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Experimental Group in Psychology Experiments

In a randomized and controlled psychology experiment , the researchers are examining the impact of an experimental condition on a group of participants (does the independent variable 'X' cause a change in the dependent variable 'Y'?). To determine cause and effect, there must be at least two groups to compare, the experimental group and the control group.

The participants who are in the experimental condition are those who receive the treatment or intervention of interest. The data from their outcomes are collected and compared to the data from a group that did not receive the experimental treatment. The control group may have received no treatment at all, or they may have received a placebo treatment or the standard treatment in current practice.

Comparing the experimental group to the control group allows researchers to see how much of an impact the intervention had on the participants.

A Closer Look at Experimental Groups

Imagine that you want to do an experiment to determine if listening to music while working out can lead to greater weight loss. After getting together a group of participants, you randomly assign them to one of three groups. One group listens to upbeat music while working out, one group listens to relaxing music, and the third group listens to no music at all. All of the participants work out for the same amount of time and the same number of days each week.

In this experiment, the group of participants listening to no music while working out is the control group. They serve as a baseline with which to compare the performance of the other two groups. The other two groups in the experiment are the experimental groups.   They each receive some level of the independent variable, which in this case is listening to music while working out.

In this experiment, you find that the participants who listened to upbeat music experienced the greatest weight loss result, largely because those who listened to this type of music exercised with greater intensity than those in the other two groups. By comparing the results from your experimental groups with the results of the control group, you can more clearly see the impact of the independent variable.  

Some Things to Know

When it comes to using experimental groups in a psychology experiment, there are a few important things to know:

• In order to determine the impact of an independent variable, it is important to have at least two different treatment conditions. This usually involves using a control group that receives no treatment against an experimental group that receives the treatment. However, there can also be a number of different experimental groups in the same experiment.
• Care must be taken when assigning participants to groups. So how do researchers determine who is in the control group and who is in the experimental group? In an ideal situation, the researchers would use random assignment to place participants in groups. In random assignment, each individual stands an equal shot at being assigned to either group. Participants might be randomly assigned using methods such as a coin flip or a number draw. By using random assignment, researchers can help ensure that the groups are not unfairly stacked with people who share characteristics that might unfairly skew the results.
• Variables must be well-defined. Before you begin manipulating things in an experiment, you need to have very clear operational definitions in place. These definitions clearly explain what your variables are, including exactly how you are manipulating the independent variable and exactly how you are measuring the outcomes.

A Word From Verywell

Experiments play an important role in the research process and allow psychologists to investigate cause-and-effect relationships between different variables. Having one or more experimental groups allows researchers to vary different levels or types of the experimental variable and then compare the effects of these changes against a control group. The goal of this experimental manipulation is to gain a better understanding of the different factors that may have an impact on how people think, feel, and act.

Byrd-Bredbenner C, Wu F, Spaccarotella K, Quick V, Martin-Biggers J, Zhang Y. Systematic review of control groups in nutrition education intervention research . Int J Behav Nutr Phys Act. 2017;14(1):91. doi:10.1186/s12966-017-0546-3

Steingrimsdottir HS, Arntzen E. On the utility of within-participant research design when working with patients with neurocognitive disorders . Clin Interv Aging. 2015;10:1189-1200. doi:10.2147/CIA.S81868

Oberste M, Hartig P, Bloch W, et al. Control group paradigms in studies investigating acute effects of exercise on cognitive performance—An experiment on expectation-driven placebo effects . Front Hum Neurosci. 2017;11:600. doi:10.3389/fnhum.2017.00600

Kim H. Statistical notes for clinical researchers: Analysis of covariance (ANCOVA) . Restor Dent Endod . 2018;43(4):e43. doi:10.5395/rde.2018.43.e43

Bate S, Karp NA. A common control group — Optimising the experiment design to maximise sensitivity . PLoS ONE. 2014;9(12):e114872. doi:10.1371/journal.pone.0114872

Myers A, Hansen C. Experimental Psychology . 7th Ed. Cengage Learning; 2012.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Control Group Definition and Examples

The control group is the set of subjects that does not receive the treatment in a study. In other words, it is the group where the independent variable is held constant. This is important because the control group is a baseline for measuring the effects of a treatment in an experiment or study. A controlled experiment is one which includes one or more control groups.

• The experimental group experiences a treatment or change in the independent variable. In contrast, the independent variable is constant in the control group.
• A control group is important because it allows meaningful comparison. The researcher compares the experimental group to it to assess whether or not there is a relationship between the independent and dependent variable and the magnitude of the effect.
• There are different types of control groups. A controlled experiment has one more control group.

Control Group vs Experimental Group

The only difference between the control group and experimental group is that subjects in the experimental group receive the treatment being studied, while participants in the control group do not. Otherwise, all other variables between the two groups are the same.

Control Group vs Control Variable

A control group is not the same thing as a control variable. A control variable or controlled variable is any factor that is held constant during an experiment. Examples of common control variables include temperature, duration, and sample size. The control variables are the same for both the control and experimental groups.

Types of Control Groups

There are different types of control groups:

• Placebo group : A placebo group receives a placebo , which is a fake treatment that resembles the treatment in every respect except for the active ingredient. Both the placebo and treatment may contain inactive ingredients that produce side effects. Without a placebo group, these effects might be attributed to the treatment.
• Positive control group : A positive control group has conditions that guarantee a positive test result. The positive control group demonstrates an experiment is capable of producing a positive result. Positive controls help researchers identify problems with an experiment.
• Negative control group : A negative control group consists of subjects that are not exposed to a treatment. For example, in an experiment looking at the effect of fertilizer on plant growth, the negative control group receives no fertilizer.
• Natural control group : A natural control group usually is a set of subjects who naturally differ from the experimental group. For example, if you compare the effects of a treatment on women who have had children, the natural control group includes women who have not had children. Non-smokers are a natural control group in comparison to smokers.
• Randomized control group : The subjects in a randomized control group are randomly selected from a larger pool of subjects. Often, subjects are randomly assigned to either the control or experimental group. Randomization reduces bias in an experiment. There are different methods of randomly assigning test subjects.

Control Group Examples

Here are some examples of different control groups in action:

Negative Control and Placebo Group

For example, consider a study of a new cancer drug. The experimental group receives the drug. The placebo group receives a placebo, which contains the same ingredients as the drug formulation, minus the active ingredient. The negative control group receives no treatment. The reason for including the negative group is because the placebo group experiences some level of placebo effect, which is a response to experiencing some form of false treatment.

Positive and Negative Controls

For example, consider an experiment looking at whether a new drug kills bacteria. The experimental group exposes bacterial cultures to the drug. If the group survives, the drug is ineffective. If the group dies, the drug is effective.

The positive control group has a culture of bacteria that carry a drug resistance gene. If the bacteria survive drug exposure (as intended), then it shows the growth medium and conditions allow bacterial growth. If the positive control group dies, it indicates a problem with the experimental conditions. A negative control group of bacteria lacking drug resistance should die. If the negative control group survives, something is wrong with the experimental conditions.

• Bailey, R. A. (2008).  Design of Comparative Experiments . Cambridge University Press. ISBN 978-0-521-68357-9.
• Chaplin, S. (2006). “The placebo response: an important part of treatment”.  Prescriber . 17 (5): 16–22. doi: 10.1002/psb.344
• Hinkelmann, Klaus; Kempthorne, Oscar (2008).  Design and Analysis of Experiments, Volume I: Introduction to Experimental Design  (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
• Pithon, M.M. (2013). “Importance of the control group in scientific research.” Dental Press J Orthod . 18 (6):13-14. doi: 10.1590/s2176-94512013000600003
• Stigler, Stephen M. (1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

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• Control Groups and Treatment Groups | Uses & Examples

Control Groups & Treatment Groups | Uses & Examples

Published on 6 May 2022 by Lauren Thomas . Revised on 13 April 2023.

In a scientific study, a control group is used to establish a cause-and-effect relationship by isolating the effect of an independent variable .

Researchers change the independent variable in the treatment group and keep it constant in the control group. Then they compare the results of these groups.

Using a control group means that any change in the dependent variable can be attributed to the independent variable.

Table of contents

Control groups in experiments, control groups in non-experimental research, importance of control groups, frequently asked questions about control groups.

Control groups are essential to experimental design . When researchers are interested in the impact of a new treatment, they randomly divide their study participants into at least two groups:

• The treatment group (also called the experimental group ) receives the treatment whose effect the researcher is interested in.
• The control group receives either no treatment, a standard treatment whose effect is already known, or a placebo (a fake treatment).

The treatment is any independent variable manipulated by the experimenters, and its exact form depends on the type of research being performed. In a medical trial, it might be a new drug or therapy. In public policy studies, it could be a new social policy that some receive and not others.

In a well-designed experiment, all variables apart from the treatment should be kept constant between the two groups. This means researchers can correctly measure the entire effect of the treatment without interference from confounding variables .

• You pay the students in the treatment group for achieving high grades.
• Students in the control group do not receive any money.

Studies can also include more than one treatment or control group. Researchers might want to examine the impact of multiple treatments at once, or compare a new treatment to several alternatives currently available.

• The treatment group gets the new pill.
• Control group 1 gets an identical-looking sugar pill (a placebo).
• Control group 2 gets a pill already approved to treat high blood pressure.

Since the only variable that differs between the three groups is the type of pill, any differences in average blood pressure between the three groups can be credited to the type of pill they received.

• The difference between the treatment group and control group 1 demonstrates the effectiveness of the pill as compared to no treatment.
• The difference between the treatment group and control group 2 shows whether the new pill improves on treatments already available on the market.

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Although control groups are more common in experimental research, they can be used in other types of research too. Researchers generally rely on non-experimental control groups in two cases: quasi-experimental or matching design.

Control groups in quasi-experimental design

While true experiments rely on random assignment to the treatment or control groups, quasi-experimental design uses some criterion other than randomisation to assign people.

Often, these assignments are not controlled by researchers, but are pre-existing groups that have received different treatments. For example, researchers could study the effects of a new teaching method that was applied in some classes in a school but not others, or study the impact of a new policy that is implemented in one region but not in the neighbouring region.

In these cases, the classes that did not use the new teaching method, or the region that did not implement the new policy, is the control group.

Control groups in matching design

In correlational research , matching represents a potential alternate option when you cannot use either true or quasi-experimental designs.

In matching designs, the researcher matches individuals who received the ‘treatment’, or independent variable under study, to others who did not – the control group.

Each member of the treatment group thus has a counterpart in the control group identical in every way possible outside of the treatment. This ensures that the treatment is the only source of potential differences in outcomes between the two groups.

Control groups help ensure the internal validity of your research. You might see a difference over time in your dependent variable in your treatment group. However, without a control group, it is difficult to know whether the change has arisen from the treatment. It is possible that the change is due to some other variables.

If you use a control group that is identical in every other way to the treatment group, you know that the treatment – the only difference between the two groups – must be what has caused the change.

For example, people often recover from illnesses or injuries over time regardless of whether they’ve received effective treatment or not. Thus, without a control group, it’s difficult to determine whether improvements in medical conditions come from a treatment or just the natural progression of time.

Risks from invalid control groups

If your control group differs from the treatment group in ways that you haven’t accounted for, your results may reflect the interference of confounding variables instead of your independent variable.

Minimising this risk

A few methods can aid you in minimising the risk from invalid control groups.

• Ensure that all potential confounding variables are accounted for , preferably through an experimental design if possible, since it is difficult to control for all the possible confounders outside of an experimental environment.
• Use double-blinding . This will prevent the members of each group from modifying their behavior based on whether they were placed in the treatment or control group, which could then lead to biased outcomes.
• Randomly assign your subjects into control and treatment groups. This method will allow you to not only minimise the differences between the two groups on confounding variables that you can directly observe, but also those you cannot.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

A true experiment (aka a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment
• Random assignment of participants to ensure the groups are equivalent

Depending on your study topic, there are various other methods of controlling variables .

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

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

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

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• Verywell Mind - What Is a Control Group?
• National Center for Biotechnology Information - PubMed Central - Control Group Design: Enhancing Rigor in Research of Mind-Body Therapies for Depression

control group , the standard to which comparisons are made in an experiment. Many experiments are designed to include a control group and one or more experimental groups; in fact, some scholars reserve the term experiment for study designs that include a control group. Ideally, the control group and the experimental groups are identical in every way except that the experimental groups are subjected to treatments or interventions believed to have an effect on the outcome of interest while the control group is not. Inclusion of a control group greatly strengthens researchers’ ability to draw conclusions from a study. Indeed, only in the presence of a control group can a researcher determine whether a treatment under investigation truly has a significant effect on an experimental group, and the possibility of making an erroneous conclusion is reduced. See also scientific method .

A typical use of a control group is in an experiment in which the effect of a treatment is unknown and comparisons between the control group and the experimental group are used to measure the effect of the treatment. For instance, in a pharmaceutical study to determine the effectiveness of a new drug on the treatment of migraines , the experimental group will be administered the new drug and the control group will be administered a placebo (a drug that is inert, or assumed to have no effect). Each group is then given the same questionnaire and asked to rate the effectiveness of the drug in relieving symptoms . If the new drug is effective, the experimental group is expected to have a significantly better response to it than the control group. Another possible design is to include several experimental groups, each of which is given a different dosage of the new drug, plus one control group. In this design, the analyst will compare results from each of the experimental groups to the control group. This type of experiment allows the researcher to determine not only if the drug is effective but also the effectiveness of different dosages. In the absence of a control group, the researcher’s ability to draw conclusions about the new drug is greatly weakened, due to the placebo effect and other threats to validity. Comparisons between the experimental groups with different dosages can be made without including a control group, but there is no way to know if any of the dosages of the new drug are more or less effective than the placebo.

It is important that every aspect of the experimental environment be as alike as possible for all subjects in the experiment. If conditions are different for the experimental and control groups, it is impossible to know whether differences between groups are actually due to the difference in treatments or to the difference in environment. For example, in the new migraine drug study, it would be a poor study design to administer the questionnaire to the experimental group in a hospital setting while asking the control group to complete it at home. Such a study could lead to a misleading conclusion, because differences in responses between the experimental and control groups could have been due to the effect of the drug or could have been due to the conditions under which the data were collected. For instance, perhaps the experimental group received better instructions or was more motivated by being in the hospital setting to give accurate responses than the control group.

In non-laboratory and nonclinical experiments, such as field experiments in ecology or economics , even well-designed experiments are subject to numerous and complex variables that cannot always be managed across the control group and experimental groups. Randomization, in which individuals or groups of individuals are randomly assigned to the treatment and control groups, is an important tool to eliminate selection bias and can aid in disentangling the effects of the experimental treatment from other confounding factors. Appropriate sample sizes are also important.

A control group study can be managed in two different ways. In a single-blind study, the researcher will know whether a particular subject is in the control group, but the subject will not know. In a double-blind study , neither the subject nor the researcher will know which treatment the subject is receiving. In many cases, a double-blind study is preferable to a single-blind study, since the researcher cannot inadvertently affect the results or their interpretation by treating a control subject differently from an experimental subject.

Home » experimental control important

What An Experimental Control Is And Why It’s So Important

Daniel Nelson

An experimental control is used in scientific experiments to minimize the effect of variables which are not the interest of the study. The control can be an object, population, or any other variable which a scientist would like to “control.”

You may have heard of experimental control, but what is it? Why is an experimental control important? The function of an experimental control is to hold constant the variables that an experimenter isn’t interested in measuring.

This helps scientists ensure that there have been no deviations in the environment of the experiment that could end up influencing the outcome of the experiment, besides the variable they are investigating. Let’s take a closer look at what this means.

You may have ended up here to understand why a control is important in an experiment. A control is important for an experiment because it allows the experiment to minimize the changes in all other variables except the one being tested.

To start with, it is important to define some terminology.

Terminology Of A Scientific Experiment

Randomization is important as it allows for more non-biased results in experiments. Random numbers generators are often used both in scientific studies as well as on 지노 사이트 to make outcomes fairer.

Scientists use the scientific method to ask questions and come to conclusions about the nature of the world. After making an observation about some sort of phenomena they would like to investigate, a scientist asks what the cause of that phenomena could be. The scientist creates a hypothesis, a proposed explanation that answers the question they asked. A hypothesis doesn’t need to be correct, it just has to be testable.

The hypothesis is a prediction about what will happen during the experiment, and if the hypothesis is correct then the results of the experiment should align with the scientist’s prediction. If the results of the experiment do not align with the hypothesis, then a good scientist will take this data into consideration and form a new hypothesis that can better explain the phenomenon in question.

Independent and Dependent Variables

In order to form an effective hypothesis and do meaningful research, the researcher must define the experiment’s independent and dependent variables . The independent variable is the variable which the experimenter either manipulates or controls in an experiment to test the effects of this manipulation on the dependent variable. A dependent variable is a variable being measured to see if the manipulation has any effect.

Photo: frolicsomepl via Pixabay, CC0

For instance, if a researcher wanted to see how temperature impacts the behavior of a certain gas, the temperature they adjust would be the independent variable and the behavior of the gas the dependent variable.

Control Groups and Experimental Groups

There will frequently be two groups under observation in an experiment, the experimental group, and the control group . The control group is used to establish a baseline that the behavior of the experimental group can be compared to. If two groups of people were receiving an experimental treatment for a medical condition, one would be given the actual treatment (the experimental group) and one would typically be given a placebo or sugar pill (the control group).

Without an experimental control group, it is difficult to determine the effects of the independent variable on the dependent variable in an experiment. This is because there can always be outside factors that are influencing the behavior of the experimental group. The function of a control group is to act as a point of comparison, by attempting to ensure that the variable under examination (the impact of the medicine) is the thing responsible for creating the results of an experiment. The control group is holding other possible variables constant, such as the act of seeing a doctor and taking a pill, so only the medicine itself is being tested.

Why Are Experimental Controls So Important?

Experimental controls allow scientists to eliminate varying amounts of uncertainty in their experiments. Whenever a researcher does an experiment and wants to ensure that only the variable they are interested in changing is changing, they need to utilize experimental controls.

Experimental controls have been dubbed “controls” precisely because they allow researchers to control the variables they think might have an impact on the results of the study. If a researcher believes that some outside variables could influence the results of their research, they’ll use a control group to try and hold that thing constant and measure any possible influence it has on the results. It is important to note that there may be many different controls for an experiment, and the more complex a phenomenon under investigation is, the more controls it is likely to have.

Not only do controls establish a baseline that the results of an experiment can be compared to, they also allow researchers to correct for possible errors. If something goes wrong in the experiment, a scientist can check on the controls of the experiment to see if the error had to do with the controls. If so, they can correct this next time the experiment is done.

A Practical Example

Let’s take a look at a concrete example of experimental control. If an experimenter wanted to determine how different soil types impacted the germination period of seeds , they could set up four different pots. Each pot would be filled with a different soil type, planted with seeds, then watered and exposed to sunlight. Measurements would be taken regarding how long it took for the seeds to sprout in the different soil types.

Photo: Kaz via Pixabay, CC0

A control for this experiment might be to fill more pots with just the different types of soil and no seeds or to set aside some seeds in a pot with no soil. The goal is to try and determine that it isn’t something else other than the soil, like the nature of the seeds themselves, the amount of sun they were exposed to, or how much water they are given, that affected how quickly the seeds sprouted. The more variables a researcher controlled for, the surer they could be that it was the type of soil having an impact on the germination period.

  Not All Experiments Are Controlled

“It doesn’t matter how beautiful your theory is, it doesn’t matter how smart you are. If it doesn’t agree with experiment, it’s wrong.” — Richard P. Feynman

While experimental controls are important , it is also important to remember that not all experiments are controlled. In the real world, there are going to be limitations on what variables a researcher can control for, and scientists often try to record as much data as they can during an experiment so they can compare factors and variables with one another to see if any variables they didn’t control for might have influenced the outcome. It’s still possible to draw useful data from experiments that don’t have controls, but it is much more difficult to draw meaningful conclusions based on uncontrolled data.

Though it is often impossible in the real world to control for every possible variable, experimental controls are an invaluable part of the scientific process and the more controls an experiment has the better off it is.

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Experimental Design: Types, Examples & Methods

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

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

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

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

Three types of experimental designs are commonly used:

1. Independent Measures

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

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

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

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

2. Repeated Measures Design

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

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

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

Counterbalancing

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

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

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

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

3. Matched Pairs Design

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

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

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

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

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

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

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

Learning Check

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

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

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

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

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

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

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

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

Experiment Terminology

Ecological validity.

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

Experimenter effects

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

Demand characteristics

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

Independent variable (IV)

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

Dependent variable (DV)

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

Extraneous variables (EV)

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

Confounding variables

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

Random Allocation

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

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

Order effects

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

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

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

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Control Group

Reviewed by: BD Editors

Control Group Definition

In scientific experiments, the control group is the group of subject that receive no treatment or a standardized treatment. Without the control group, there would be nothing to compare the treatment group to. When statistics refer to something being “X times more likely to happen” they are referring to the difference in the measurement between the treatment and control group. The control group provides a baseline in the experiment. The variable that is being studied in the experiment is not changed or is limited to zero in the control group. This insures that the effects of the variable are being studied. Most experiments try to add the variable back in increments to different treatment groups, to really begin to discern the effects of the variable in the system.

Ideally, the control group is subject to the same exact conditions as the treatment groups. This insures that only the effects produced by the variable are being measured. In a study of plants, for instance, all the plants would ideally be in the same room, with the same light and air conditions. In biological studies, it is also important that the organisms in the treatment and control groups come from the same population. Ideally, the organisms would all be clones of each other, to reduce genetic differences. This is the case in many artificially selected lab species, which have been selected to be very similar to each other. This ensures that the results obtained are valid.

Examples of Control Group

Testing enzyme strength.

In a simple biological lab experiment, students can test the effects of different concentrations of enzyme. The student can prepare a stock solution of enzyme by spitting into a beaker. Human spit contains the enzyme amylase, which breaks down starches. The concentration of enzyme can be varied by dividing the stock solution and adding in various amounts of water. Once various solutions of different strength enzyme have been produced, the experiment can begin.

In several treatment beakers are placed the following ingredients: starch, iodine, and the different solutions of enzyme. In the control group, a beaker is filled with starch and iodine, but no enzyme. When iodine is in the presence of starch, it turns black. As the enzyme depletes the starch in each beaker, the solution clears up and is a lighter yellow or brown color. In this way, the student can tell how long the enzymes in each beaker take to completely process the same amount of substrate. The control group is important because it will tell the student if the starch breaks down without the enzyme, which it will, given enough time.

Testing Drugs and the Placebo Effect

When drugs are tested on humans, control groups are also used. Although control groups were just considered good science, they have found an interesting phenomena in drug trials. Oftentimes, control groups in drug trials consist of people who also have the disease or ailment, but who don’t receive the medicine being tested. Instead, to keep the control group the same as the treatment groups, the patients in the control group are also given a pill. This is a sugar pill usually and contains no medicine. This practice of having a control group is important for drug trial, because it validates the results obtained. However, the control groups have also demonstrated an interesting effect, known as the placebo effect

In some drug trials, where the control group is given a fake medicine, patients start to see results. Scientists call this the placebo effect, and as of yet it is mostly unexplained. Some scientists have suggested that people get better simply because they believed they were going to get better, but this theory remains untested. Other scientists claim that unknown variables in the experiment caused the patients to get better. This theory remains unproven, as well.

Related Biology Terms

• Treatment Group – The group that receives the variable, or altered amounts of the variable.
• Variable – The part of the experiment being studied which is changed, or altered, throughout the experiment.
• Scientific Method – The steps scientist follow to ensure their results are valid and reproducible.
• Placebo Effect – A phenomenon when patients in the control group experience the same effects as those in the treatment group, though no treatment was given.

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Frequently asked questions

What is the difference between a control group and an experimental group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

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

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

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

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

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

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

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

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

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

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

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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.

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.

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.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

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

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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

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

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

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

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.

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

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

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.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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• Study Protocol
• Open access
• Published: 26 August 2024

Evaluation of the impact of an online video game as an educational intervention on sexual health and the prevention, diagnosis, and treatment of sexually transmitted infection: A randomized controlled trial protocol

• Alba Martinez-Satorres 1 , 2 , 3 ,
• Carme Roca-Saumell 1 , 3 , 4 ,
• Anna Escale-Besa 2 , 5 , 6 ,
• Marta Arcarons-Marti 1 , 2 , 3 ,
• Francisco Javier Fernandez-Segura 1 , 2 , 3 ,
• Carolina Allegra Wagner 1 , 3 , 7 ,
• Pablo Pires-Nuñez 1 ,
• Nuria Turmo-Tristan 1 , 2 ,
• Lorena Diez-Garcia 2 ,
• Andrea Maron-Lopez 1 ,
• Zulema Marti-Oltra 1 , 2 ,
• Marta Vanrell-Nicolau 1 , 2 ,
• Sonia Da Silva Torres 1 ,
• Alvaro Ruiz-Torres 1 , 3 ,
• Pablo Pino-Prieto 1 , 3 ,
• Dhyaanenshan Pillay 1 , 3 ,
• Angels Casaldaliga-Sola 2 , 5 ,
• Xavi Lazaro-Navarro 2 , 5 ,
• Maria Lasagabaster-Uriarte 1 , 3 &
• Maria Isabel Fernandez-San Martin 1 , 3  

BMC Medical Education volume  24 , Article number:  922 ( 2024 ) Cite this article

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The incidence of sexually transmitted infections (STIs) is increasing, especially among young people. Tools are needed to increase knowledge about sex education and STI prevention and treatment. Gamification can be a good training tool for both young people and health professionals. The primary objective of this study is to assess the impact of a training intervention on STI prevention, detection, and treatment in primary care professionals.

Methods/design

Multicentre cluster randomized controlled trial.

Groups of primary care professionals will receive an intervention (online video game on sex education and STIs [SEXIT]) and will be compared with control groups that will not receive the intervention. Group assignments will be randomized by clusters.

The study will consist of a pre-post evaluation of the intervention: a knowledge test will be administered before and after the intervention and 3 months after the intervention. This test will also be carried out on the same time sequence in the control groups. The impact of the training intervention will be assessed over a 6-month period, focusing on various variables associated with the clinical management of STIs. This evaluation entails the clinical records of diagnostic tests and antibiotic prescriptions related to the clinical approach to STIs.

The required sample size is 262 (131 per group).

Compared with those in the control group, improvements in knowledge and clinical behavioural outcomes after the intervention are expected for participants in the intervention groups. We plan to develop an educational video game to increase the knowledge about sexuality, STIs and violence.

Protocol registered at ISRCTN with reference number ISRCTN17783607 .

Peer Review reports

Over the last decade, there has been a clear overall increase in the incidence of Chlamydia trachomatis , gonorrhoea, syphilis, and L. venereum in Europe (Área (Área [ 1 ]); , Centro (Centro [ 2 ])). According to a study by the Barcelona STI and HIV Group published in 2019 (Sentís et al. (Sentís et al. [ 3 ])), between 2007 and 2015, the incidence of STIs significantly increased among young people aged 15–24 years, and the importance of improving programmes and interventions targeting STIs in young people is stressed. The group also found that a history of a previously diagnosed STI, being a man who has sex with men and having a greater number of sexual partners are risk factors for HIV coinfection in young people with gonorrhoea, syphilis, or lymphogranuloma; these are therefore the people targeted for screening and educational interventions.

Intimate partner violence (IPV) is associated with several high-risk sexual behaviors, including inconsistent condom use, multiple sexual partners, early sexual debut, substance use during sexual activity, and a higher prevalence of sexually transmitted infections (Seth [ 4 ]); , Stubbs and Szoeke (Stubbs and Szoeke [ 5 ])). Female IPV victims exhibit higher rates of STIs and engage in more STI-risk behaviors compared to women in non-violent relationships. Therefore, women in violent relationships should be prioritized for STI screening in clinics. Additionally, STI prevention messages should address IPV issues due to their significant impact on STI risk (Hess et al. (Hess et al. [ 6 ])).

A questionnaire for the detection of male violence was carried out on 1,566 young women aged 15 to 33 years who were users of the Youth Centre for Sexuality Care [ 7 ] between April 2017 and January 2019. According to the data extracted from the answers to this questionnaire, 5.2 out of every 10 women surveyed had suffered at least one situation of physical, psychological, or sexual violence.

The aim of this project is to design, develop and evaluate an educational intervention aimed at residents and primary care professionals to improve people’s sexual health and the prevention, detection, and treatment of STIs.

Justification

Among the possible causes of difficulty in dealing correctly with STIs may be a lack of specific training from professionals, a lack of knowledge about safer practices among the most vulnerable population, and a barrier to accessing the health system.

In this context, teaching tools that encourage active participation in learning are needed. New teaching methods have been used whereby students learn without being directly taught. Based on more participatory methods, it is not the teacher who provides the student with the information, but rather the student who learns thanks to the teaching dynamic. Gamification is an example of this. Gamification is a relatively new trend that involves applying game mechanics to nongame contexts to engage audiences, generate fun, and produce motivational and cognitive benefits. In the field of education, gamification is a formative process through which learning experiences are seen as games. It is currently one of the most attractive methodologies and has aroused great interest, topping the list of new teaching methods in terms of its effectiveness (Beemer et al. (Beemer et al. [ 8 ]); , Grimalt (Grimalt [ 9 ])).

Theoretical framework for learning with computer games for education

Transfer involves applying what you have learned in one context to solve problems or learn in a new context. Multimedia learning scenarios incorporate both words (printed or spoken) and pictures (graphics, animation, and video). Mayer’s Cognitive Theory of Multimedia Learning (CTML) explains how learning occurs in these multimedia situations, based on three key principles (Mayer and Mayer (Mayer and Mayer [ 10 ]))

Dual Channel Principle: People have separate channels for processing visual and verbal material.

Limited Capacity Principle: Each channel can process only a limited amount of material at one time.

Active Processing Principle: Deep learning occurs when people actively engage in cognitive processing during learning.

By integrating these principles (Fig.  1 ), gamified training interventions can create more engaging, effective, and efficient learning experiences that align with the cognitive processes of learners. CTML principles help ensure that gamified training aligns with how people process information, enhancing learning outcomes.

Mayer’s Cognitive Model of Multimedia Learning: this model summarizes the cognitive processes and mental representations involved in multimedia learning

A 2023 article meta-analysed 21 studies that tested the effectiveness of animated videos in improving learning in clinical and nonclinical settings compared with standard education.

Mayer’s Cognitive Theory of Multimedia Learning provided the theoretical model to frame the current analyses. Findings indicated an overall positive effect (d = 0.35) for use of animation in improving viewers’ learning across a variety of health and clinical contexts (Feeley et al. (Feeley et al. [ 11 ])).

Gamification strategies include the serious game, in which learning takes place through an organized game with a set of rules and an objective. This type of game creates a challenge, involves interaction, and has a theme or thread. It is devised specifically to promote health and, at the same time, to be fun (DeSmet et al. (DeSmet et al. [ 12 ])), promoting group integration and cohesion. Advances in technology allow these strategies to be used when face-to-face activities may not be effective.

The team game allows the application of debriefing (Maestre et al. (Maestre et al. [ 13 ])) methodology, involving a conversation to review a simulated event in which players analyse their actions and reflect on the reasoning, skills, and emotional states generated in the simulated situation to improve or maintain their performance in the future. Decisions and mistakes are reflected upon together. It helps participants not only increase their knowledge but also change their attitudes and practices by providing questions based on reflection on their own mistakes and offering opportunities for learning, reflection, and attitudinal changes.

A review of 30 studies with 3,634 participants concluded that gamification appears to be at least as effective as traditional formal teaching (Gentry et al. (Gentry et al. [ 14 ])). Moreover, it appears to be even more effective at improving learning, skills, and satisfaction. More rigorous studies of higher quality are needed to assess whether gamification can lead to real learning more effectively than traditional teaching.

Another systematic literature review was conducted to examine gamification strategies in e-Health, assessing their benefits and challenges. A total of 46 studies were thoroughly analyzed. The review found strong evidence that gamification aids cognitive development, enhancing strategic abilities, working memory, visual attention, and processing speed. Despite challenges, most studies highlighted the positive effects of gamified e-Health interventions and serious games, making typically mundane activities more enjoyable and engaging. Gamification also improved user experiences and provided extrinsic motivation and positive emotional states.

(Sardi et al. (Sardi et al. [ 15 ])). A third review of 40 studies of educational interventions using gamification with healthcare professionals also concluded that it is possible to improve learning outcomes in health profession education through gamification, especially when using game attributes that improve learning behaviours and attitudes towards learning (Van Gaalen et al. (Van Gaalen et al. [ 16 ])). High satisfaction rates and positive changes in behaviour and learning have been reported.

However, many studies had short evaluation periods, reducing result accuracy. Therefore, long-term empirical evaluations are recommended for gamified applications, especially in therapy and prevention.

Furthermore many of the reviewed studies do not compare the results with equivalent control groups, so there is a need to delve deeper into and explore theories that can explain the effects of gamified interventions with well-defined longitudinal control groups (Manterola et al. (Manterola et al. [ 17 ])). All three reviews agree that additional studies are needed in this regard.

Kirkpatrick developed an organizational model that has been used for the evaluation of training actions (Johnston et al. (Johnston et al. [ 18 ]); –Pertiñez (Pertiñez [ 20 ])). It is based on the classification of learning on four levels:

Reaction: Participants’ perceptions or satisfaction with training interventions immediately after receiving them.

Learning: knowledge and skills acquired by taking, for example, a knowledge test before and after the intervention.

Attitude or application of learning: the application of knowledge in the workplace and, consequently, any changes in service delivery. It is recommended to wait at least 3–6 weeks to evaluate this phenomenon.

Outcomes: assesses whether the learning is transferred to the clinical setting and whether it improves patient outcomes. This could be the impact of the training on the population.

Educational impact assessment provides valuable information for educators to assist in the development and improvement of teaching methods. In training activities, it is important not only to assess the impact on knowledge gain but also to determine whether this learning translates into changes in attitudes and clinical practices after the intervention (Norman (Norman [ 21 ])). To date, online learning is known to be at least as effective as traditional learning in terms of learning acquisition, but studies evaluating the third and fourth levels, i.e., the impact of educational interventions on changing practitioner attitudes and improving patient goals, are still scarce (DeSilets et al. (DeSilets et al. [ 22 ]); , Sinclair et al. (Sinclair et al. [ 23 ])).

A gamified educational intervention, the SEXIT videogame, will generate knowledge about sexuality education; access to health care; and the prevention, diagnosis, and treatment of sexually transmitted infections. It will contribute to better health care and promote better sexual health at the community level.

Main objective

The purpose of this study is to evaluate the impact of a training intervention in the form of an online video game aimed at improving sexual health and STI prevention, detection, and treatment in primary care professionals.

Specific objectives

To assess the intervention’s impact on knowledge about sexual and reproductive health.

To assess and detect behaviours and knowledge for the prevention of gender violence and/or violence based on sexual identity or orientation.

To describe the changes in the clinical management of STIs: screening and diagnostic tests performed, diagnoses and antibiotic prescriptions.

We will design, develop, and evaluate an educational intervention in the form of a video game aimed at primary care professionals. The intervention will be studied and compared with control groups that will not carry out the intervention.

Design: Design-cluster randomized clinical trial with pre-post evaluation. The PCC teams (health professionals working in a primary care centre (doctors, nurses and residents)) will be randomly assigned to the intervention or control group.

The intervention groups are doing a preintervention test, which will be repeated immediately after the intervention and then 3 months later. In the control groups, the test will be administered at the beginning of the study and repeated after 3 months. In addition to questions to assess knowledge, there will be a qualitative assessment satisfaction survey for the participants. This test will also be carried out at the same time in control groups with the same sociodemographic characteristics.

The intervention will consist of an online video game developed by a multidisciplinary team.

Study population, site participation, and recruitment

The study will be conducted in primary care centres (PCCs) managed by the Institut Català de la Salut (ICS, Catalan Health Institute), the main primary care service provider in Catalonia, with the participation of family and community medicine areas and nursing residents and professionals from PCCs of the public health system.

The study will start on April 2024 (Timeline in Table  1 ).

Recruitment of participants

The PCCs (Table  2 ) will be invited to participate in the study by training referents. Participation in the study will be proposed by the primary care training referral platform. A letter and a slide presentation will be made to explain the study. Once the PCCs who wish to participate have been selected, they will be randomly assigned to a control/intervention group (Fig.  2 ).

Timeline schedule

Assignment of intervention/control groups

The assignment will be randomized by clusters (PCC).

Once the centres have been recruited, they will be stratified and matched according to the following variables: teaching/nonteaching status, classification according to the MEDEA index, percentage of assigned population of migrant origin and number of family doctors and primary care nurses with assigned quotas.

Centres with similar characteristics will be randomly assigned to the control or intervention group. The allocation of PCCs to each group will be made by a person outside the circle of researchers using a table of random numbers.

The individuals in the control group will be able to carry out the training activity once the study will be completed.

Evaluation outcomes

Independent variables.

Educational intervention: The training activity will consist of a video game accessed online from a computer and played in teams of 4–6 people. The game consists of an online escape room where the resolution of various chained tests allows knowledge to be acquired.

Variables that may act as confounders or effect modifiers

Demographic data: gender (male/female/nonbinary), sexual orientation (heterosexual/homosexual/bisexual/asexual/don’t want to answer), and age.

Professional category (medicine resident/nursing resident/doctor/nurse).

Years worked in primary care.

Training experience in STI

Training experience in sexuality.

Variables of the PCC: teaching/nonteaching; classification according to MEDEA; percentage of assigned population of migrant origin; number of family doctors and primary care nurses with assigned quota.

Main outcome

The impact of the intervention will be assessed at three levels following Kirkpatrick’s model: reaction, learning, and clinical behaviour change.

Dependent variables

Reaction : assessment of satisfaction with the intervention. Satisfaction will be measured with a qualitative survey on the formative activity that will be administered to the intervention groups after playing the game (Table  3 ).

Knowledge change will be measured with a self-developed questionnaire. Prior to implementation, the questionnaire will be validated through evaluation by a group of health professionals with expertise in STIs and pilot testing by resident doctors and nurses who will not subsequently participate in the study. The clarity of the questions, comprehension of the instructions, length of the test, and relevance of the distractors will be evaluated. Modifications suggested in the expert judgement and pilot testing will be incorporated into the questionnaire.

The questionnaires will include:

Knowledge test (Table  4 ): 25 multiple choice questions with 4 possible answers.

The items included in the questionnaire will be prevention of gender violence and/or violence based on sexual identity or orientation and clinical approach to STI.

Evaluation of changes in the application of the knowledge acquired in clinical practice

The impact of the training intervention will be evaluated for 6 months by studying different variables related to the clinical approach to treating STIs:

Performance of diagnostic tests: multitest PCR, exudate culture, and serology.

Recording of aetiologically oriented health problems in clinical history.

Number of epidemiological surveys (data provided by the Public Health Agency).

Prescription of antibiotics.

These clinical data will be collected from health professionals during the 6 months following the intervention, both in the intervention and control groups.

Antibiotic use data will be collected from electronic prescriptions generated by health professionals.

Data collection and sources of information

The participants in each group will complete an initial knowledge test. Once the first knowledge test will be completed, the participants in the intervention group will carry out the training activity, and at the end of the activity, they will answer the knowledge test again and complete a satisfaction survey on the same platform. The control group will not complete these post-intervention questionnaires. After 3 months, the knowledge test will be repeated for both the intervention and control groups. The data will be collected and stored in the same way.

Clinical data (request for tests, recording of health problems and antibiotic prescriptions) will be extracted from the computerized medical records.

Data from epidemiological surveys of notifiable diseases will be requested from the Public Health Agency.

Study population

Inclusion criteria for participants will be as follows: family and community medicine and nursing residents; family doctors; and primary care nurses with assigned quotas.

Exclusion criteria will be: not having online gaming devices; not being able to follow up for 6 months; being an STI referral professional (STI referrers are those professionals who, after specific training, are designated as consultants with or without their own STI agenda).

Statistical analysis

Calculation of sample size.

The sample size was determined based on findings from a pilot test involving pre- and post-intervention assessments conducted on 35 health professionals. Using the mean intervention effect (the mean test score improvement) and its standard deviation to estimate the expected intervention effect and within-group variability in the pilot test, the mean improvement was 2.07, with a standard deviation of 4.07. Assuming an alpha error of 0.05, a beta error of 0.2, and a design effect factor of 2 and expecting a 20% loss to follow-up after a 6-month period, the required sample size would be 262 (131 per group).In addition, the proportion of the LGTBIQ + population was considered to carry out a subsequent analysis from a gender and LGTBIQ + perspective. To estimate the proportion of LGTBIQ + professionals, data from the IPSOS (DeSilets et al. (DeSilets et al. [ 22 ])) survey were used; 14% of the Spanish population is estimated to belong to this group. This is the same proportion obtained in the sample of the pilot test (5 individuals (14.29% of the 35 participants) identified as LGTBIQ + ..

Planned analysis

For the treatment and analysis of the data, a descriptive analysis of the variables (percentages and averages with measures of dispersion) will be carried out according to the nature of the variables.

Demographic and background data: descriptive statistics will be used (mean, median, standard deviation, range) to summarize the characteristics of the participants, including age, sex, years worked, etc. The distribution of test scores will be used to observe the distribution of pre- and postintervention test scores.

Sensitivity analysis of the instrument: Analyses will be repeated using different methods to verify the robustness of the results.

Comparison of baselines – Mann‒Whitney U or independent sample T-tests: whether the groups will be comparable in terms of demographic characteristics and baseline scores. Chi-square tests will be used to compare categorical proportions between groups (if applicable).

Analysis of the effect of the intervention: T-tests for dependent samples or Wilcoxon signed-rank tests will be used to compare pre- and postintervention values within the same group. ANCOVA (analysis of covariance): to control for confounding or baseline variables, ANCOVA could be useful. Mixed analysis of variance (ANOVA): for multiple repeated measures (for example, pre, post- and 3-month follow-up).

Cluster analysis (if applicable): Multilevel analysis or mixed models. These models will be useful for considering the hierarchical nature of clusters in the data.

Lost data management – sensitivity analysis: To determine if missing data affects the results. Imputation techniques such as multiple imputation could be used if there is a significant amount of missing data.

Limitations

The intervention to be assessed cannot be masked since it could influence its effect. Because there is a possibility of contamination bias among professionals, randomization by clusters is proposed under the assumption that the number of clusters would be sufficient for each group.

Geographic dispersion of the PCC will reduce the possibility of contamination.

Participants will know they are being assessed, which could lead to observer bias, although comparisons with a control group who will also know they are being observed may reduce the effect of this bias in the surveys. On the other hand, regarding the clinical variables of the professionals, it is thought that the observation time is long enough that the fact that they are being observed does not interfere with their clinical use.

We expect to obtain a validated educational tool that, through gamification, allows primary care professionals to increase their knowledge of sexuality, sexually transmitted infections, and the prevention and detection of violence, improving the results of the evaluation test after the educational intervention.

Expected results:

An increase in the knowledge of sexuality, sexually transmitted infections, and the prevention and detection of violence among primary care professionals, as well as an improvement in the results of the evaluation test after the educational intervention and improved results in the participants of the intervention groups compared to their control groups.

An increase in the number of diagnostic and screening tests performed after the intervention in comparison to the control group.

A better recording of diagnoses and more appropriate antibiotic treatments for the groups of professionals for whom the intervention will be carried out.

The aim of this study is to evaluate the tool used by primary care professionals But in the near future it is expected to be possible to play the game at different levels of difficulty.. The level of difficulty will depend on the videogame players: professionals, university students of health sciences or patient populations as young people.

We expect to build an educational tool that motivates active participation and facilitates acquisition of knowledge and safe behaviours related to sexual health in the short and medium term, including the prevention and detection of violence based on gender, sexual identity or orientation, access to the health system, and prevention, detection, and treatment of sexually transmitted infections. Likewise, we expect this tool to be a facilitator of health education for at-risk or vulnerable groups.

Availability of data and materials

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

Abbreviations

Lesbian, gay, bisexual, trans, intersex, queer and plus

Primary care

Primary care centre

Polymerase chain reaction

Randomized controlled trial

• Sexually transmitted infections

Área de vigilancia del VIH y conductas de riesgo. Vigilancia epidemiológica de las infecciones de transmisión sexual, 1995–2015. Madrid: Centro Nacional de Epidemiología/Subdirección General de Promoción de la Salud y Epidemiología - Plan Nacional sobre el Sida, 2017.

Centro Europeo para la Prevención y el Control de Enfermedades. Infecciones de transmisión sexual en Europa 2013. Stockholm, 2015.

Sentís A, Martín-Sánchez M, Arando M, Vall M, Barberà MJ, Ocaña I, et al. Sexually transmitted infections in young people and factors associated with HIV coinfection: An observational study in a large city. BMJ Open. 2019May 5;9(5): e027245.

Article   Google Scholar  

Seth P, DiClemente RJ, Lovvorn AE. State of the evidence: intimate partner violence and HIV/STI risk among adolescents. Curr HIV Res. 2013 Oct;11(7):528–35. https://doi.org/10.2174/1570162x12666140129103122 . PMID: 24476354. https://pubmed.ncbi.nlm.nih.gov/24476354/

Stubbs A, Szoeke C. The Effect of Intimate Partner Violence on the Physical Health and Health-Related Behaviors of Women: A Systematic Review of the Literature. Trauma Violence Abuse. 2022Oct;23(4):1157–72. https://doi.org/10.1177/1524838020985541 . ( Epub 2021 Feb 5 PMID: 33541243 ).

Hess KL, Javanbakht M, Brown JM, Weiss RE, Hsu P, Gorbach PM. Intimate partner violence and sexually transmitted infections among young adult women. Sex Transm Dis. 2012May;39(5):366–71. https://doi.org/10.1097/OLQ.0b013e3182478fa5.PMID:22504601;PMCID:PMC3856434 .

Centro Joven de Atención a las Sexualidades. https://centrejove.org/violenciamachista/

Beemer LR, Ajibewa TA, DellaVecchia G, Hasson RE. A Pilot Intervention Using Gamification to Enhance Student Participation in Classroom Activity Breaks. Int J Environ Res Public Health. 2019Oct 24;16(21):4082.

Grimalt R. Técnicas de innovación docente en dermatología. Piel. 2020; 35(8).

Mayer RE. Computer Games in Education. Annu Rev Psychol. 2019Jan;4(70):531–49. https://doi.org/10.1146/annurev-psych-010418-102744 . ( Epub 2018 Sep 19 PMID: 30231003 ).

Feeley TH, Keller M, Kayler L. Using Animated Videos to Increase Patient Knowledge: A Meta-Analytic Review. Health Educ Behav. 2023Apr;50(2):240–9. https://doi.org/10.1177/10901981221116791 . ( Epub 2022 Aug 11 PMID: 35950326 ).

DeSmet A, Shegog R, Van Ryckeghem D, Crombez G, De Bourdeaudhuij I. A Systematic Review and Meta-analysis of Interventions for Sexual Health Promotion Involving Serious Digital Games. Games Health J. 2015Apr;4(2):78–90.

Maestre JM, Rudolph JW. Teorías y estilos de debriefing: el método con buen juicio como herramienta de evaluación formativa en salud. Rev Esp Cardiol. 2015Apr;68(4):282–5.

Gentry SV, Gauthier A, L’Estrade Ehrstrom B, Wortley D, Lilienthal A, Tudor Car L, et al. Serious Gaming and Gamification Education in Health Professions: Systematic Review. J Med Internet Res. 2019Mar 28;21(3): e12994.

Sardi L, Idri A, Fernández-Alemán JL. A systematic review of gamification in e-Health. J Biomed Inform. 2017Jul;71:31–48.

Van Gaalen AEJ, Brouwer J, Schönrock-Adema J, Bouwkamp-Timmer T, Jaarsma ADC, Georgiadis JR. Gamification of health professions education: A systematic review. Adv Health Sci Educ. 2021May 31;26(2):683–711.

Manterola C, Otzen T. Estudios Experimentales 2 Parte: Estudios Cuasi-Experimentales. Int J Morphol. 2015Mar;33(1):382–7.

Johnston S, Coyer FM, Nash R. Kirkpatrick’s Evaluation of Simulation and Debriefing in Health Care Education: A Systematic Review. J Nurs Educ. 2018Jul;57(7):393–8.

Kirkpatrick DL, Kirkpatrick JD. Evaluating Training Programs. Berrett-Koehler Publishers, 1994.

Pertiñez MJ. Educación continua. AMF. 2022;18(1):8–17.

Google Scholar  

Norman G. Teaching basic science to optimize transfer. Med Teach. 2009Jan 9;31(9):807–11.

DeSilets LD. An Update on Kirkpatrick’s Model of Evaluation: Part Two. The Journal of Continuing Education in Nursing. 2018Jul;49(7):292–3.

Sinclair PM, Kable A, Levett-Jones T, Booth D. The effectiveness of Internet-based e-learning on clinician behaviour and patient outcomes: A systematic review. Int J Nurs Stud. 2016May;57:70–81.

Ipsos Global Advisor Survey. LGTB+ Pride 2023 https://www.ipsos.com/en/pride-month-2023-9-of-adults-identify-as-lgbt

Departament de Salut. RESOLUCIÓ SLT/3896/2021, de 23 de desembre. DOGC núm. 8580. https://web.gencat.cat/web/shared/OVT/Departaments/SLT/Documents/Formularis_pdf/SUBV_Resolucio_admesos_atencio_primaria_PERIS2021.pdf

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Acknowledgements

We are grateful for the financial support provided by the Strategic Plan for Research and Innovation in Health (PERIS) of the Government of Catalonia (Spain).

The research team would like to thank the City of Barcelona Multidisciplinary Teaching Unit, who supported the pilot trial; the collaboration of the Dermatology Working Group of the Catalan Society for Family and Community Medicine (CAMFiC); the Barcelona Public Health Agency (ASPB); and the University Institute for Primary Health Care Research Jordi Gol i Gurina Foundation (IDIAP Jordi Gol).

This project has major external funding, courtesy of the Department of Health of the Generalitat de Catalunya. The project obtained funding through a competitive selection process dedicated to funding research initiatives in the field of primary health care. In 2021, the Strategic Plan for Research and Innovation in Health (PERIS) 2022–2024 published a call for grants specifically aimed at research projects in primary care (ref. BDNS 604045), which led to the approval of the project through RESOLUTION SLT/3896/2021 (Departament (Departament [ 25 ])).

In addition, the project has received financial support from the semFYC private foundation. This support was extended following the award of the Isabel Fernández 2023 grant, specifically for the completion of doctoral theses.

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Alba Martinez-Satorres, Carme Roca-Saumell, Marta Arcarons-Marti, Francisco Javier Fernandez-Segura, Carolina Allegra Wagner, Pablo Pires-Nuñez, Nuria Turmo-Tristan, Andrea Maron-Lopez, Zulema Marti-Oltra, Marta Vanrell-Nicolau, Sonia Da Silva Torres, Alvaro Ruiz-Torres, Pablo Pino-Prieto, Dhyaanenshan Pillay, Maria Lasagabaster-Uriarte & Maria Isabel Fernandez-San Martin

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Contributions

AMS conceived the study and participated in its design and coordination. MFS, CRS, MAM, FFS, PPN, AEB, NTT, CAW, LDG, MLE, AML, and ZMO participated in different phases of the protocol study design. AMS wrote the final manuscript, and AEB, MFS, ART and CAW collaborated in the writing of the manuscript. All the authors have read and approved the final manuscript.

Corresponding authors

Correspondence to Alba Martinez-Satorres or Francisco Javier Fernandez-Segura .

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Ethics approval and consent to participate.

The Institut Universitari d’Investigació en Atenció Primària (University Institute for Research in Primary Health Care) – Jordi Gol i Gurina Ethics Committee approved the trial study protocol (Code 22/072-P). Written informed consent will be obtained from all professionals participating in the study.

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Martinez-Satorres, A., Roca-Saumell, C., Escale-Besa, A. et al. Evaluation of the impact of an online video game as an educational intervention on sexual health and the prevention, diagnosis, and treatment of sexually transmitted infection: A randomized controlled trial protocol. BMC Med Educ 24 , 922 (2024). https://doi.org/10.1186/s12909-024-05903-3

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A controlled experiment is one in which everything is held constant except for one variable . Usually, a set of data is taken to be a control group , which is commonly the normal or usual state, and one or more other groups are examined where all conditions are identical to the control group and to each other except for one variable.

Sometimes it's necessary to change more than one variable, but all of the other experimental conditions will be controlled so that only the variables being examined change. And what is measured is the variables' amount or the way in which they change.

Controlled Experiment

• A controlled experiment is simply an experiment in which all factors are held constant except for one: the independent variable.
• A common type of controlled experiment compares a control group against an experimental group. All variables are identical between the two groups except for the factor being tested.
• The advantage of a controlled experiment is that it is easier to eliminate uncertainty about the significance of the results.

Example of a Controlled Experiment

Let's say you want to know if the type of soil affects how long it takes a seed to germinate, and you decide to set up a controlled experiment to answer the question. You might take five identical pots, fill each with a different type of soil, plant identical bean seeds in each pot, place the pots in a sunny window, water them equally, and measure how long it takes for the seeds in each pot to sprout.

This is a controlled experiment because your goal is to keep every variable constant except the type of soil you use. You control these features.

Why Controlled Experiments Are Important

The big advantage of a controlled experiment is that you can eliminate much of the uncertainty about your results. If you couldn't control each variable, you might end up with a confusing outcome.

For example, if you planted different types of seeds in each of the pots, trying to determine if soil type affected germination, you might find some types of seeds germinate faster than others. You wouldn't be able to say, with any degree of certainty, that the rate of germination was due to the type of soil. It might as well have been due to the type of seeds.

Or, if you had placed some pots in a sunny window and some in the shade or watered some pots more than others, you could get mixed results. The value of a controlled experiment is that it yields a high degree of confidence in the outcome. You know which variable caused or did not cause a change.

Are All Experiments Controlled?

No, they are not. It's still possible to obtain useful data from uncontrolled experiments, but it's harder to draw conclusions based on the data.

An example of an area where controlled experiments are difficult is human testing. Say you want to know if a new diet pill helps with weight loss. You can collect a sample of people, give each of them the pill, and measure their weight. You can try to control as many variables as possible, such as how much exercise they get or how many calories they eat.

However, you will have several uncontrolled variables, which may include age, gender, genetic predisposition toward a high or low metabolism, how overweight they were before starting the test, whether they inadvertently eat something that interacts with the drug, etc.

Scientists try to record as much data as possible when conducting uncontrolled experiments, so they can see additional factors that may be affecting their results. Although it is harder to draw conclusions from uncontrolled experiments, new patterns often emerge that would not have been observable in a controlled experiment.

For example, you may notice the diet drug seems to work for female subjects, but not for male subjects, and this may lead to further experimentation and a possible breakthrough. If you had only been able to perform a controlled experiment, perhaps on male clones alone, you would have missed this connection.

• Box, George E. P., et al.  Statistics for Experimenters: Design, Innovation, and Discovery . Wiley-Interscience, a John Wiley & Soncs, Inc., Publication, 2005. 
• Creswell, John W.  Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research . Pearson/Merrill Prentice Hall, 2008.
• Pronzato, L. "Optimal experimental design and some related control problems". Automatica . 2008.
• Robbins, H. "Some Aspects of the Sequential Design of Experiments". Bulletin of the American Mathematical Society . 1952.
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Nutritional Impacts of Dietary Selenium, Iodine and their Interaction on Egg Performance, and Antioxidant Profile in Laying Longyuan Duck Breeders

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• Md Touhiduzzaman Sarker   na1   nAff2 ,
• Xiuguo Shang   na1   nAff1 ,
• Wei Chen 2 ,
• Runsheng Xu 2 ,
• Shuang Wang 2 ,
• Weiguang Xia 2 ,
• Yanan Zhang 2 ,
• Chenglong Jin 2 ,
• Shenglin Wang 2 ,
• Chuntian Zheng 2 &
• Abdelmotaleb Elokil 2 , 3 , 4  

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The present study aimed to optimize the combined effect of dietary selenium (SE) and iodine (ID) on the productive and reproductive performance and antioxidant capacity of Longyuan breeding ducks. A total of 288 Longyan duck breeders aged 20 wk were randomly assigned to four groups with six replicates ( n  = 72 ducks/group; 12 ducks/replicate). A 2 × 2 factorial arrangement experiment was performed and included 2 supplementation levels of each SE and ID for 200 days of the experimental period. The first group (SE0/ID0) received a basal diet without SE or ID supplementation and was considered to be the control group, whereas the other three groups, SE0/ID4, SE2/ID0 and SE2/ID4, received a basal diet supplemented with 0.4 mg ID/kg, 0.2 mg SE/kg or 0.2 mg SE supplemented with 0.4 mg ID/kg, respectively. The results indicated that the albumin height of the SE2/ID0 group was lower (P < 0.05) than that of the control group, that the egg shape index of the SE2/ID4 and SE0/ID4 groups were lower (P < 0.05) than that of the control group (SE0/ID0), and that the SE concentration significantly increased (P < 0.05) in the SE2/ID0 and SE2/ID4 groups. Hatchability and embryonic mortality improved (P < 0.05) in the SE2/ID0 group. Plasma GSH-Px activity was increased (P < 0.05) by reducing the concentration of malondialdehyde (MDA) in the SE groups. In addition, the tibia length significantly increased (P < 0.05) in the ID (SE0/ID4 and SE2/ID4) groups compared with that in the control group, the plasma content of IGF-1 in the SE2/ID4 and SE0/ID4 groups were greater (P < 0.05) than that in the control group, and the bone mineral content increased (P > 0.05) in the SE2/ID0 and SE0/ID0 groups. Compared with those in the other groups, the mRNA expression of antioxidant-related genes, including Nrf2 and SHMT1 in the SE2/ID4 group was upregulated (P > 0.05), especially in the SE2/ID4 group. Overall, dietary treatment with SE2/ID4 (0.2 mg SE in 0.4 mg ID/kg diet) could be a suitable feed supplement for improving the the egg quality, health status, endogenous antioxidant content, antioxidant-related gene expression and pre-hatching quality of Longyuan duck breeders.

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Introduction

Selenium (SE) and iodine (ID) are two essential trace elements (ETEs) that play important roles in animal performance and health status by regulating several biological functions, such as endogenous antioxidants, cell proliferation, protein metabolism, thyroid secretions and reproductive organ development [ 1 , 2 ]. Both SE and ID are necessary to regulate growth and bone mineralization in meat ducks as well as reproductive performance and the egg-laying rate in duck layers and duck breeders [ 3 ]. In addition, Xia , et al . [ 4 ] reported a positive effect of dietary inclusion at concentrations of 0.11 mg, 0.19 mg, 0.27 mg, 0.35 mg, 0.43 mg and 0.51 mg Se/kg diet on egg quality traits, SE deposition efficiency in eggs, egg fertility and Gpx1 activity in erythrocytes and liver in Longyan duck breeders. Xia , et al . [ 5 ] reported significant effects of a diet supplemented with 0.16 mg SE/kg on the growth performance and antioxidant capacity of maternal and progeny ducklings. A study of laying ducks by Chen , et al . [ 6 ] reported that the optimal concentration for optimal daily egg production was within the range of 0.18 mg to 0.24 mg SE/kg feed during the early to peak laying period. Earlier research revealed that low levels of 0.125 mg SE/kg did not affect the egg quality of Cherry Valley ducks [ 7 ]. In addition, growth performance, mortality, immunological parameters, hematological variables, cytokine production and histopathological data were significantly different after vaccination of Pekin ducklings with a diet supplemented with 0.3 mg Nano-SE/kg [ 8 ]. Li et al. [ 1 ] reported that diets supplemented with 0.15 mg and 0.30 SE/kg enhanced the production performance, egg quality, egg selenium content, antioxidant capacity, immunity and selenoprotein expression in Hyland Brown laying hens. Dietary supplementation of 2 mg SE/kg diet to Lohmann pink-shell laying hens improved performance and egg quality by enhancing the antioxidant activity of T-AOC, GSH-PX and SOD and reducing the MDA concentration [ 9 ].

Likewise, ID is associated with the development of hypothyroidism and thyroid autoimmunity in laying poultry due to the presence of salt or mineral premixes in the diet, such as sodium iodate, calcium iodate and potassium iodate [ 10 , 11 ]. It is involved in the biosynthesis of the hormones IGF-1 (insulin-like growth factor-1), T3 (triiodothyronine) and T4 (thyroxine) for regulating the basal metabolic rate (BMR), thermoregulation, muscular function and intermediary metabolism [ 12 , 13 ]. Röttger , et al . [ 14 ] revealed that diets supplemented with 0.44 mg ID/kg had a marked effect on egg production performance in laying hens. However, Christensen and Ort [ 15 ] reported that diets supplemented with 2.1 mg ID/kg potassium iodide had notable differences in terms of the egg albumen index and Haugh unit in laying turkeys. An antioxidant study revealed that dietary ID may enhance glutathione peroxidase 3 (GPx3) and glutathione peroxidase 4 (GPx4) mRNA expression through modulation of H 2 O 2 production for thyroid hormone synthesis rather than exerting a protective effect against oxidative cellular damage [ 16 ]. Dietary inclusion of 5 mg ID/kg in broiler diets for 42 days improved performance, carcass characteristics, meat iodine, thyroid hormones and some blood indices to enrich broiler meat [ 17 ]. The addition of 1 mg, 3 mg or 5 mg ID/kg diet did not negatively affect the structure or function of the thyroid gland or the immunoglobulin concentration in laying hens [ 18 ].

Notably, bioactive micronutrients, such as SE and ID, regulate many key metabolic pathways in the body. SE is nutritionally essential for animals and is a constituent of more than two dozen selenoproteins that play critical roles in reproduction, thyroid hormone metabolism, DNA synthesis and protection from oxidative damage and infection. ID is responsible, above all, for proper functioning of the thyroid gland and the hormones it secretes, which, in turn, determine the correct course of many metabolic processes. To our knowledge, no previous studies have evaluated the nutritional impacts of dietary selenium, iodine and their interaction on the performance of breeder ducks. It is hypothesized that the dietary addition of SE, ID and their interaction is expected to exert beneficial effects on egg performance, antioxidant capacity and immune gene expression in Longyuan breeder ducks. Therefore, the present study was designed to evaluate the impacts of dietary inclusion of 0.2 mg SE/kg diet, 0.4 mg ID/kg diet and their interaction of two levels for 200 days of the experimental period on egg production, egg quality, antioxidant capacity and immune gene expression in Longyuan duck breeders.

Materials and Methods

Animals, diets and management.

All procedures employed in this study were approved by the Animal Care and Use Committee of Guangdong Academy of Agricultural Sciences, Guangzhou, China (ACUCGAAC2019). A total of 288 Longyan duck breeders aged 20 wk with the same genetic background and comparable body weights (1.55 ± 0.01 kg) were assigned to the 2 × 2 factorial design, which included four groups (n = 72/group); each group with six replicates (n = 12/replicate) continued over the subsequent 200 days of the experimental period. During the experiment, laying ducks were reared in cages with free access to water and 170 g/bird/d feed distributed twice per day. The farm provided sufficient lighting, and the daily temperature, humidity, and duck health status were recorded. The experimental diet for laying ducks was formulated according to the National Research Council [ 19 ]. For the chemical analysis, the nitrogen content of the feed sample was determined using the Kjeldahl method (Kjeltec™ 9, FOSS, Denmark), and crude protein was calculated as N × 6.25. The mineral (calcium and total phosphorus) feed samples were ashed at 600 °C for 12 h in a muffle furnace. To determine the calcium and total phosphorus in the diet, 2 g of each feed sample was collected as ash. The samples were heated overnight at 600 °C for 12 h using a muffle furnace until ash was obtained. The Ca and P contents of the minerals were determined by dry-ashing samples. The formulated basal diet composition and nutritional levels of the egg-laying ducks are presented in Table  1 .

Dietary Supplements with SE and ID

For the diet supplementation, sodium selenite and calcium iodate were purchased from Changsha Jiebao Biotechnology Co., Ltd. The experimental diets were supplemented with 0.24 mg SE/kg diet of sodium selenite and 0.40 mg ID/kg diet of calcium iodate as SE2 and ID4 treatments, respectively. The results of chemical analysis for the concentration of SE and ID in the diet were 0.11 mg SE/kg (SE0), 0.35 mg SE/kg (SE2), 0.16 mg ID/kg (ID0) and 0.56 mg ID/kg (ID4). The SE concentration in the treatment diets was analyzed at the Mineral Laboratory, Institute of Animal Science, Guangdong Academy of Agricultural Sciences (Guangzhou, China), according to the methods proposed by Olson et al. [ 20 ]. Briefly, approximately 1 g of the feed sample was digested for about 2 h at 200 °C with a nitric acid and perchloric acid solution at a ratio of 5:3 (v/v). Then add 5 mL of hydrochloric acid solution to the mixture and heat to ensure the removal of left of the organic elements. After the cooling process, 20 mL of EDTA and 3 mL of 2, 3-DiAminoNaphthalene were added and heated to the mixture for 5 min. After that, 4 mL of cyclohexane was added and mixed properly by shaking. The supernatant was measured by fluorescence method using a spectrophotometer (Tokyo, Japan). The iodine content in treatment diets was analyzed by inductively coupled plasma mass spectrometry (ICP-MS, Perkin Elmer, Elan 6000, and Toronto, Canada) according to Benkhedda et al. [ 21 ]. Briefly, the dietary feed sample (5 g) was boiled for 30 min of alkaline digestion using an ammonia solution (0.59 mol/L). The feed sample was diluted 1:5 ratio with Tetramethylammonium hydroxide (TMAH) and distilled deionized water. Then iodine was extracted from the dietary sample using a sealed container at 90 °C for 3 h. After the cooling process add 14 ml of deionized water, then centrifuge at 4000 rpm for 15 min. For the determination of iodine concentration, 0.5 ml of the supernatant was taken, and during calculation, the standard addition calibration method was applied.

Productive Performance

Egg number (EN), egg weight (EW) and feed intake (FI, the difference between supplied feed refusal feed) were recorded daily for 200 d of the experimental period. The percent of egg production rate (EPR, %), average egg weight (AEW, g), egg weight per day (EWPD, g/d), egg mass (EM, g egg/bird/day) and feed conversion ratio (FCR, g feed:g egg) were calculated for each replicate for 200 d of the experimental period. Thirty-six eggs from each group (three eggs/replicate) after 100 d (n = 18 eggs/group) and 200 d (n = 18 eggs/group) of treatment were collected randomly for egg quality assessment. Egg quality variables, including albumin height (AH, mm), Haugh unit (HU), the egg shape index (ESI), eggshell strength (ESS) and eggshell thickness (EST, mm), were measured by standard methods. The breaking strength of normal eggs was determined on the vertical axis using an egg force reader (ORKA Food Technology, Ramat Hasharon, Israel). Then, the weights of the eggs were recorded individually and broken onto a flat surface to measure the albumen height, and Haugh units were measured with an egg analyzer (model EA-01, ORKA Food Technology, Ramat HaSharon, Israel). The yolk, albumen and shell (air-dried for 24 h) were weighed individually and are expressed as percentages of the total egg weight. Eggshell thickness was measured based on three pieces of shell without membranes from the blunt, mid-length and pointed ends using a digital micrometer and was averaged.

Plasma Antioxidant Variables

At the end of the experiment, 2 ducks close to the average body weight were randomly selected from each replicate, and blood samples from the wing vein were collected in anticoagulated test tubes. After standing for 30 min for blood collection, the samples were centrifuged at 3,000 rpm/min for 10 min at 4 °C to separate the supernatant and stored at -80 °C until further analysis. After bloodletting, the ducks were euthanized by cervical dislocation. The concentrations of plasma malondialdehyde (MDA), glutathione peroxidase-Px (GSH-Px), total superoxide dismutase (T-SOD), total antioxidant capacity (T-AOC), oxidized low-density lipoprotein (Ox-LDL) and 8-hydroxydeoxyguanosine (8-OHdG) were analyzed by spectrophotometry using commercial kits according to the instructions provided by the Nanjing Jiancheng Bioengineering Institute (Nanjing, Nanjing, China).

Plasma IGF-1 and Thyroid Hormone Indicators

The concentrations of plasma insulin-like growth Factor 1 (IGF-1), triiodothyronine (T3) and thyroxine (T4) were determined by radioimmunoassay (North Institute of Biology and radioimmunoassay Co., Ltd., China) according to the instructions described previously [ 22 ].

Determination of Se and I Contents in Egg Yolk

To measure the selenium and iodine concentrations in duck egg yolks, 3 eggs for each replicate and 12 eggs per treatment were randomly chosen for this assay before the end of the 200 d experimental period. The egg yolk was separated from the egg and freeze-dried at -50 °C for 72 h in a Christ ALPHA 1–2 LD plus freeze-drying machine (Marin Christ, Osterode, Germany). The dried egg yolk was smashed by an FW 100 high-speed grinder (Taisite Instrument Co., Ltd. Tianjin, China). Approximately 0.5 g of yolk dry mash was digested with a mixture of 5 mL of HNO3 (Sigma‒Aldrich, MO, USA) and 2 mL of H2O2 (EMSURE® ISO, Merck, Germany). After the digest cooled, a final volume of 10 mL was diluted with deionized water. The concentrations of Se and I were determined based on fluorometric analysis. The deposition of Se and I in the duck egg yolk was expressed as mg/g dry matter relative to the daily feed intake.

Artificial Insemination, Fertility and Hatchability

Ten days before the end of the experiment, all birds were artificially inseminated twice (at 3 d intervals) with 100 mL of diluted fresh semen (diluted with 0.9% saline solution at a ratio of 1:1 vol/vol). The semen samples were collected from drakes of the same breed. An average egg weight > 63 g per replicate (without soft or cracked shells, double yolks or dirtiness) accumulated from the first insemination to the end of these tenth day. All of the eggs were incubated in the same incubator (Bengbu Sanyuan Incubation Equipment Co., Ltd., Anhui, China) at 37.2 °C to 38.0 °C and 60 to 75% relative humidity for 28 d. The eggs were turned 12 times/d throughout the incubation period and sprayed with water once daily from the 15th day of incubation until they hatched. Egg fertility was checked by candling on the seventh day of incubation. After 28 d, the healthy hatched ducklings were counted and recorded, and the number of eggs that failed to hatch was calculated as the percentage of mortality. Finally, the percent hatchability and body weight at hatch (BWH, g) were recorded for the experimental groups.

Tibia Quality Assessment

The tibia samples were collected and stored at -20 °C until further analysis. Then, the collected tibiae were weighed after the muscles and tendons were removed at the end of the investigation. The left and right tibias were separated, and the following tibial quality indicators were measured. The length, weight and bone density were measured using the right tibia following [ 23 ]. In addition, the bone mineral content was determined by Guangzhou Overseas Chinese Hospital with an X-ray osteodensitometer (Lunar Prodigy; General Electric Company, Fairfield, CT).

Relative Expression of Genes Related to Hepatic Antioxidant Activity and Immunity: RNA Extraction and Real-Time Quantitative PCR

To detect the relative expression of heme oxygenase ( HO-1 ), nuclear factor erythroid 2 related Factor 2 ( Nrf2 ), superoxide dismutase ( SOD ), catalase ( CAT ), glutathione peroxidase 4 ( GPx4 ), serine hydroxy methyl transferase 1 ( SHMT1 ) and DNA methyl transferase 1 ( DNMT1 ) by qPCR, total RNA was isolated from frozen liver samples using a TRIzol reagent kit (Invitrogen, Carlsbad, CA) according to the manufacturer’s instructions. Then, the concentration and purity of the RNA were determined using a spectrophotometer (Takara, Biotechnology Co., Ltd., Dalian, China) and a NanoDrop ND-1000 UV spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA) to determine the purity of the RNA between the absorbance values at 260/280 nm. Next, reverse transcription was performed using 1 µg of total RNA for cDNA synthesis with a kit (Promega, Madison, WI), and cDNA was amplified in a 20 µg reaction mixture according to the manufacturer’s instructions (Takara, Beijing, China). Quantitative real-time PCR was performed using a Bio-Rad iQ5 Real-Time PCR detection system (Bio-Rad, San Diego, CA). The reaction program was 95 °C for 30 s of predenaturation, followed by 40 cycles, with each cycle consisting of denaturation at 95 °C for 5 s and annealing and extension at 60 °C for 30 s. All samples were run in triplicate, and the amplification products were verified by a standard curve. The primer sequences are presented in Table  2 . The housekeeping gene β-actin was selected to standardize the expression of other target genes. The relative mRNA expression levels of the target genes (fold changes) were analyzed by the 2 −ΔΔCt method [ 24 ] after normalization against the reference housekeeping gene β-actin.

Statistical Analysis

The data from the present work were statistically analyzed on a 2 × 2 factorial basis according to the following model: Y ijk  = µ + S i  + D j  + SD ij  + e ijk , where Y ijk  = an observation, µ = the overall mean, Si = effect of SM levels ( i  = 0.2 mg and 0 mg), Dj = effect of ID levels ( j  = 0.4 mg and 0 mg), SD ij  = the interaction between levels of SE and ID and e ijk  = random error according to the GLM procedure of SAS (SAS 9.1., Statistical Analysis Systems Institute). Differences among means within the same factor were tested using Duncan’s new multiple range test, and statements of statistical significance are based on P  ≤ 0.05. The data are presented as the means ± standard errors (SEMs), with different superscript letters indicating significant differences ( P  < 0.05).

Egg Performance

The effects of dietary selenium, iodine and their interaction on egg performance traits in Longyan breeding duck breeders are shown in Table  3 . No significant differences were detected in the EPR (%), AEW (g), EWPD (g/d), ADFI (g) or FCR (g: g) between the groups (Table  3 ). For egg quality traits, the average AH (mm) was significantly lower (P < 0.05) in the SE2/ID0 group than in the SE0/ID0 group, whereas the average ESI was significantly greater (P < 0.05) in both the SE2/ID0 and SE0/ID0 groups than in the other groups (Table  4 ). There were no significant differences in the average HU, ESS or EST (mm) among the experimental groups. However, the results reveal significant differences (P < 0.05) due to the effect of ID4 supplementation in increasing AH and HU, and I concentration in egg yolk, and decreasing ESI. The SE content in the yolk was significantly greater (P < 0.05) in the SE2/ID4 group than in the SE0/ID0 group, whereas there were no significant differences in the ID content in the yolk among the experimental groups (Table  4 ). SE supplementation increased (P < 0.05) the SE concentration in egg yolk and decreased ID concentration of egg yolk. No significant differences were detected in the percentage of fertility; however, there were significant increase in the percentage of hatchability eggs among the experimental groups, as shown in Table  5 .

Antioxidant Indices

The results of the plasma antioxidant indices are summarized in Table  6 . There was no significant differences in the average of plasma MDA concentration; however, the lowest MDA concentration was measured with the SE2/ID4 group. In addition, plasma GSH-Px activity was significantly greater ( P  < 0.05) in the SE2/ID4 group than in the SE0/ID0 group. However, there were no significant differences among the groups in terms of plasma T-SOD, T-AOC, Ox-LDL or 8-OHdG activity, as shown in Table  6 .

Relative Expression of Antioxidant and Immune genes

The relative mRNA expression levels of the HO-1 , Nrf2 , SOD , CAT , GPx4 , SHMT1 and DNMT1 genes are presented in Table  7 . The expression of the Nrf2 and SHMT1 genes was significantly greater (P < 0.05) in the SE2/ID4 group than in the SE0/ID0 group. On the other hand, no significant differences were detected in the relative mRNA expression levels of the HO-1 , SOD , CAT , GPx4 and DNMT1 genes among the experimental groups, as shown in Table  7 .

Tibia Quality Traits

The effects of dietary selenium, iodine and their interaction on tibia quality traits (tibia weight, length, bone density and bone mineral content) in Longyan breeding duck breeders are shown in Table  8 . The average tibia length (mm) was significantly greater (P < 0.05) in the SE2/ID4 group than in the SE0/ID0 group; however, the average bone mineral content was significantly greater (P < 0.05) in the SE0/ID0 and SE2/ID0 groups than in the SE2/ID2 group (Table  8 ). No significant differences in tibia weight (g) or bone density were detected among the experimental groups, as shown in Table  8 .

Plasma Concentrations of IGF-1, T3 and T4

The effects of dietary selenium, iodine and their interaction on the plasma concentrations of IGF-1, T3 and T4 in Longyan breeding ducks are presented in Table  9 . The data indicate that the plasma concentration of IGF-1 (ng/mL) significantly increased (P < 0.05) in the SE2/ID4 group compared to that in the SE0/ID0 group, whereas the plasma concentration of T3 (ng/mL) significantly increased (P < 0.05) in the SE0/ID0 group compared to that in the SE2/ID4 group. On the other hand, there were no significant differences in the plasma concentration of T4 (ng/mL) among the experimental groups (Table  9 ).

The efficient and optimized dietary supplementation of trace minerals such as selenium (SE) and iodine (ID) in laying ducks is highly beneficial in the duck farming industry because it results in the best production performance. The supplemented SE in the diet easily mobilized to meet the breeding requirements; therefore, an adequate amount of SE was deposited efficiently on the breed eggs for better hatchlings. Long , et al . [ 25 ] reported that dietary supplementation of laying chicken diets with 0.1 mg/kg methionine selenium can significantly improve the egg fertilization rate and hatchability. Selenium depletion leads to a reduction in the deposition of SEs in bird eggs and tissues for the development of bird embryos and simultaneously damages the antioxidant defense system through the production of free radicals (O − ) and reactive oxygen species (ROS), thereby leading to the oxidation of the lipid profile, low hatchability and embryo mortality [ 26 , 27 ]. Iodine is an important trace element in the animal diet that affects various physiological functions by participating in the production of thyroid hormones. The ID-enriched eggs are noticeably influenced by the ID intake of breeding poultry [ 10 , 11 ]. ID inadequacy disrupts normal thyroid function and can lead to thyroid hyperplasia, also known as thyroid dysplasia. The thyroid is an essential hormone for the formation and expansion of bones [ 17 ].

In the current results, the effect of dietary selenium, iodine and their interaction on fertility was not significant, but there was a significant differences in the percentage of hatchability among the experimental groups. These findings are in agreement with those of a previous study, which reported no significant effect on egg production parameters due to dietary supplementation with 0.23 to 0.46 mg/kg sodium selenite [ 28 ]. Likewise, some other studies in chicken breeders and laying poultry showed similar results in terms of production parameters when selenium was supplemented in their diet [ 29 , 30 ]. In contrast to previous findings, the concentrations of 0.24 mg/kg SE in laying duck diets are effective for egg production each day at the beginning and peak laying times [ 6 ]. The inconsistency of results and differences in production performance might be related to the animal model, combined effects of SE and ID, and the rearing environment.

Our results revealed that SE and ID supplementations did not improve AH, ESI and HU in compared to the control group (SE0/ID0) due to their non-significant effects on the average of EPR and AEW. Şekeroǧlu and Altuntaş [ 31 ] reported that a positive correlation between egg weight and egg quality characteristics especially AH and ESI when evaluated the effect of egg weight on the total egg quality characteristics (albumen height, shape index, shell thickness, albumen index, Haugh unit, yolk height, yolk index and yolk color) in different egg weight groups. This finding is in agreement with a previous study, which revealed that supplementation of SE in laying hens increases the egg albumen height and egg shape index but does not affect other traits of the egg quality index [ 32 ]. Duck eggs deposit a high amount of SE, mostly from egg yolk and albumen, which is used for embryo development prior to hatching [ 33 ]. The deposition of SE (inorganic selenium) in the egg yolk in the present study showed that the SE2/ID0 and SE2/ID4 supplementation groups exhibited greater increases in the SE2/ID0 concentration than did the other two groups. The results indicate that SE enrichment in eggs leads to an increase in egg shelf-life and the defense of the antioxidant system by reducing the production of free radicals (O − ) and reactive oxygen species (ROS) while improving hatchability and chicken embryo mortality [ 4 , 34 ]. The mechanism of selenium of selenite supplementation for increasing egg yolk SE content is based on enhancing the concentration of SE in plasma, which leads to an increase in the bioavailability of SE for accumulation in egg yolk, and adding 0.5 mg SE/kg diet significantly increased deposition of SE content in egg yolk of laying hens [ 35 ]. In addition, the results showed that SE2 supplementation was decreased the concentration of ID in egg yolk, and dietary inorganic SE, especially in combination with ID, can enhance the concentration of SE in egg yolk [ 36 ].

Indices such as hatching egg weight, fertility, hatchability, dead embryo rate and hatching weight of ducklings directly reflect the reproductive performance of breeding poultry. Our current results show the beneficial effect of supplementation with SE (0.24 mg/kg) in laying ducks in the SE2/ID0 group, which led to an improved hatchability percentage with a low embryo mortality rate, which may provide evidence of improved reproductive performance in the prehatch period of Longyuan breeding ducks. Compared with the control group (SE0/ID0), positive effects of SE2/ID0 and SE2/ID4 were recorded but there was no effect of SE0/ID4 on hatchability and mortality. Dietary supplementation with different selenium, zinc, and iron sources were enhanced the internal egg quality characteristics (shell thickness, shell weight per unit surface area, yolk color, and yolk index) associated with increasing the hatchability and decreasing the embryonic mortality of laying hens [ 37 , 38 ]. In addition tibia length was increased in the SE2/ID4 group with decreasing bone mineral due to withdrawal of mineral elements from the bones to form the tibia. The negative association between tibia length and bone mineral content was measured in the SE2/ID4 and SE0/ID4 groups, and tibia length was negatively associated with fracture strength due to decreased bone mineralization [ 39 ]. The supplementation dose is close to the commercial poultry nutrition feeding program for ducks and geese, which recommends 0.30 SE mg/kg diet for breeding ducks. In addition, in the present study, supplementation with 0.4 ID mg/kg diet significantly affected hatching performance parameters, indicating that ID inadequacy affects the egg hatching rate and increases the embryo mortality rate of Longyan Shelducks. Early studies revealed that high levels of ID (≥ 6.25 mg/kg) in breeding poultry increased embryo mortality, decreased the hatchability of fertilized eggs and prolonged hatching time [ 25 , 32 ], which might be due to toxic and pathogenic effects caused by excessive ID concentrations. In contrast, two different commercial laying turkey strains fed (0.7 and 4.2 mg/kg of ID) in their diet positively influenced hatching performance in the breeding phase[ 40 ].

The present study showed that dietary supplementation of SE and ID to the diet of duck breeder decreased plasma MDA concentrations and increased GSH-Px activity in the SE groups. Reduced MDA levels indicate decreased lipid peroxidation, which is a marker of disruption of cellular homeostasis and oxidative stress. These findings are in agreement with earlier research by Jing , et al . [ 41 ], who reported that dietary supplementation with 0.28 SE mg/kg in laying hens significantly increased the activities of the antioxidant enzymes GSH-Px and T-SOD and decreased the MDA concentration. Selenium is a crucial component of the antioxidant enzyme glutathione peroxidase-Px (GSH-Px), which alleviates the potential damage of lipid peroxides and hydrogen peroxide (H 2 O 2 ) [ 42 ]. The results of the antioxidant study indicated that SE deficiency in the breeder diet caused the accumulation of free radicals and reactive species in cells, reducing endogenous capacity and cellular immune defense [ 26 , 27 ]. In addition, SE and ID have a significant interactive effect on the antioxidant index, and SE plays an important role in the synthesis of deiodinase, which is a family of selenoenzymes, GSH-Px, that are crucial for the activation and inactivation of thyroid hormones at the cellular level [ 43 ]. Thyroid hormone homeostasis improves thyroid gland function, leading to upregulated relative mRNA expression of the Nrf2 , SOD and SHMT1 genes in the SE2/ID4 group, Nrf2 regulates the antioxidant and cytoprotective response in the thyroid through the abundance of iodinated thyroglobulin regulation. It is already clear that Nrf2 regulates some thyroid cell functions, including antioxidant defense, iodine metabolism, protection against thyroid autoimmunity, promotion of thyroid cell survival, etc.[ 44 ]. In addition ID4 supplementation decreased Ox-LDL expression, which plays a central role in atherosclerosis by acting on multiple cells such as endothelial cells, macrophages, platelets, fibroblasts and smooth muscle cells through LOX-1. These findings are in agreement with those of Xiao et al. [ 27 ], who reported that broiler breeders supplemented with 0.04 mg SE/kg in their diet exhibited increased antioxidant immune-related mRNA expression in chicken plasma. Nrf2 is a crucial transcription factor that controls the expression of the SOD and SHMT1 genes. Also the result of I supplementation increased expression of SHMT1 genes which are related to antioxidant enzymes and required for the formation of new DNA and RNA, promoting healthy cell division which is especially important in the immune system. [ 45 ]. Another study by Wu , et al . [ 46 ] reported that the Nrf2 pathway has an antioxidant effect on regulating endogenous protective genes.

Our results revealed that the plasma concentration of T3 was decreased when ducks received dietary SE and ID supplementations, inhibiting the production of TSH in the anterior pituitary gland. As concentration of T3 hormones decrease, the anterior pituitary gland increases production of TSH, and by these processes, a feedback control system stabilizes the level of thyroid hormones in the bloodstream [ 47 ]. In addition, the findings of this study demonstrated that dietary supplementation with ID increased the tibia length of laying ducks in the ID groups compared with those in the other groups. Dietary SE and ID supplementation increase the bone mineral content, but it is well known that these two trace elements have combined effects on the bone metabolism of breeder poultry. Cao , et al . [ 48 ] reported that SE inadequacy in the breeder diet has a detrimental effect on bone microarchitecture, which plays a crucial role in eggshell development and potentially decreases antioxidant capacity. Osteoporosis deficiency in SE and ID has been associated with effects on bones and joints, leading to decreased bone quality and tibial length in breeding ducks. In contrast, a study revealed that SE deficiency significantly decreased tibia length, bone density and bone mineral content [ 49 ]. Breeding poultry bone mineralization and bone development also affect the earliest stage of embryonic development [ 39 ]. Insulin-like growth factor-1 (IGF-1) is an active protein polypeptide that plays a vital role in animal growth, protein metabolism and glucose, lipid and bone metabolism [ 50 , 51 , 52 ]. Our results showed that supplementation of ID in the breeding ducks significantly increased plasma IGF-1 growth hormone levels in the ID groups. Our findings support the findings of a previous study in turkeys, in which ID supplementation significantly increased the plasma IGF-1 concentration compared to inadequate ID [ 53 ]. IGF-1 is one of the principal mediators of growth factors, promoting animal growth and regulating substance metabolism [ 54 ]. In addition, the effects on dietary levels of SE and ID in breeding ducks interact with plasma IGF-1 hormone concentrations. Thyroid hormones (T3 and T4) can influence the synthesis and biological effects of growth factors (IGF-I and IGFBP-3) on target tissues. Thyroid hormones dysfunction negatively affects the growth of children with iodine deficiency, and this effect may be mediated, through effects on these growth factors status. Therefore, treatment of iodine deficiency significantly increased IGF-I and IGFBP-3 concentrations, which improved somatic growth [ 55 ].

In conclusion, the dietary supplementation of SE and ID (0.2 mg SE/kg diet and 0.4 mg ID/kg diet) in the breeding ducks had no effect on egg production parameters with the exception of AH and SE concentration in egg yolk. In addition, duck breeders fed with the SE2/ID4 diet lower the plasma MDA concentration, greater the GSH-Px activity to enhance the antioxidant capacity, and increase the Nrf2 and SHMT1 gene expression. The SE2/ID4 diet elongated the average tibia length and plasma IGF-1 hormone concentration compared to other diets. Further investigation needs to be required to examine the SE and ID and their interactive effects on production, reproduction performance, and antioxidant capacity during the laying phase.

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Li Y, Mu T, Li R, Miao S, Jian H, Dong X, Zou X (2024) Effects of different selenium sources and levels on the physiological state, selenoprotein expression, and production and preservation of selenium-enriched eggs in laying hens. Poult Sci 103:103347. https://doi.org/10.1016/j.psj.2023.103347

Article   CAS   PubMed   Google Scholar  

Adefegha SA (2024) Trace Elements in Nutrition. In: Oladiji AT, Oladele JO, Ajay EIO (eds) In: Nutrition and Diet in Health, 1st edn. CRC Press, Boca Raton, pp 16–20

Chapter   Google Scholar  

Wang H, Gao W, Huang L, Shen JJ, Liu Y, Mo CH, Yang L, Zhu YW (2020) Mineral requirements in ducks: an update. Poult Sci 99:6764–6773. https://doi.org/10.1016/j.psj.2020.09.041

Article   CAS   PubMed   PubMed Central   Google Scholar  

Xia W, Chen W, Abouelezz K, Ruan D, Wang S, Zhang Y, Fouad A, Li K, Huang X, Zheng C (2020) The effects of dietary Se on productive and reproductive performance, tibial quality, and antioxidant capacity in laying duck breeders. Poult Sci 99:3971–3978

Xia WG, Huang ZH, Chen W, Fouad AM, Abouelezz KFM, Li KC, Huang XB, Wang S, Ruan D, Zhang YN et al (2022) Effects of maternal and progeny dietary selenium supplementation on growth performance and antioxidant capacity in ducklings. Poult Sci 101:101574. https://doi.org/10.1016/j.psj.2021.101574

Chen W, Zhang H, Wang S, Ruan D, Xie X, Yu D, Lin Y (2015) Estimation of dietary selenium requirement for Chinese egg-laying ducks. Anim Prod Sci 55:1056–1063

Article   CAS   Google Scholar  

Zhang X, Tian L, Zhai S, Lin Z, Yang H, Chen J, Ye H, Wang W, Yang L, Zhu Y (2020) Effects of selenium-enriched yeast on performance, egg quality, antioxidant balance, and egg selenium content in laying ducks. Front Vet Sci 7:591

Article   PubMed   PubMed Central   Google Scholar  

Eladl AH, Nabil NM, Awad A, Badr S, Ebrahem AF, Tawakol MM, Hammad E, Ali HS, El-Shafei RA (2023) Effects of dietary nano-selenium supplementation on Riemerella anatipestifer vaccinated and challenged Pekin ducklings (Anas platyrhynchos). Vet Microbiol 284:109816. https://doi.org/10.1016/j.vetmic.2023.109816

Chen Y, He H, Bi X, Zhang Y, Yin H (2024) Effects of various selenium-enriched yeasts, selenomethionine, and nanoselenium on production performance, quality, and antioxidant capacity in laying hens. Poult Sci 103:103387. https://doi.org/10.1016/j.psj.2023.103387

Sarlak S, Tabeidian SA, Toghyani M, Foroozandeh Shahraki AD, Goli M, Habibian M (2020) Supplementation of two sources and three levels of iodine in the diet of laying hens: effects on performance, egg quality, serum and egg yolk lipids, antioxidant status, and iodine accumulation in eggs. Ital J Anim Sci 19:974–988

Bakhshalinejad R, Hassanabadi A, Nassiri-Moghaddam H, Zarghi H (2018) The effects of dietary calcium iodate on productive performance, egg quality and iodine accumulation in eggs of laying hens. J Anim Physiol Anim Nutr 102:746–754

Lewis P (2004) Responses of domestic fowl to excess iodine: a review. Br J Nutr 91:29–39

Li Q, Mair C, Schedle K, Hammerl S, Schodl K, Windisch W (2012) Effect of iodine source and dose on growth and iodine content in tissue and plasma thyroid hormones in fattening pigs. Eur J Nutr 51:685–691

Röttger AS, Halle I, Wagner H, Breves G, Dänicke S, Flachowsky G (2012) The effects of iodine level and source on iodine carry-over in eggs and body tissues of laying hens. Arch Anim Nutr 66:385–401

Article   PubMed   Google Scholar  

Christensen V, Ort J (1990) Influence of diet-mediated maternal thyroid alterations on functional properties of turkey eggs. Poult Sci 69:1576–1581

Howie A, Walker S, Akesson B, Arthur J, Beckett G (1995) Thyroidal extracellular glutathione peroxidase: a potential regulator of thyroid-hormone synthesis. Biochem J 308:713

Behroozlak M, Daneshyar M, Farhomand P (2020) The effects of dietary iodine and its consumption duration on performance, carcass characteristics, meat iodine, thyroid hormones and some blood indices in broiler chickens. J Anim Physiol Anim Nutr 104:876–885. https://doi.org/10.1111/jpn.13321

Słupczyńska M, Jamroz D, Orda J, Wiliczkiewicz A, Kuropka P, Król B (2023) The Thyroid Hormone and Immunoglobulin Concentrations in Blood Serum and Thyroid Gland Morphology in Young Hens Fed with Different Diets, Sources, and Levels of Iodine Supply. Animals 13:158

Article   Google Scholar  

Council NR (1994) Nutrient requirements of poultry: Ninth Revised Edition, 1994. National Academies Press, Washington

Google Scholar  

Olson OE, Palmer IS, Cary EE (2020) Modification of the official fluorometric method for selenium in plants. Journal of Association of Official Analytical Chemists 58:117–121. https://doi.org/10.1093/jaoac/58.1.117

Sullivan D, Zywicki R (2012) Determination of total iodine in foods and dietary supplements using inductively coupled plasma-mass spectrometry. J AOAC Int 95:195–202. https://doi.org/10.5740/jaoacint.11-350

Song Z, Zhang X, Zhu L, Jiao H, Lin H (2011) Dexamethasone alters the expression of genes related to the growth of skeletal muscle in chickens (Gallus gallus domesticus). J Mol Endocrinol 46:217

Zhang B, Coon CN (1997) The relationship of various tibia bone measurements in hens. Poult Sci 76:1698–1701

Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2− ΔΔCT method. Methods 25:402–408

Long C, Zhu G-Y, Sheng X-H, Xing K, Venema K, Wang X-G, Xiao L-F, Guo Y, Ni H-M, Zhu N-H (2022) Dietary supplementation with selenomethionine enhances antioxidant capacity and selenoprotein gene expression in layer breeder roosters. Poult Sci 101:102113

Yuan D, Zheng L, Guo X, Wang Y, Zhan X (2013) Regulation of selenoprotein P concentration and expression by different sources of selenium in broiler breeders and their offspring. Poult Sci 92:2375–2380

Xiao X, Yuan D, Wang Y-X, Zhan X-A (2016) The protective effects of different sources of maternal selenium on oxidative stressed chick embryo liver. Biol Trace Elem Res 172:201–208

Wang Y, Yang H, Cao W, Li Y (2017) Effect of selenium supplementation on pigeon reproductive performance, selenium concentration and antioxidant status. Poult Sci 96:3407–3413

Khashaba H, Mariey Y, Ibrahem M (2009) Nutritional and management studies on the pigeon: effect of selenium source and level on pigeons performance. Egyptian Poultry Science Journal 29:971–992

Han X, Qin P, Li W, Ma Q, Ji C, Zhang J, Zhao L (2017) Effect of sodium selenite and selenium yeast on performance, egg quality, antioxidant capacity, and selenium deposition of laying hens. Poult Sci 96:3973–3980

Şekeroǧlu A, Altuntaş E (2009) Effects of egg weight on egg quality characteristics. J Sci Food Agric 89:379–383

Yalçin S, Kahraman Z, Yalcin S, Yalcin S, Dedeoğlu H (2004) Effects of supplementary iodine on the performance and egg traits of laying hens. Br Poult Sci 45:499–503

Golubkina NA, Papazyan TT (2006) Selenium distribution in eggs of avian species. Comp Biochem Physiol B: Biochem Mol Biol 145:384–388

Yao H-D, Wu Q, Zhang Z-W, Zhang J-L, Li S, Huang J-Q, Ren F-Z, Xu S-W, Wang X-L, Lei XG (2013) Gene expression of endoplasmic reticulum resident selenoproteins correlates with apoptosis in various muscles of se-deficient chicks. J Nutr 143:613–619

Liu H, Yu Q, Fang C, Chen S, Tang X, Ajuwon KM, Fang R (2020) Effect of Selenium Source and Level on Performance, Egg Quality, Egg Selenium Content, and Serum Biochemical Parameters in Laying Hens. Foods 9:68

Rizzi L, Bochicchio D, Bargellini A, Parazza P, Simioli M (2009) Effects of dietary microalgae, other lipid sources, inorganic selenium and iodine on yolk n-3 fatty acid composition, selenium content and quality of eggs in laying hens. J Sci Food Agric 89:1775–1781. https://doi.org/10.1002/jsfa.3655

Attia Y, Al Sagan AA, Hussein EOS, Olal MJ, Ebeid TA, Alabdullatif AA, Alhotan RA, Suliman GM, Qaid MM, Tufarelli V et al (2024) Egg Quality, Sensory Attributes, and Protein Metabolites of Laying Hens Fed Whole Flaxseed, Fish Oil, and Different Sources of Trace Elements. J Poult Sci 61:2024021. https://doi.org/10.2141/jpsa.2024021

Attia YA, Abdalah AA, Zeweil HS, Bovera F, El-Din AAT, Araft MA (2010) Effect of inorganic or organic selenium supplementation on productive performance, egg quality and some physiological traits of dual-purpose breeding hens. Czech J Anim Sci 55(11):505-519  https://doi.org/10.17221/1702-CJAS

Shim MY, Karnuah AB, Mitchell AD, Anthony NB, Pesti GM, Aggrey SE (2012) The effects of growth rate on leg morphology and tibia breaking strength, mineral density, mineral content, and bone ash in broilers. Poult Sci 91:1790–1795. https://doi.org/10.3382/ps.2011-01968

Christensen V, Ort J (1991) Iodine toxicity in large white turkey breeder hens. Poult Sci 70:2402–2410

Jing C, Dong X, Wang Z, Liu S, Tong J (2015) Comparative study of DL-selenomethionine vs sodium selenite and seleno-yeast on antioxidant activity and selenium status in laying hens. Poult Sci 94:965–975

Anita A, Singha S, Dhillon K, Nayyar S (2004) Antioxidant enzymes in postpartum anoestrus buffaloes supplemented with vitamin E and selenium. Asian Australas J Anim Sci 17:608–611

Hefnawy AE, Youssef S, Aguilera PV, Rodríguez CV, Pérez JT (2014) The relationship between selenium and T3 in selenium supplemented and nonsupplemented ewes and their lambs. Vet Med Int 2014(1):105236

PubMed   PubMed Central   Google Scholar  

Chartoumpekis DV, Ziros PG, Habeos IG, Sykiotis GP (2022) Emerging roles of Keap1/Nrf2 signaling in the thyroid gland and perspectives for bench-to-bedside translation. Free Radical Biol Med 190:276–283. https://doi.org/10.1016/j.freeradbiomed.2022.08.021

Kim J, Cha Y-N, Surh Y-J (2010) A protective role of nuclear factor-erythroid 2-related factor-2 (Nrf2) in inflammatory disorders. Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis 690:12–23

Wu G, Zhu L, Yuan X, Chen H, Xiong R, Zhang S, Cheng H, Shen Y, An H, Li T (2017) Britanin ameliorates cerebral ischemia–reperfusion injury by inducing the Nrf2 protective pathway. Antioxid Redox Signal 27:754–768

Gorini F, Sabatino L, Pingitore A, Vassalle C (2021) Selenium: An Element of Life Essential for Thyroid Function. Molecules 26:7084. https://doi.org/10.3390/molecules26237084

Cao JJ, Gregoire BR, Zeng H (2012) Selenium deficiency decreases antioxidative capacity and is detrimental to bone microarchitecture in mice. J Nutr 142:1526–1531

Moreno-Reyes R, Egrise D, Nève J, Pasteels JL, Schoutens A (2001) Selenium deficiency-induced growth retardation is associated with an impaired bone metabolism and osteopenia. J Bone Miner Res 16:1556–1563

Ipsa E, Cruzat VF, Kagize JN, Yovich JL, Keane KN (2019) Growth hormone and insulin-like growth factor action in reproductive tissues. Front Endocrinol 10:777

Hakuno F, Takahashi S-I (2018) 40 years of IGF1: IGF1 receptor signaling pathways. J Mol Endocrinol 61:T69–T86

Garcia-Fernandez M, Delgado G, Puche JE, González-Barón S, Castilla Cortázar I (2008) Low doses of insulin-like growth factor I improve insulin resistance, lipid metabolism, and oxidative damage in aging rats. Endocrinology 149:2433–2442

Alikaşifoğlu A, Ozön A, Yordam N (2002) Serum insulin-like growth factor-I (IGF-I) and IGF-binding protein-3 levels in severe iodine deficiency. Turk J Pediatr 44:215–218

PubMed   Google Scholar  

Bailes J, Soloviev M (2021) Insulin-like growth factor-1 (IGF-1) and its monitoring in medical diagnostic and in sports. Biomolecules 11:217

Zimmermann MB, Jooste PL, Mabapa NS, Mbhenyane X, Schoeman S, Biebinger R, Chaouki N, Bozo M, Grimci L, Bridson J (2007) Treatment of Iodine Deficiency in School-Age Children Increases Insulin-Like Growth Factor (IGF)-I and IGF Binding Protein-3 Concentrations and Improves Somatic Growth. J Clin Endocrinol Metab 92:437–442. https://doi.org/10.1210/jc.2006-1901

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This work was supported by the National Key R&D Program of China (Grant No. 2022YFD1300502), State Key Laboratory ofSwine and Poultry Breeding Industry (Grant No. ZQQZ-9), Project for Science and Technology Plan of Guangzhou City (GrantNo. 202201011862), China Agriculture Research System of MOF and MARA (Grant No. CARS-42-13), Opening fund for KeyLaboratory of Animal Breeding and Nutrition of Guangdong Province (Grant No. 2022SZ01), Talented Young ScientistsProgram TYSP (No. P19U42006), Ministry of Science and Technology (MOST), China.

Author information

Xiuguo Shang

Present address: College of Animal Science, Foshan University, Foshan, 528225, China

Md Touhiduzzaman Sarker

Present address: Institute of Animal ScienceState Key Laboratory of Livestock and Poultry BreedingKey Laboratory of Animal Nutrition and Feed Science in South China, Ministry of Agriculture and Rural AffairsGuangdong Public Laboratory of Animal Breeding and Nutrition; Guangdong Key Laboratory of Animal Breeding and Nutrition, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China

Md Touhiduzzaman Sarker and Xiuguo Shang contributed equally to this work.

Authors and Affiliations

Institute of Animal ScienceState Key Laboratory of Livestock and Poultry BreedingKey Laboratory of Animal Nutrition and Feed Science in South China, Ministry of Agriculture and Rural AffairsGuangdong Public Laboratory of Animal Breeding and Nutrition; Guangdong Key Laboratory of Animal Breeding and Nutrition, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China

Wei Chen, Runsheng Xu, Shuang Wang, Weiguang Xia, Yanan Zhang, Chenglong Jin, Shenglin Wang, Chuntian Zheng & Abdelmotaleb Elokil

Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, Hubei, China

Abdelmotaleb Elokil

Department of Animal Production, Faculty of Agriculture, Benha University, 13736, Moshtohor, Egypt

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XS: Conceptualization, formal analysis, and software, writing-original draft. WC: methodology and resources, funding acquisition, supervision. MTS: Conceptualization, formal analysis, and software, writing-original RX: formal analysis and software. SW: data curation WX: data curation, investigation, and validation. CTZ: funding acquisition and project administration. AE: formal analysis and software, writing- reviewing and editing, investigation and validation. All authors have read and agreed to the published version of the manuscript.

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Sarker, M.T., Shang, X., Chen, W. et al. Nutritional Impacts of Dietary Selenium, Iodine and their Interaction on Egg Performance, and Antioxidant Profile in Laying Longyuan Duck Breeders. Biol Trace Elem Res (2024). https://doi.org/10.1007/s12011-024-04308-z

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DOI : https://doi.org/10.1007/s12011-024-04308-z

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