It should be mentioned that the renewal of the absorbent in practical applications is important in any absorption process including CO 2 capture. Zhang et al. 45 demonstrated that after six absorption–desorption cycles, the regeneration efficiency of [TETA]Cl-DG (1:2) and [TETA]Cl-EG (1:3) drops from 100 to 97.5%. Yan et al. 46 examined the solubility of CO 2 in various superbase IL-based DESs. The 1,8-diazabicyclo-[5,4,0]undec-7-ene imidazole/Ethylene glycol ([HDBU][Im]/EG) with a mass ratio of 7:3, exhibited the maximum CO 2 absorption capacity of 0.141 g CO 2 per g DES at 100 kPa and 40 °C. Additionally, the CO 2 absorption capacity of DES remained stable after five absorption and desorption cycles. Recently, several articles have investigated the effect of viscosity on the solubility of CO 2 in DESs 47 , 48 . The solvent's viscosity is an important physical property that can substantially impact the mass transfer 49 . An enhancement in the solvent's capacity to capture CO 2 can result from a reduction in viscosity 50 . Viscosity, also, impacts the energy needed to manufacture and move materials 51 . Temperature, kind of HBA and HBD, and their respective molar ratios all affect the viscosities of DESs 52 . The viscosity of DES increases during absorption, resulting in a decrease in the absorption rate 46 , 49 . Some studies have documented the viscosities of amine-based DESs in their pure form and the viscosities of the DESs after CO 2 absorption 45 , 53 . For a solvent to be considered suitable in the gas absorption industry, multiple factors beyond absorption capacity must be evaluated. For solvents with relatively low toxicity, considerations include the potential for long-term use, the absence of solvent loss, the energy required for solvent recovery, and the regenaration efficiency after multiple absorption and desorption cycles. Aqeous amine solutions which chemically absorb CO 2 , present challenges such as low biodegradability, volatility, and high energy requirements for regeneration, which are costly and environmentally detrimental.

Our study is focuses on experimentally investigating the utilization of tetrapropylammonium bromide (TPAB) and a naturally occurring carboxylic acid to form a DES for CO 2 capture. To accomplish this goal, two DESs were synthesized employing TPAB as a hydrogen bond acceptor (HBA) and formic acid (Fa) as a hydrogen bond donor (HBD) in molar ratios of 1:1 and 1:2. The presence of hydrogen bond between TPAB and Fa, and physical absorption of CO 2 were confirmed through FT-IR and NMR spectra. Experiments were conducted at the temperatures of 25.0, 35.0 and 45.0 °C and pressures of approximately up to 35.000 bar. The impact of variations in pressure, temperature, and viscosity on CO 2 absorption was investigated. Using the CO 2 solubility data, Henry’s law constant and the enthalpy of dissolution were obtained. To model the vapor–liquid equilibrium of the CO 2 -DES system, the Peng–Robinson equation of state (PR EOS) 54 and the nonrandom two liquid (NRTL) 55 activity coefficient model were employed. Furthermore, five cycles of regeneration experiments were performed on the DES with better performance under vacuum and at 65.0 °C condition.

Experimental procedure

The substances used in this study and their corresponding molecular structures, sources, and purities are presented in Table 1 . They were used as received.

Synthesis of DESs

The DESs were synthesized by the heating method. HBA and HBD were mixed at a precise molar ratio and temperature. TPAB and Fa were introduced into a stainless steel two-shell reactor autoclave. Two DESs were made with the molar ratios of TPAB-Fa (1:1) and TPAB-Fa (1:2) using a balance (Precisa XT220A with the precision of ± 10 −4  g). The mixture was then agitated using a magnetic stirrer (Fisher, Cat. No. 14–511-113) and heated using a circulating water bath (LAUDA Alpha RA 8, 248–373 K) for five hours at 70.0 °C. The obtained liquid was a clear and uniform phase.

CO 2 absorption experimental setup

The equipment used for the absorption experiments, which is shown in Fig.  1 , is the same as used in the author’s previous publications 56 , 57 , 58 , 59 . It is comprised of a CO 2 gas cylinder, a gas container (182 ml) connected to the CO 2 cylinder via valve V 1 , an autoclave reactor (37 ml) connected to the gas container through valves V 4 and V 6 , a magnetic stirrer (Fisher, Cat. No. 14–511-113), a circulating water bath (LAUDA Alpha RA 8, 248–373 K) for temperature adjustment, temperature sensors (T S1 , T S2 ) (K-type, with the precision of ± 0.1 K), and pressure sensors (P S1 , P S2 ) (Sensys, Model: M5156-11700X-050BG, with the precision of ± 2.5 kPa). These sensors measure and control the temperature and pressure of the gas container and autoclave reactor. They are connected to a computer for data analysis, display, and storage. V 2 , V 7 , and V 8 are the valves that release CO 2 from the system. V 3 and V 5 valves connect the gas container and autoclave reactor to P S1 and P S2 .

Schematic of the experimental setup for CO 2 absorption–desorption: (1) CO 2 cylinder, (2) gas container, (3) autoclave reactor, (4) magnetic stirrer, (5) circulating water bath, and (6) computer.

FT-IR, NMR, and viscosity analyses

To investigate the hydrogen bond interaction between HBA and HBD and the production of DES, we employed analytical techniques such as FT-IR (Perkin Elmer Spectrum RX1, Canada) and NMR (Bruker DRX-500, operating at 500 MHz). Furthermore, to determine the absorption mechanism, these investigations were carried out before and after CO 2 absorption. The viscosity of the eutectic solvent was measured using the dynamic shear rheometer (DSR) SmartPave 102e at temperatures of 25.0 and 45.0 °C for each molar ratio of DES.

CO 2 absorption and desorption experiments

A precise amount of DES was injected into the autoclave reactor, then, the gas container and the autoclave reactor were purged of gases using a vacuum pump. CO 2 was supplied into the gas container from the cylinder. After the temperature and pressure of the gas container were fixed, the quantity of CO 2 entering the gas container ( \(n_{{co_{2} }}^{e}\) ) was determined by applying Eq. ( 1 ). Subsequently, CO 2 was delivered into the autoclave reactor. Equations ( 2 ), ( 3 ) were employed to calculate the number of moles of CO 2 that remained in the CO 2 container ( \(n_{{co_{2} }}^{r}\) ) and entered into the autoclave reactor ( \(n_{{co_{2} }}^{g}\) ).

In the above equations P , T , and Z represent pressure, temperature, and compressibility factor, respectively. Superscripts and subscripts e and r refer to entering and remaining in the gas container, respectively. \({n}_{{CO}_{2}}^{g}\) denotes the number of gas molecules entering the autoclave reactor. R and \({V}_{gc}\) represent the universal gas constant and the volume of the gas container, respectively. Equations ( 4 ), ( 5 ) were employed to calculate the amount of CO 2 molecules absorbed in the DES ( \({n}_{{CO}_{2}}^{l}\) ) and the amount that remained in the autoclave reactor after reaching equilibrium ( \({n}_{{CO}_{2}}^{eq}\) ). Superscript eq refers to the phase equilibrium condition, \({V}_{DES}\) refers to the volume of the solvent and V is the volume of the autoclave reactor.

All the compressibility factors were calculated by the PR EOS 54 .

CO 2 absorption investigations were conducted at three temperatures (25.0, 35.0, and 45.0 °C) and, as mentioned before, two TPAB to Fa molar ratios of 1:1 and 1:2. Six equilibrium pressures (ranging from 1.650 to 35.125 bar) were determined at each temperature for each TPAB to Fa molar ratios. Five regeneration cycles were conducted at 65.0 °C and atmospheric pressure for the best TPAB to FA ratio, which is specified in the next sections. The efficiency of solvent regeneration ( \({\eta }_{reg}\) ) was determined using Eq. ( 6 ) in which the number of moles of CO 2 absorbed during the i-th regeneration and the initial absorption are represented by the variables \({n}_{i}\) and \({n}_{0}\) , respectively.

CO 2 absorption thermodynamic modeling

The γ - φ approach was adopted for thermodynamic modeling. The solubility of CO 2 was determined by using the NRTL 55 model in conjunction with the PR EOS. The PR EOS 54 is presented by Eqs. ( 7 ), ( 8 ), ( 9 ), ( 10 ).

where a is the parameter representative of attractive forces between molecules and b denotes the co-volume parameter. v , \({T}_{C}\) , \({P}_{C}\) , \(\omega\) , and \({T}_{r}\) are the volume, the critical temperature, the critical pressure, the acentric factor, and reduced temperature, respectively. Equations ( 11 ), ( 12 ), ( 13 ) represent the NRTL 55 activity coefficient model.

\(\alpha\) and \(b_{ji}\) are the non-randomness and binary interaction parameters, respectively.

Results and discussion

Structure of dess, ft-ir analysis of synthesized dess.

Figure  2 displays the FT-IR spectra of Fa, TPAB, TPAB-Fa (1:1), and TPAB-Fa (1:2). The spectra of Fa exhibits two peaks at 2941 and 3106 cm −1 , which are attributed to the –OH stretching vibration 29 , 60 . In contrast, the spectra of TPAB shows peaks at 2870, 2926, and 2963 cm −1 , which are related to the -CH stretching vibration 61 . After blending the substances, the spectral peaks at 2941 cm −1 and 3106 cm −1 of Fa and the peak at 2926 cm −1 of TPAB are eliminated for both DESs. The removal can be attributed to the establishment of hydrogen bonds between TPAB and Fa. Furthermore, the TPAB peaks at 2870 and 2963 cm −1 have shifted to 2960 and 2975 cm −1 , respectively, with decreased intensity. This phenomenon can be attributed to a modification in the intensity of the hydrogen bonds. The combination of two separate peaks at 1458 and 1487 cm −1 in the TPAB spectrum into a solitary peak at 1476 cm -1 , detected in the spectra of both DESs, provides more insight into the creation of hydrogen bond 62 .

FT-IR spectra of HBA, HBD, and DESs. ( a ) Fa, ( b ) TPAB, ( c ) TPAB-Fa (1:1), ( d ) TPAB-Fa (1:2).

NMR analysis

The 1 HNMR and 13 CNMR spectra of HBA, HBD, and DES were obtained using deuterium oxide (D 2 O) as the solvent. Figure  3 displays the 1 HNMR spectra of Fa, TPAB, and TPAB-Fa (1:2). TPAB-Fa (1:2) was subjected to 13 CNMR and 1 HNMR analysis as the superior DES for CO 2 absorption. Upon mixing, it is evident that the peak of Fa at δ = 7.41 ppm, associated with the –CH group, has shifted to δ = 8.11 ppm, showing a change of the C–H bond in Fa as an HBD. However, the change in the peak at δ = 4.85 ppm, related to D 2 O, is insignificant 63 . The peak related to OH has been removed due to the presence of D 2 O in the system. Fa and TPAB have formed a hydrogen bond connection using the hydrogen atom in the OH group of Fa. Furthermore, the TPAB exhibits peaks at δ = 3.11, 3.17, and 1.72 ppm, which have shifted to 2.98, 1.51, and 0.77 ppm, respectively. This shift indicates that the C–H stretching of TPAB, as a hydrogen bond acceptor, has changed. Based on the explanations provided, it can be deduced that Fa and TPAB effectively carried out their roles as the HBD and HBA, respectively. A primary concern in application of the resulting eutectic solvent in gas absorption, is its potential release into the environment. NMR and FTIR analyses of the raw materials and the DES indicate that hydrogen bonds form between the raw materials without any chemical reactions. This characteristic reduces the solvent's volatility that significantly diminishes the risk of atmospheric dissemination.

1 HNMR spectra of Fa, TPAB, TPAB-Fa (1:2).

CO 2 solubility

The results of CO 2 solubility at three different temperatures, two molar ratios of HBA to HBD, and six pressures are shown in Fig.  4 . It is observed that the molar fraction of CO 2 increases by increase in pressure and decreases with increasing temperature. Initially, the impact of the HBA to HBD molar ratio was examined, which showed that the CO 2 solubility of TPAB-Fa (1:2) was higher than that of TPAB-Fa (1:1) at the same temperature. The TPAB-Fa (1:2) had the maximum CO 2 absorption ( \({x}_{{CO}_{2}}\) ) at pressures higher than 30.000 bar and temperatures of 25.0, 35.0, and 45.0 °C, with values of 0.218, 0.156, and 0.137, respectively, whereas the TPAB-Fa (1:1) showed solubility values of 0.169, 0.147, and 0.127 at the same temperatures. Higher absorption in the TPAB-Fa (1:2) could be attributed to the existence of more location for CO 2 absorption because of one more –OH group, as shown by the 1 HNMR study, which also revealed that TPAB and Fa had a hydrogen bond from the –OH side of Fa.

Experimental mole fraction of absorbed CO 2 in two molar ratios of DES at 25.0, 35.0, and 45.0 °C and pressures up to 35.000 bar.

Furthermore, the justification for this observation can be the viscosity of DESs. As mentioned previously, the viscosity of any solvent plays a crucial role in absorption. The viscosity values of TPAB-Fa (1:2) at 25.0 and 45.0 °C are 53.98 and 21.62 mPa·s, respectively, whereas the viscosity of TPAB-Fa (1:1) are 946.16 and 222.55 mPa·s at the same temperatures. As the HBD ratio increased, the eutectic solvent viscosity decreased.

Our results showed that the solvation of CO 2 in the DES with the higher HBD to HBA ratio was higher at the same temperature. In other words, the relationship between viscosity and temperature is inversely proportional. Higher temperatures lead to less absorption, whereas lower viscosities increase the solubility of CO 2 . At 25.0 and 35.0 °C temperatures, TPAB-Fa (1:1) demonstrated more capture than TPAB-Fa (1:2) at temperatures of 35.0 and 45.0 °C, respectively. This suggests that the impact of temperature outweighs the impact of viscosity. Figure  4 also demonstrates that the difference in CO 2 absorption at varying temperatures under low pressures is negligible.

Nevertheless, with increasing pressure, the effect of temperature on absorption becomes more significant. Desorption of CO 2 may be done by lowering pressure to a vacuum and relying less on raising temperature to high levels. The desorption operation can make advantage of this observation to achieve high desorption efficiency with less energy consumption.

The absorption process mechanism was identified by FT-IR and 13 CNMR analyses. Figure  5 presents the FT-IR analysis of DESs before and after CO 2 absorption. A small new peak at 2200 cm −1 is observed after absorption in TPAB-Fa (1:1); indicating the presence of asymmetric O = C = O stretching in the eutectic solvent. In addition, considerable peaks are seen at 3600–3800 cm −1 , identifying CO 2 combination bands 64 , 65 . These combination bands arise from the interactions between different vibrational modes, enhancing the utility of FTIR spectroscopy in studying and quantifying CO 2 in diverse applications 65 . No substantial change is observed in peaks of TPAB-Fa (1:2) after absorption. This may be due to the low viscosity of the DES, which causes CO 2 to desorb quickly before FT-IR and NMR analyses can be conducted, also indicating no chemical reactions between the DES and CO 2 . This issue was further validated by the 13 CNMR analysis depicted in Fig.  6 . It is evident that after absorption, both the number and quantity of 13 CNMR peaks are not changed in TPAB-Fa (1:2), suggesting that the CO 2 was absorbed physically 66 . We may reach to the conclusion that it is due to the physical nature of the absorption mechanism that absorption amount increases with increasing pressure.

FT-IR analysis of DES before and after absorption: ( a ) before absorption, ( b ) after absorption, (1) TPAB-Fa (1:1), (2) TPAB-Fa (1:2).

13 CNMR analysis of TPAB-Fa (1:2), ( a ) before absorption, ( b ) after absorption.

Thermodynamic modeling of vapor–liquid equilibrium

The DES-CO 2 phase equilibrium was correlated using PR EOS 54 and NRTL model 55 for the vapor and liquid phases, respectively. It was assumed that the experimental vapor pressure of DES is negligible. Hence, the vapor phase consists of pure CO 2 . Henry’s law constant was calculated using the slope of the fugacity-mole fraction diagram of experimental data at points where the mole fraction was less than 0.08. The \(\gamma\) - \(\varphi\) approach was used to fit the NRTL 55 binary interaction parameters. The value of \(\alpha\) was set to 0.3. Parameters were adjusted using the MATLAB 67 software (version 2022b 1.0.0.1) for both DESs at each temperature. The objective function (OF) was defined by Eq. ( 14 ).

where \(n\) represents the number of data points. \({x}_{exp}\) and \({x}_{cal}\) denote the experimental and calculated mole fractions, respectively. Table 2 introduces the Henry’s law constants, OF, \(({b}_{ij},{b}_{ji})\) .

In Fig.  7 , the modeling results of the \(\gamma\) - \(\varphi\) approach and the experimental solubility data are compared. As it is observed, the calculation results are extremely close to the experimental data.

Calculated and experimental mole fractions as a function of pressure at different temperatures: ( a ) TPAB-Fa (1:1) at 25.0 °C, ( b ) TPAB-Fa (1:1) at 35.0 °C, ( c ) TPAB-Fa (1:1) at 45.0 °C, ( d ) TPAB-Fa (1:2) at 25.0 °C, ( e ) TPAB-Fa (1:2) at 35.0 °C, ( f ) TPAB-Fa (1:2) at 45.0 °C.

Desorption of CO 2

Critical considerations when identifying stable solvents for industrial applications require the degree to which the solubility decreases after a series of absorption–desorption steps. Based on the data presented in Fig.  4 , there is no significant difference in CO 2 absorption at low pressures between 25.0 and 45.0 °C. Therefore, it can be inferred that in the desorption process, which occurs at higher temperatures and lower pressures, the temperature increase does not play a significant role in desorption compared to the pressure decrease. In contrast to previous studies that considered a desorption temperature of 100°C 66 , 68 , 69 , this study reached high desorption efficiency at approximately vacuum pressure and a temperature of 65.0 °C. This practical study demonstrates high desorption efficiency at a temperature considerably lower than other studies. The mole fraction of CO 2 in TPAB-Fa (1:2) is shown in Fig.  8 after six successive absorptions at 25.0 °C and desorption at 65.0 °C in vacuum condition. During the initial absorption after regeneration, the solvent’s efficiency decreased by approximately 1% at the maximum pressure. The amount of experimental CO 2 absorption during the second to fifth cycle, at the same pressures as the first absorption–desorption cycle, was determined using regression between values of pressure. However, the solvent’s efficiency remained relatively stable in the subsequent four cycles. Table 3 displays regeneration efficiency ( \({\eta }_{reg}\) ) of DESs at each cycle. At low pressures, the percentage was approximately 90–95%. However, as the pressure increased, the percentage also increased. Eventually, at the highest pressure, regeneration efficiency was 99%, demonstrating the low volatility and high stability of the resulting DES. Additionally, the high reversibility of this DES minimizes the need for replenishment.

TPAB-Fa (1:2) absorption–desorption cycle: absorption at 25.0 °C and approximately 2.000–35.000 bar, desorption at 65.0 °C and under vacuum.

Comparison to other DESs

Table 4 presents the solubility of CO 2 in several DESs at varying pressure and temperature ranges compared to the DESs utilized in this study. The findings indicated that TPAB-Fa (1:2) and TPAB-Fa (1:1) outperformed most other solvents. Moreover, it should be taken into account that formic acid is an affordable organic acid with minimal risks to humans and environment due to its natural character. Similarly, TPAB is a cost-effective quaternary ammonium salt. Consequently, the chemicals used in preparing these DESs are more economical than most commonly employed substances in other DESs.

To determine the energy required for the process and the subsequent recovery of the solvent, it is necessary to estimate the enthalpy of solvation (Δ H sol ), defined as the strength of the intermolecular interaction between DES and CO 2 . The Δ H sol was determined at a fixed mole fraction ( \({x}_{{CO}_{2}}=0.1\) ) using the Clausius–Clapeyron equation at 25.0 to 45.0 °C. The results are presented in Table 5 . This is then compared to several other DESs and solvents.

Due to the negative values of Δ H sol , the absorption process of DESs is exothermic. This is the reason why the absorption amount decreases with increasing temperature. While the values of Δ H sol are within the range of other physical and chemical solvents, they are notably lower than aqueous MEA solution. This behavior suggests a weaker interaction between CO 2 and DES molecules, resulting in improved regeneration capability for the DESs used in this study. In other words, the DES requires less energy for the regeneration process, which leads to a reduction in the consumption of non-renewable energy sources and a reduction in the environmental impact caused by their consumption.

Conclusions

This research aimed to investigate the CO 2 absorption capacity of TPAB-Fa (1:1) and TPAB-Fa (1:2) DESs at three different temperatures (25.0, 35.0, and 45.0 °C) and pressures up to more than 35.000 bar. The FT-IR spectra validated the hydrogen bond between HBA and HBD and confirmed the physical absorption of CO 2 in DES. TPAB-Fa (1:2) demonstrated the highest CO 2 solubility ( \({x}_{{CO}_{2}}=0.218\) ) at 25.0 °C and the pressure of 35.200 bar. The solubility of CO 2 displayed an inverse relation with temperature and viscosity while exhibited a direct relation with pressure. The NRTL activity coefficient model and PR EOS accurately modeled the experimental CO 2 solubility data. Henry’s law constant was obtained from experimental data at each temperature. Its minimum value of 15.09 MPa was related to TPAB-Fa (1:2) + CO 2 mixture at 25.0 °C, showing the maximum solubility. The TPAB-Fa (1:2) solvent was regenerated five times under vacuum conditions at 65.0 °C, resulting in a marginal 1% decrease in efficiency. The Clausius–Clapeyron equation was employed to calculate the Δ H sol . According to the values of Δ H sol , the exothermic nature of CO 2 absorption was proved. We may conclude that this study has introduced an environmentally sustainable DES which is distinguished by its cost-effectiveness, higher absorption efficiency, and good recyclability.

Data availability

The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.

Guo, Y. et al. Recent advances in potassium-based adsorbents for CO 2 capture and separation: A review. Carbon Capture Sci. Technol. 1 , 100011 (2021).

Article   CAS   Google Scholar  

Nayak, N., Mehrotra, R. & Mehrotra, S. Carbon biosequestration strategies: A review. Carbon Capture Sci. Technol. 4 , 100065 (2022).

Davis, S. J., Caldeira, K. & Matthews, H. D. Future CO 2 emissions and climate change from existing energy infrastructure. Science 329 , 1330–1333 (2010).

Article   ADS   CAS   PubMed   Google Scholar  

Metz, B., Davidson, O., De Coninck, H., Loos, M. & Meyer, L. IPCC Special Report on Carbon Dioxide Capture and Storage (Cambridge University Press, 2005).

Google Scholar  

Karimi, M., Jodaei, A., Khajvandi, A., Sadeghinik, A. & Jahandideh, R. In-situ capture and conversion of atmospheric CO 2 into nano-CaCO 3 using a novel pathway based on deep eutectic choline chloride-calcium chloride. J. Environ. Manag. 206 , 516–522 (2018).

Zhang, Y. et al. CO 2 absorption and desorption performance by ChCl-MEA-PZ deep eutectic solvent aqueous solutions. Sep. Purif. Technol. 330 , 125275 (2024).

Nocito, F. & Dibenedetto, A. Atmospheric CO 2 mitigation technologies: Carbon capture utilization and storage. Curr.Opin. Green Sustain. Chem. 21 , 34–43 (2020).

Nowosielski, B., Warminska, D. & Cichowska-Kopczynska, I. CO 2 separation using supported deep eutectic liquid membranes based on 1, 2-propanediol. ACS Sustain. Chem. Eng. 11 , 4093–4105 (2023).

Gupta, S. Carbon sequestration in cementitious matrix containing pyrogenic carbon from waste biomass: A comparison of external and internal carbonation approach. J. Build. Eng. 43 , 102910 (2021).

Article   Google Scholar  

Castro, M., Gómez-Díaz, D., Navaza, J. M. & Rumbo, A. Carbon dioxide capture by chemical solvents based on amino acids: Absorption and regeneration. Chem. Eng. Technol. 44 , 248–257 (2021).

Naderi, K., Foroughi, A. & Ghaemi, A. Analysis of hydraulic performance in a structured packing column for air/water system: RSM and ANN modeling. Chem. Eng. Process. Process Intensif. 193 , 109521 (2023).

He, X. et al. Analysis of the energy consumption in solvent regeneration processes using binary amine blends for CO 2 capture. Energy 270 , 126903 (2023).

Aronu, U. E. et al. Solubility of CO 2 in 15, 30, 45 and 60 mass% MEA from 40 to 120 °C and model representation using the extended UNIQUAC framework. Chem. Eng. Sci. 66 , 6393–6406 (2011).

Dutcher, B., Fan, M. & Russell, A. G. Amine-based CO 2 capture technology development from the beginning of 2013 a review. ACS App. Mater. Interfaces 7 , 2137–2148 (2015).

Zhang, R. et al. CuO modified KIT-6 as a high-efficiency catalyst for energy-efficient amine solvent regeneration. Sep. Purif. Technol. 300 , 121702 (2022).

Zheng, Y., Gao, L. & He, S. Analysis of the mechanism of energy consumption for CO 2 capture in a power system. Energy 262 , 125103 (2023).

Chen, L., Xiong, Y., Qin, H. & Qi, Z. Advances of ionic liquids and deep eutectic solvents in green processes of biomass-derived 5-hydroxymethylfurfural. ChemSusChem 15 , e202102635 (2022).

Article   CAS   PubMed   Google Scholar  

Greer, A. J., Jacquemin, J. & Hardacre, C. Industrial applications of ionic liquids. Molecules 25 , 5207 (2020).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Ptak, S., Zarski, A. & Kapusniak, J. The importance of ionic liquids in the modification of starch and processing of starch-based materials. Materials 13 , 4479 (2020).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Pandey, S. Analytical applications of room-temperature ionic liquids: A review of recent efforts. Anal. Chim. Acta 556 , 38–45 (2006).

Blanchard, L. A., Hancu, D., Beckman, E. J. & Brennecke, J. F. Green processing using ionic liquids and CO 2 . Nature 399 , 28–29 (1999).

Article   ADS   Google Scholar  

Bates, E. D., Mayton, R. D., Ntai, I. & Davis, J. H. CO 2 capture by a task-specific ionic liquid. J. Am. Chem. Soc. 124 , 926–927 (2002).

Huang, Y. et al. Preorganization and cooperation for highly efficient and reversible capture of low-concentration CO 2 by ionic liquids. Angew. Chem. Int. Ed. 56 , 13293–13297 (2017).

Article   ADS   CAS   Google Scholar  

Chen, L. et al. Inexpensive ionic liquids:[HSO 4 ] − -based solvent production at bulk scale. Green Chem. 16 , 3098–3106 (2014).

Elhenawy, S., Khraisheh, M., AlMomani, F. & Hassan, M. Key applications and potential limitations of ionic liquid membranes in the gas separation process of CO 2 , CH 4 , N 2 , H 2 or mixtures of these gases from various gas streams. Molecules 25 , 4274 (2020).

Deetlefs, M. & Seddon, K. R. Assessing the greenness of some typical laboratory ionic liquid preparations. Green Chem. 12 , 17–30 (2010).

Romero, A., Santos, A., Tojo, J. & Rodríguez, A. Toxicity and biodegradability of imidazolium ionic liquids. J. Hazard. Mater. 151 , 268–273 (2008).

Tiano, M., Clark, R., Bourgeois, L. & Gomes, M. C. The dialkylcarbonate route to ionic liquids: Purer, safer, greener?. Green Chem. 25 , 2541–2558 (2023).

Naderi, K. et al. Modeling based on machine learning to investigate flue gas desulfurization performance by calcium silicate absorbent in a sand bed reactor. Sci. Rep. 14 , 954 (2024).

Abbott, A. P., Capper, G., Davies, D. L., Rasheed, R. K. & Tambyrajah, V. Novel solvent properties of choline chloride/urea mixtures. Chem. commun. 1 , 70–71 (2003).

Bučko, M. & Bajat, J. A review of the electrochemical corrosion of metals in choline chloride based deep eutectic solvents. J. Electrochem. Sci. Eng. 12 , 237–252 (2022).

García, G., Aparicio, S., Ullah, R. & Atilhan, M. Deep eutectic solvents: Physicochemical properties and gas separation applications. Energy Fuels 29 , 2616–2644 (2015).

Rezaee, M., Feyzi, F. & Dehghani, M. R. Extractive desulfurization of dibenzothiophene from normal octane using deep eutectic solvents as extracting agent. J. Mol. Liq. 333 , 115991 (2021).

Liu, Y. et al. Ionic liquids/deep eutectic solvents for CO 2 capture: Reviewing and evaluating. Green Energy Environ. 6 , 314–328 (2021).

Mirzaei, S., Shamiri, A. & Aroua, M. K. CO 2 absorption/desorption in aqueous single and novel hybrid solvents of glycerol and monoethanolamine in a pilot-scale packed bed column. Energy Fuels 34 , 8503–8515 (2020).

Wang, Y., Ren, S., Hou, Y. & Wu, W. Capture of acidic gases from flue gas by deep eutectic solvents. Processes 9 , 1268 (2021).

Zhang, N. et al. Highly efficient and reversible CO 2 capture by task-specific deep eutectic solvents. Ind. Eng. Chem. Res. 58 , 13321–13329 (2019).

Al-Bodour, A., Alomari, N., Gutiérrez, A., Aparicio, S. & Atilhan, M. High-pressure carbon dioxide solubility in terpene based deep eutectic solvents. J. Environ. Chem. Eng. 10 , 108237 (2022).

Ghanbari-Kalajahi, H. & Haghtalab, A. Vapor-liquid equilibrium of carbon dioxide solubility in a deep eutectic solvent (choline chloride: MDEA) and a mixture of DES with piperazine-experimental study and modeling. J. Mol. Liq. 375 , 121310 (2023).

Imteyaz, S. & Ingole, P. P. Comparison of physicochemical properties of choline chloride-based deep eutectic solvents for CO 2 capture: Progress and outlook. J. Mol. Liq. 376 , 121436 (2023).

Yu, J. et al. Deep eutectic solvents based on cyclodextrin–monoethanolamine for high-efficiency carbon dioxide capture under high temperature. J. Environ. Chem. Eng. 12 , 111625 (2023).

Li, X., Hou, M., Han, B., Wang, X. & Zou, L. Solubility of CO 2 in a choline chloride+ urea eutectic mixture. J. Chem. Eng. Data 53 , 548–550 (2008).

Leron, R. B. & Li, M.-H. Solubility of carbon dioxide in a eutectic mixture of choline chloride and glycerol at moderate pressures. J. Chem. Thermodyn. 57 , 131–136 (2013).

Sarmad, S., Xie, Y., Mikkola, J.-P. & Ji, X. Screening of deep eutectic solvents (DESs) as green CO 2 sorbents: From solubility to viscosity. New J. Chem. 41 , 290–301 (2017).

Zhang, K. et al. Efficient and reversible absorption of CO 2 by functional deep eutectic solvents. Energy Fuels 32 , 7727–7733 (2018).

Yan, H. et al. Superbase ionic liquid-based deep eutectic solvents for improving CO 2 absorption. ACS Sustain. Chem. Eng. 8 , 2523–2530 (2020).

Li, M., Zhu, C., Fu, T., Gao, X. & Ma, Y. Effect of water on amine-based deep eutectic solvents (choline chloride+ monoethanolamine): Structure and physicochemical properties. J. Environ. Chem. Eng. 10 , 106952 (2022).

Mirza, N. R. et al. Viscosities and carbon dioxide solubilities of guanidine carbonate and malic acid-based eutectic solvents. J. Chem. Eng. Data 62 , 348–354 (2017).

Abdrabou, H. K. et al. Experimental investigation of novel ternary amine-based deep eutectic solvents for CO 2 capture. PLoS One 18 , e0286960 (2023).

Wasserscheid, P. & Welton, T. M. Ionic Liquids in Synthesis (Wiley Online Library, 2008).

Li, Z. et al. Absorption of carbon dioxide using ethanolamine-based deep eutectic solvents. ACS Sustain. Chem. Eng. 7 , 10403–10414 (2019).

Song, X. et al. Carbon dioxide separation performance evaluation of amine-based versus choline-based deep eutectic solvents. Renew. Sustain. Energy Rev. 184 , 113499 (2023).

Trivedi, T. J., Lee, J. H., Lee, H. J., Jeong, Y. K. & Choi, J. W. Deep eutectic solvents as attractive media for CO 2 capture. Green Chem. 18 , 2834–2842 (2016).

Peng, D.-Y. & Robinson, D. B. A new two-constant equation of state. Ind. Eng. Chem. Fundam. 15 , 59–64 (1976).

Renon, H. & Prausnitz, J. M. Local compositions in thermodynamic excess functions for liquid mixtures. AIChE J. 14 , 135–144 (1968).

Hedayati, A. & Feyzi, F. CO 2 -binding organic liquids comprised of 1, 1, 3, 3-tetramethylguanidine and alkanol for postcombustion CO 2 capture: Water inhibitory effect of amine promoters. ACS Sustain. Chem. Eng. 8 , 7909–7920 (2020).

Najafloo, A., Zoghi, A. T. & Feyzi, F. Measuring solubility of carbon dioxide in aqueous blends of N-methyldiethanolamine and 2-((2-aminoethyl) amino) ethanol at low CO 2 loadings and modelling by electrolyte SAFT-HR EoS. J. Chem. Thermodyn. 82 , 143–155 (2015).

Zoghi, A. T., Feyzi, F. & Zarrinpashneh, S. Experimental investigation on the effect of addition of amine activators to aqueous solutions of N-methyldiethanolamine on the rate of carbon dioxide absorption. Int. J. Greenh. Gas Control 7 , 12–19 (2012).

Zoghi, A. T., Feyzi, F. & Zarrinpashneh, S. Equilibrium solubility of carbon dioxide in a 30 wt.% aqueous solution of 2-((2-aminoethyl) amino) ethanol at pressures between atmospheric and 4400 kPa: An experimental and modelling study. J. Chem. Thermodyn. 44 , 66–74 (2012).

Ibrahim, M., Nada, A. & Kamal, D. E. Density functional theory and FTIR spectroscopic study of carboxyl group. Indian J. Pure Appl. Phys. 43 , 911–917 (2005).

CAS   Google Scholar  

Ke, P. et al. Preparation of quaternary ammonium salt-modified chitosan microspheres and their application in dyeing wastewater treatment. ACS Omega 5 , 24700–24707 (2020).

Jincheng, W., Xiaoyu, Z., Wenli, H., Nan, X. & Xingchen, P. Synthesis of hyper-branched quaternary ammonium salt and its application into montmorillonite. Powder Technol. 221 , 80–89 (2012).

Hore, P. J. Nuclear Magnetic Resonance (Oxford University Press, 2015).

Book   Google Scholar  

Rolle, F. & Sega, M. Use of FTIR spectroscopy for the measurement of CO 2 carbon stable isotope ratios. in 19th International Congress of Metrology (CIM2019) . 05002 (EDP Sciences).

Stuart, B. H. Infrared Spectroscopy: Fundamentals and Applications (John Wiley & Sons, 2004).

Zubeir, L. F., Van Osch, D. J., Rocha, M. A., Banat, F. & Kroon, M. C. Carbon dioxide solubilities in decanoic acid-based hydrophobic deep eutectic solvents. J. Chem. Eng. Data 63 , 913–919 (2018).

The MathWork, Inc. MATLAB version: 1.0.0.1 (R2022b). https://www.mathworks.com (2022).

Gu, Y., Hou, Y., Ren, S., Sun, Y. & Wu, W. Hydrophobic functional deep eutectic solvents used for efficient and reversible capture of CO 2 . ACS Omega 5 , 6809–6816 (2020).

Jiang, B. et al. Superbase/acylamido-based deep eutectic solvents for multiple-site efficient CO 2 absorption. Energy Fuels 33 , 7569–7577 (2019).

Deng, D., Jiang, Y., Liu, X., Zhang, Z. & Ai, N. Investigation of solubilities of carbon dioxide in five levulinic acid-based deep eutectic solvents and their thermodynamic properties. J. Chem. Thermodyn. 103 , 212–217 (2016).

Zubeir, L. F., Held, C., Sadowski, G. & Kroon, M. C. PC-SAFT modeling of CO 2 solubilities in deep eutectic solvents. J. Phys. Chem. B 120 , 2300–2310 (2016).

Li, G., Deng, D., Chen, Y., Shan, H. & Ai, N. Solubilities and thermodynamic properties of CO 2 in choline–chloride based deep eutectic solvents. J. Chem. Thermodyn. 75 , 58–62 (2014).

Francisco, M., van den Bruinhorst, A., Zubeir, L. F., Peters, C. J. & Kroon, M. C. A new low transition temperature mixture (LTTM) formed by choline chloride+ lactic acid: Characterization as solvent for CO 2 capture. Fluid Phase Equilib. 340 , 77–84 (2013).

Leron, R. B., Caparanga, A. & Li, M.-H. Carbon dioxide solubility in a deep eutectic solvent based on choline chloride and urea at T= 303.15–343.15 K and moderate pressures. J. Taiwan Inst. Chem. Eng. 44 , 879–885 (2013).

Leron, R. B. & Li, M.-H. Solubility of carbon dioxide in a choline chloride–ethylene glycol based deep eutectic solvent. Thermochim. Acta 551 , 14–19 (2013).

Xu, Y., Schutte, R. P. & Hepler, L. G. Solubilities of carbon dioxide, hydrogen sulfide and sulfur dioxide in physical solvents. Can. J. Chem. Eng. 70 , 569–573 (1992).

Wilhelm, E. & Battino, R. Thermodynamic functions of the solubilities of gases in liquids at 25. deg. Chem. Rev. 73 , 1–9 (1973).

Download references

Author information

Authors and affiliations.

Thermodynamic Research Laboratory, School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Tehran, 16846-13114, Iran

Ali Asghar Manafpour, Farzaneh Feyzi & Mehran Rezaee

You can also search for this author in PubMed   Google Scholar

Contributions

A.A.M.: experimental work, methodology, conceptualization, data curation, formal analysis, investigation, writing original draft. F.F.: supervision, conceptualization, methodology, resources, data curation, review and editing. M.R.: methodology, data curation, conceptualization. All authors reviewed the manuscript.

Corresponding author

Correspondence to Farzaneh Feyzi .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Manafpour, A.A., Feyzi, F. & Rezaee, M. An environmentally friendly deep eutectic solvent for CO 2 capture. Sci Rep 14 , 19744 (2024). https://doi.org/10.1038/s41598-024-70761-4

Download citation

Received : 22 April 2024

Accepted : 21 August 2024

Published : 26 August 2024

DOI : https://doi.org/10.1038/s41598-024-70761-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Carbon dioxide absorption
• Deep eutectic solvent
• Thermodynamic modeling
• Tetrapropylammonium bromide
• Formic acid
• Sustainable regeneration

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

arXiv's Accessibility Forum starts next month!

Help | Advanced Search

Computer Science > Social and Information Networks

Title: large-scale collective dynamics in the three iterations of the reddit r/place experiment.

Abstract: The Reddit r/place experiments were a series of online social experiments hosted by Reddit in 2017, 2022, and 2023, where users were allowed to update the colors of pixels in a large shared canvas. The largest of these experiments (in 2022) has attracted over 100 million users who collaborated and competed to produce elaborate artworks that together provide a unique view of the shared interests connecting the diverse communities on Reddit. The user activity traces resulting from these experiments enable us to analyze how online users engage, collaborate, and compete online at an unprecedented scale. However, this requires labeling millions of updates made during the experiments according to their intended artwork. This paper characterizes large-scale activity traces from r/place with a focus on dynamics around successful and failed artworks. To achieve this goal, we propose a dynamic graph clustering algorithm to label artworks by leveraging visual and user-level features. %In the first phase of the algorithm, updates within a snapshot of the experiment are grouped based on proximity, color, and user embeddings. In the second phase, clusters across snapshots are merged via an efficient approximation for the set cover problem. We apply the proposed algorithm to the 2017 edition of r/place and show that it outperforms an existing baseline in terms of accuracy and running time. Moreover, we use our algorithm to identify key factors that distinguish successful from failed artworks in terms of user engagement, collaboration, and competition.

Submission history

Access paper:.

• HTML (experimental)
• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Data Collection | Definition, Methods & Examples

Data Collection | Definition, Methods & Examples

Published on June 5, 2020 by Pritha Bhandari . Revised on June 21, 2023.

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem .

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

• The  aim of the research
• The type of data that you will collect
• The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

Table of contents

Step 1: define the aim of your research, step 2: choose your data collection method, step 3: plan your data collection procedures, step 4: collect the data, other interesting articles, frequently asked questions about data collection.

Before you start the process of data collection, you need to identify exactly what you want to achieve. You can start by writing a problem statement : what is the practical or scientific issue that you want to address and why does it matter?

Next, formulate one or more research questions that precisely define what you want to find out. Depending on your research questions, you might need to collect quantitative or qualitative data :

• Quantitative data is expressed in numbers and graphs and is analyzed through statistical methods .
• Qualitative data is expressed in words and analyzed through interpretations and categorizations.

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data. If you have several aims, you can use a mixed methods approach that collects both types of data.

• Your first aim is to assess whether there are significant differences in perceptions of managers across different departments and office locations.
• Your second aim is to gather meaningful feedback from employees to explore new ideas for how managers can improve.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

Based on the data you want to collect, decide which method is best suited for your research.

• Experimental research is primarily a quantitative method.
• Interviews , focus groups , and ethnographies are qualitative methods.
• Surveys , observations, archival research and secondary data collection can be quantitative or qualitative methods.

Carefully consider what method you will use to gather data that helps you directly answer your research questions.

When you know which method(s) you are using, you need to plan exactly how you will implement them. What procedures will you follow to make accurate observations or measurements of the variables you are interested in?

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design (e.g., determine inclusion and exclusion criteria ).

Operationalization

Sometimes your variables can be measured directly: for example, you can collect data on the average age of employees simply by asking for dates of birth. However, often you’ll be interested in collecting data on more abstract concepts or variables that can’t be directly observed.

Operationalization means turning abstract conceptual ideas into measurable observations. When planning how you will collect data, you need to translate the conceptual definition of what you want to study into the operational definition of what you will actually measure.

• You ask managers to rate their own leadership skills on 5-point scales assessing the ability to delegate, decisiveness and dependability.
• You ask their direct employees to provide anonymous feedback on the managers regarding the same topics.

You may need to develop a sampling plan to obtain data systematically. This involves defining a population , the group you want to draw conclusions about, and a sample, the group you will actually collect data from.

Your sampling method will determine how you recruit participants or obtain measurements for your study. To decide on a sampling method you will need to consider factors like the required sample size, accessibility of the sample, and timeframe of the data collection.

Standardizing procedures

If multiple researchers are involved, write a detailed manual to standardize data collection procedures in your study.

This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorize observations. This helps you avoid common research biases like omitted variable bias or information bias .

This helps ensure the reliability of your data, and you can also use it to replicate the study in the future.

Creating a data management plan

Before beginning data collection, you should also decide how you will organize and store your data.

• If you are collecting data from people, you will likely need to anonymize and safeguard the data to prevent leaks of sensitive information (e.g. names or identity numbers).
• If you are collecting data via interviews or pencil-and-paper formats, you will need to perform transcriptions or data entry in systematic ways to minimize distortion.
• You can prevent loss of data by having an organization system that is routinely backed up.

Finally, you can implement your chosen methods to measure or observe the variables you are interested in.

The closed-ended questions ask participants to rate their manager’s leadership skills on scales from 1–5. The data produced is numerical and can be statistically analyzed for averages and patterns.

To ensure that high quality data is recorded in a systematic way, here are some best practices:

• Record all relevant information as and when you obtain data. For example, note down whether or how lab equipment is recalibrated during an experimental study.
• Double-check manual data entry for errors.
• If you collect quantitative data, you can assess the reliability and validity to get an indication of your data quality.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

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.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bhandari, P. (2023, June 21). Data Collection | Definition, Methods & Examples. Scribbr. Retrieved August 26, 2024, from https://www.scribbr.com/methodology/data-collection/

Is this article helpful?

Pritha Bhandari

Other students also liked, qualitative vs. quantitative research | differences, examples & methods, sampling methods | types, techniques & examples, get unlimited documents corrected.

✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts