experimental group results

Understanding Experimental Groups

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Scientific experiments often include two groups: the experimental group and the control group . Here's a closer look at the experimental group and how to distinguish it from the experimental group.

Key Takeaways: Experimental Group

• The experimental group is the set of subjects exposed to a change in the independent variable. While it's technically possible to have a single subject for an experimental group, the statistical validity of the experiment will be vastly improved by increasing the sample size.
• In contrast, the control group is identical in every way to the experimental group, except the independent variable is held constant. It's best to have a large sample size for the control group, too.
• It's possible for an experiment to contain more than one experimental group. However, in the cleanest experiments, only one variable is changed.

Experimental Group Definition

An experimental group in a scientific experiment is the group on which the experimental procedure is performed. The independent variable is changed for the group and the response or change in the dependent variable is recorded. In contrast, the group that does not receive the treatment or in which the independent variable is held constant is called the control group .

The purpose of having experimental and control groups is to have sufficient data to be reasonably sure the relationship between the independent and dependent variable is not due to chance. If you perform an experiment on only one subject (with and without treatment) or on one experimental subject and one control subject you have limited confidence in the outcome. The larger the sample size, the more probable the results represent a real correlation .

Example of an Experimental Group

You may be asked to identify the experimental group in an experiment as well as the control group. Here's an example of an experiment and how to tell these two key groups apart .

Let's say you want to see whether a nutritional supplement helps people lose weight. You want to design an experiment to test the effect. A poor experiment would be to take a supplement and see whether or not you lose weight. Why is it bad? You only have one data point! If you lose weight, it could be due to some other factor. A better experiment (though still pretty bad) would be to take the supplement, see if you lose weight, stop taking the supplement and see if the weight loss stops, then take it again and see if weight loss resumes. In this "experiment" you are the control group when you are not taking the supplement and the experimental group when you are taking it.

It's a terrible experiment for a number of reasons. One problem is that the same subject is being used as both the control group and the experimental group. You don't know, when you stop taking treatment, that is doesn't have a lasting effect. A solution is to design an experiment with truly separate control and experimental groups.

If you have a group of people who take the supplement and a group of people who do not, the ones exposed to the treatment (taking the supplement) are the experimental group. The ones not-taking it are the control group.

How to Tell Control and Experimental Group Apart

In an ideal situation, every factor that affects a member of both the control group and experimental group is exactly the same except for one -- the independent variable . In a basic experiment, this could be whether something is present or not. Present = experimental; absent = control.

Sometimes, it's more complicated and the control is "normal" and the experimental group is "not normal". For example, if you want to see whether or not darkness has an effect on plant growth. Your control group might be plants grown under ordinary day/night conditions. You could have a couple of experimental groups. One set of plants might be exposed to perpetual daylight, while another might be exposed to perpetual darkness. Here, any group where the variable is changed from normal is an experimental group. Both the all-light and all-dark groups are types of experimental groups.

Bailey, R.A. (2008). Design of Comparative Experiments . Cambridge: Cambridge University Press. ISBN 9780521683579.

Hinkelmann, Klaus and Kempthorne, Oscar (2008). Design and Analysis of Experiments, Volume I: Introduction to Experimental Design (Second ed.). Wiley. ISBN 978-0-471-72756-9.

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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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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

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

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

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

Three types of experimental designs are commonly used:

1. Independent Measures

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

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

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

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

2. Repeated Measures Design

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

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

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

Counterbalancing

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

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

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

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

3. Matched Pairs Design

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

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

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

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

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

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

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

Learning Check

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

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

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

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

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

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

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

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

Experiment Terminology

Ecological validity.

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

Experimenter effects

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

Demand characteristics

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

Independent variable (IV)

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

Dependent variable (DV)

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

Extraneous variables (EV)

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

Confounding variables

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

Random Allocation

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

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

Order effects

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

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

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

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What Is a Control Group?

Control Groups vs. Experimental Groups in Psychology Research

Doug Corrance/The Image Bank/Getty Images

Control Group vs. Experimental Group

Types of control groups.

In simple terms, the control group comprises participants who do not receive the experimental treatment. When conducting an experiment, these people are randomly assigned to this group. They also closely resemble the participants who are in the experimental group or the individuals who receive the treatment.

Experimenters utilize variables to make comparisons between an experimental group and a control group. A variable is something that researchers can manipulate, measure, and control in an experiment. The independent variable is the aspect of the experiment that the researchers manipulate (or the treatment). The dependent variable is what the researchers measure to see if the independent variable had an effect.

While they do not receive the treatment, the control group does play a vital role in the research process. Experimenters compare the experimental group to the control group to determine if the treatment had an effect.

By serving as a comparison group, researchers can isolate the independent variable and look at the impact it had.

The simplest way to determine the difference between a control group and an experimental group is to determine which group receives the treatment and which does not. To ensure that the results can then be compared accurately, the two groups should be otherwise identical.

Not exposed to the treatment (the independent variable)

Used to provide a baseline to compare results against

May receive a placebo treatment

Exposed to the treatment

Used to measure the effects of the independent variable

Identical to the control group aside from their exposure to the treatment

Why a Control Group Is Important

While the control group does not receive treatment, it does play a critical role in the experimental process. This group serves as a benchmark, allowing researchers to compare the experimental group to the control group to see what sort of impact changes to the independent variable produced.  

Because participants have been randomly assigned to either the control group or the experimental group, it can be assumed that the groups are comparable.

Any differences between the two groups are, therefore, the result of the manipulations of the independent variable. The experimenters carry out the exact same procedures with both groups with the exception of the manipulation of the independent variable in the experimental group.

There are a number of different types of control groups that might be utilized in psychology research. Some of these include:

• Positive control groups : In this case, researchers already know that a treatment is effective but want to learn more about the impact of variations of the treatment. In this case, the control group receives the treatment that is known to work, while the experimental group receives the variation so that researchers can learn more about how it performs and compares to the control.
• Negative control group : In this type of control group, the participants are not given a treatment. The experimental group can then be compared to the group that did not experience any change or results.
• Placebo control group : This type of control group receives a placebo treatment that they believe will have an effect. This control group allows researchers to examine the impact of the placebo effect and how the experimental treatment compared to the placebo treatment.
• Randomized control group : This type of control group involves using random selection to help ensure that the participants in the control group accurately reflect the demographics of the larger population.
• Natural control group : This type of control group is naturally selected, often by situational factors. For example, researchers might compare people who have experienced trauma due to war to people who have not experienced war. The people who have not experienced war-related trauma would be the control group.

Examples of Control Groups

Control groups can be used in a variety of situations. For example, imagine a study in which researchers example how distractions during an exam influence test results. The control group would take an exam in a setting with no distractions, while the experimental groups would be exposed to different distractions. The results of the exam would then be compared to see the effects that distractions had on test scores.

Experiments that look at the effects of medications on certain conditions are also examples of how a control group can be used in research. For example, researchers looking at the effectiveness of a new antidepressant might use a control group that receives a placebo and an experimental group that receives the new medication. At the end of the study, researchers would compare measures of depression for both groups to determine what impact the new medication had.

After the experiment is complete, researchers can then look at the test results and start making comparisons between the control group and the experimental group.

Uses for Control Groups

Researchers utilize control groups to conduct research in a range of different fields. Some common uses include:

• Psychology : Researchers utilize control groups to learn more about mental health, behaviors, and treatments.
• Medicine : Control groups can be used to learn more about certain health conditions, assess how well medications work to treat these conditions, and assess potential side effects that may result.
• Education : Educational researchers utilize control groups to learn more about how different curriculums, programs, or instructional methods impact student outcomes.
• Marketing : Researchers utilize control groups to learn more about how consumers respond to advertising and marketing efforts.

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National Cancer Institute. Control group.

Pithon MM. Importance of the control group in scientific research . Dental Press J Orthod. 2013;18(6):13-14. doi:10.1590/s2176-94512013000600003

Karlsson P, Bergmark A. Compared with what? An analysis of control-group types in Cochrane and Campbell reviews of psychosocial treatment efficacy with substance use disorders . Addiction . 2015;110(3):420-8. doi:10.1111/add.12799

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

Understanding Control Groups for Research

Introduction

What are control groups in research, examples of control groups in research, control group vs. experimental group, types of control groups, control groups in non-experimental research.

A control group is typically thought of as the baseline in an experiment. In an experiment, clinical trial, or other sort of controlled study, there are at least two groups whose results are compared against each other.

The experimental group receives some sort of treatment, and their results are compared against those of the control group, which is not given the treatment. This is important to determine whether there is an identifiable causal relationship between the treatment and the resulting effects.

As intuitive as this may sound, there is an entire methodology that is useful to understanding the role of the control group in experimental research and as part of a broader concept in research. This article will examine the particulars of that methodology so you can design your research more rigorously .

Suppose that a friend or colleague of yours has a headache. You give them some over-the-counter medicine to relieve some of the pain. Shortly after they take the medicine, the pain is gone and they feel better. In casual settings, we can assume that it must be the medicine that was the cause of their headache going away.

In scientific research, however, we don't really know if the medicine made a difference or if the headache would have gone away on its own. Maybe in the time it took for the headache to go away, they ate or drank something that might have had an effect. Perhaps they had a quick nap that helped relieve the tension from the headache. Without rigorously exploring this phenomenon , any number of confounding factors exist that can make us question the actual efficacy of any particular treatment.

Experimental research relies on observing differences between the two groups by "controlling" the independent variable , or in the case of our example above, the medicine that is given or not given depending on the group. The dependent variable in this case is the change in how the person suffering the headache feels, and the difference between taking and not taking the medicine is evidence (or lack thereof) that the treatment is effective.

The catch is that, between the control group and other groups (typically called experimental groups), it's important to ensure that all other factors are the same or at least as similar as possible. Things such as age, fitness level, and even occupation can affect the likelihood someone has a headache and whether a certain medication is effective.

Faced with this dynamic, researchers try to make sure that participants in their control group and experimental group are as similar as possible to each other, with the only difference being the treatment they receive.

Experimental research is often associated with scientists in lab coats holding beakers containing liquids with funny colors. Clinical trials that deal with medical treatments rely primarily, if not exclusively, on experimental research designs involving comparisons between control and experimental groups.

However, many studies in the social sciences also employ some sort of experimental design which calls for the use of control groups. This type of research is useful when researchers are trying to confirm or challenge an existing notion or measure the difference in effects.

Workplace efficiency research

How might a company know if an employee training program is effective? They may decide to pilot the program to a small group of their employees before they implement the training to their entire workforce.

If they adopt an experimental design, they could compare results between an experimental group of workers who participate in the training program against a control group who continues as per usual without any additional training.

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Mental health research

Music certainly has profound effects on psychology, but what kind of music would be most effective for concentration? Here, a researcher might be interested in having participants in a control group perform a series of tasks in an environment with no background music, and participants in multiple experimental groups perform those same tasks with background music of different genres. The subsequent analysis could determine how well people perform with classical music, jazz music, or no music at all in the background.

Educational research

Suppose that you want to improve reading ability among elementary school students, and there is research on a particular teaching method that is associated with facilitating reading comprehension. How do you measure the effects of that teaching method?

A study could be conducted on two groups of otherwise equally proficient students to measure the difference in test scores. The teacher delivers the same instruction to the control group as they have to previous students, but they teach the experimental group using the new technique. A reading test after a certain amount of instruction could determine the extent of effectiveness of the new teaching method.

As you can see from the three examples above, experimental groups are the counterbalance to control groups. A control group offers an essential point of comparison. For an experimental study to be considered credible, it must establish a baseline against which novel research is conducted.

Researchers can determine the makeup of their experimental and control groups from their literature review . Remember that the objective of a review is to establish what is known about the object of inquiry and what is not known. Where experimental groups explore the unknown aspects of scientific knowledge, a control group is a sort of simulation of what would happen if the treatment or intervention was not administered. As a result, it will benefit researchers to have a foundational knowledge of the existing research to create a credible control group against which experimental results are compared, especially in terms of remaining sensitive to relevant participant characteristics that could confound the effects of your treatment or intervention so that you can appropriately distribute participants between the experimental and control groups.

There are multiple control groups to consider depending on the study you are looking to conduct. All of them are variations of the basic control group used to establish a baseline for experimental conditions.

No-treatment control group

This kind of control group is common when trying to establish the effects of an experimental treatment against the absence of treatment. This is arguably the most straightforward approach to an experimental design as it aims to directly demonstrate how a certain change in conditions produces an effect.

Placebo control group

In this case, the control group receives some sort of treatment under the exact same procedures as those in the experimental group. The only difference in this case is that the treatment in the placebo control group has already been judged to be ineffective, except that the research participants don't know that it is ineffective.

Placebo control groups (or negative control groups) are useful for allowing researchers to account for any psychological or affective factors that might impact the outcomes. The negative control group exists to explicitly eliminate factors other than changes in the independent variable conditions as causes of the effects experienced in the experimental group.

Positive control group

Contrasted with a no-treatment control group, a positive control group employs a treatment against which the treatment in the experimental group is compared. However, unlike in a placebo group, participants in a positive control group receive treatment that is known to have an effect.

If we were to use our first example of headache medicine, a researcher could compare results between medication that is commonly known as effective against the newer medication that the researcher thinks is more effective. Positive control groups are useful for validating experimental results when compared against familiar results.

Historical control group

Rather than study participants in control group conditions, researchers may employ existing data to create historical control groups. This form of control group is useful for examining changing conditions over time, particularly when incorporating past conditions that can't be replicated in the analysis.

Qualitative research more often relies on non-experimental research such as observations and interviews to examine phenomena in their natural environments. This sort of research is more suited for inductive and exploratory inquiries, not confirmatory studies meant to test or measure a phenomenon.

That said, the broader concept of a control group is still present in observational and interview research in the form of a comparison group. Comparison groups are used in qualitative research designs to show differences between phenomena, with the exception being that there is no baseline against which data is analyzed.

Comparison groups are useful when an experimental environment cannot produce results that would be applicable to real-world conditions. Research inquiries examining the social world face challenges of having too many variables to control, making observations and interviews across comparable groups more appropriate for data collection than clinical or sterile environments.

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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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Control Group vs. Experimental Group: What's the Difference?

Key Differences

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Control Group vs. Experimental Group: Everything You Need To Know About The Difference Between Control Group And Experimental Group

As someone who is deeply interested in the field of research, you may have heard the terms control group and experimental group thrown around a lot. If you’re not very familiar with these terms, it can be daunting to determine the role they play in research and why they are so important. In layman’s terms, a control group is a group that does not receive any experimental treatment and is used as a benchmark for the group that does receive the treatment. Meanwhile, the experimental group is a group that receives the treatment and is compared to the control group that does not receive the treatment. To put it simply, the main difference between a control group and an experimental group is whether or not they receive the experimental treatment.

Table of Contents

What Is Control Group?

A control group is a group in an experiment that does not receive the experimental treatment and is used as a comparison for the group that does receive the treatment. It is a critical aspect of experimental research to determine whether the treatment caused the outcome rather than another factor. The control group ensures that any observed effects can be attributed to the treatment and not a result of other variables. The quality of the control group can affect the validity of the experiment. Therefore, researchers must carefully design and select participants for the control group to ensure that it accurately represents the population and provides meaningful results. Overall, control groups are essential to gain accurate and reliable results in experimental research.

What Is Experimental Group?

Key differences between control group and experimental group, control group vs. experimental group similarities.

The control group and experimental group are two essential components of any research study. The main similarity between these groups is that they are both used to assess the effects of a treatment or intervention. The control group is intended to provide a baseline measurement of the outcomes that are expected in the absence of the intervention. In contrast, the experimental group is exposed to the intervention or treatment and is observed for any changes or improvements in outcomes. In summary, both groups serve as comparisons for one another, and their use increases the credibility and validity of research findings.

Control Group vs. Experimental Group Pros and Cons

Control group pros & cons, control group pros, control group cons, experimental group pros & cons, experimental group pros.

The Experimental Group, in scientific studies and experimentation, is a group that receives the experimental treatment and is compared to a control group that does not receive the treatment. There are several advantages or pros of this group. First, the experimental group allows researchers to determine the effectiveness of a new treatment or procedure. Second, it helps in identifying side effects of the treatment on the subjects. Third, it provides clear evidence regarding the cause and effect relationships between variables. Additionally, the experimental group enables researchers to validate their findings and test the hypothesis. These benefits make the Experimental Group essential in accurately assessing the effectiveness of new treatments or procedures.

Experimental Group Cons

Comparison table: 5 key differences between control group and experimental group.

Comparison Chart

Comparison video, conclusion: what is the difference between control group and experimental group.

In conclusion, understanding the difference between a control group and an experimental group is crucial in designing and conducting reliable experiments. The control group serves as a baseline, allowing researchers to compare the effects of the experimental treatment. Without a control group, it is difficult to determine whether any observed effects are due to the treatment or to other factors. By contrast, the experimental group receives the treatment and is used to evaluate the effects of the intervention. By carefully controlling for different factors, scientists can use these groups to test hypotheses and draw meaningful conclusions about the impact of different treatments on the outcomes of interest.

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Psychological Research

Experiments, learning objectives.

• Describe the experimental process, including ways to control for bias
• Identify and differentiate between independent and dependent variables

Causality: Conducting Experiments and Using the Data

Experimental hypothesis.

In order to conduct an experiment, a researcher must have a specific hypothesis to be tested. As you’ve learned, hypotheses can be formulated either through direct observation of the real world or after careful review of previous research. For example, if you think that the use of technology in the classroom has negative impacts on learning, then you have basically formulated a hypothesis—namely, that the use of technology in the classroom should be limited because it decreases learning. How might you have arrived at this particular hypothesis? You may have noticed that your classmates who take notes on their laptops perform at lower levels on class exams than those who take notes by hand, or those who receive a lesson via a computer program versus via an in-person teacher have different levels of performance when tested (Figure 1).

Figure 1 . How might the use of technology in the classroom impact learning? (credit: modification of work by Nikolay Georgiev/Pixabay)

These sorts of personal observations are what often lead us to formulate a specific hypothesis, but we cannot use limited personal observations and anecdotal evidence to rigorously test our hypothesis. Instead, to find out if real-world data supports our hypothesis, we have to conduct an experiment.

Designing an Experiment

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested (in this case, violent TV images)—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

In our example of how the use of technology should be limited in the classroom, we have the experimental group learn algebra using a computer program and then test their learning. We measure the learning in our control group after they are taught algebra by a teacher in a traditional classroom. It is important for the control group to be treated similarly to the experimental group, with the exception that the control group does not receive the experimental manipulation.

We also need to precisely define, or operationalize, how we measure learning of algebra. An operational definition is a precise description of our variables, and it is important in allowing others to understand exactly how and what a researcher measures in a particular experiment. In operationalizing learning, we might choose to look at performance on a test covering the material on which the individuals were taught by the teacher or the computer program. We might also ask our participants to summarize the information that was just presented in some way. Whatever we determine, it is important that we operationalize learning in such a way that anyone who hears about our study for the first time knows exactly what we mean by learning. This aids peoples’ ability to interpret our data as well as their capacity to repeat our experiment should they choose to do so.

Once we have operationalized what is considered use of technology and what is considered learning in our experiment participants, we need to establish how we will run our experiment. In this case, we might have participants spend 45 minutes learning algebra (either through a computer program or with an in-person math teacher) and then give them a test on the material covered during the 45 minutes.

Ideally, the people who score the tests are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was in which group, it might influence how they interpret ambiguous responses, such as sloppy handwriting or minor computational mistakes. By being blind to which child is in which group, we protect against those biases. This situation is a single-blind study, meaning that one of the groups (participants) are unaware as to which group they are in (experiment or control group) while the researcher who developed the experiment knows which participants are in each group.

Figure 2 . Providing the control group with a placebo treatment protects against bias caused by expectancy. (credit: Elaine and Arthur Shapiro)

In a double-blind study , both the researchers and the participants are blind to group assignments. Why would a researcher want to run a study where no one knows who is in which group? Because by doing so, we can control for both experimenter and participant expectations. If you are familiar with the phrase placebo effect, you already have some idea as to why this is an important consideration. The placebo effect occurs when people’s expectations or beliefs influence or determine their experience in a given situation. In other words, simply expecting something to happen can actually make it happen.

The placebo effect is commonly described in terms of testing the effectiveness of a new medication. Imagine that you work in a pharmaceutical company, and you think you have a new drug that is effective in treating depression. To demonstrate that your medication is effective, you run an experiment with two groups: The experimental group receives the medication, and the control group does not. But you don’t want participants to know whether they received the drug or not.

Why is that? Imagine that you are a participant in this study, and you have just taken a pill that you think will improve your mood. Because you expect the pill to have an effect, you might feel better simply because you took the pill and not because of any drug actually contained in the pill—this is the placebo effect.

To make sure that any effects on mood are due to the drug and not due to expectations, the control group receives a placebo (in this case a sugar pill). Now everyone gets a pill, and once again neither the researcher nor the experimental participants know who got the drug and who got the sugar pill. Any differences in mood between the experimental and control groups can now be attributed to the drug itself rather than to experimenter bias or participant expectations (Figure 2).

Independent and Dependent Variables

In a research experiment, we strive to study whether changes in one thing cause changes in another. To achieve this, we must pay attention to two important variables, or things that can be changed, in any experimental study: the independent variable and the dependent variable. An independent variable is manipulated or controlled by the experimenter. In a well-designed experimental study, the independent variable is the only important difference between the experimental and control groups. In our example of how technology use in the classroom affects learning, the independent variable is the type of learning by participants in the study (Figure 2.17). A dependent variable is what the researcher measures to see how much effect the independent variable had. In our example, the dependent variable is the learning exhibited by our participants.

Figure 3 . In an experiment, manipulations of the independent variable are expected to result in changes in the dependent variable. (credit: “classroom” modification of work by Nikolay Georgiev/Pixabay; credit “note taking”: modification of work by KF/Wikimedia)

We expect that the dependent variable will change as a function of the independent variable. In other words, the dependent variable depends on the independent variable. A good way to think about the relationship between the independent and dependent variables is with this question: What effect does the independent variable have on the dependent variable? Returning to our example, what effect does watching a half hour of violent television programming or nonviolent television programming have on the number of incidents of physical aggression displayed on the playground?

Selecting and Assigning Experimental Participants

Now that our study is designed, we need to obtain a sample of individuals to include in our experiment. Our study involves human participants so we need to determine who to include. Participants are the subjects of psychological research, and as the name implies, individuals who are involved in psychological research actively participate in the process. Often, psychological research projects rely on college students to serve as participants. In fact, the vast majority of research in psychology subfields has historically involved students as research participants (Sears, 1986; Arnett, 2008). But are college students truly representative of the general population? College students tend to be younger, more educated, more liberal, and less diverse than the general population. Although using students as test subjects is an accepted practice, relying on such a limited pool of research participants can be problematic because it is difficult to generalize findings to the larger population.

Our hypothetical experiment involves children, and we must first generate a sample of child participants. Samples are used because populations are usually too large to reasonably involve every member in our particular experiment (Figure 4). If possible, we should use a random sample (there are other types of samples, but for the purposes of this section, we will focus on random samples). A random sample is a subset of a larger population in which every member of the population has an equal chance of being selected. Random samples are preferred because if the sample is large enough we can be reasonably sure that the participating individuals are representative of the larger population. This means that the percentages of characteristics in the sample—sex, ethnicity, socioeconomic level, and any other characteristics that might affect the results—are close to those percentages in the larger population.

In our example, let’s say we decide our population of interest is fourth graders. But all fourth graders is a very large population, so we need to be more specific; instead we might say our population of interest is all fourth graders in a particular city. We should include students from various income brackets, family situations, races, ethnicities, religions, and geographic areas of town. With this more manageable population, we can work with the local schools in selecting a random sample of around 200 fourth graders who we want to participate in our experiment.

In summary, because we cannot test all of the fourth graders in a city, we want to find a group of about 200 that reflects the composition of that city. With a representative group, we can generalize our findings to the larger population without fear of our sample being biased in some way.

Figure 4 . Researchers may work with (a) a large population or (b) a sample group that is a subset of the larger population. (credit “crowd”: modification of work by James Cridland; credit “students”: modification of work by Laurie Sullivan)

Now that we have a sample, the next step of the experimental process is to split the participants into experimental and control groups through random assignment. With random assignment , all participants have an equal chance of being assigned to either group. There is statistical software that will randomly assign each of the fourth graders in the sample to either the experimental or the control group.

Random assignment is critical for sound experimental design . With sufficiently large samples, random assignment makes it unlikely that there are systematic differences between the groups. So, for instance, it would be very unlikely that we would get one group composed entirely of males, a given ethnic identity, or a given religious ideology. This is important because if the groups were systematically different before the experiment began, we would not know the origin of any differences we find between the groups: Were the differences preexisting, or were they caused by manipulation of the independent variable? Random assignment allows us to assume that any differences observed between experimental and control groups result from the manipulation of the independent variable.

Issues to Consider

While experiments allow scientists to make cause-and-effect claims, they are not without problems. True experiments require the experimenter to manipulate an independent variable, and that can complicate many questions that psychologists might want to address. For instance, imagine that you want to know what effect sex (the independent variable) has on spatial memory (the dependent variable). Although you can certainly look for differences between males and females on a task that taps into spatial memory, you cannot directly control a person’s sex. We categorize this type of research approach as quasi-experimental and recognize that we cannot make cause-and-effect claims in these circumstances.

Experimenters are also limited by ethical constraints. For instance, you would not be able to conduct an experiment designed to determine if experiencing abuse as a child leads to lower levels of self-esteem among adults. To conduct such an experiment, you would need to randomly assign some experimental participants to a group that receives abuse, and that experiment would be unethical.

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Experimental vs. control group explained.

Home » Experimental vs. Control Group Explained

Group Comparison Analysis plays a pivotal role in experimental research. By examining the differences between experimental and control groups, researchers can draw meaningful conclusions about specific interventions. This process helps in determining whether observed effects are indeed attributable to the treatment or merely due to chance.

In any experiment, understanding how participants respond to different conditions is crucial. Group Comparison Analysis allows scientists to tease apart these responses, yielding insights that can inform various fields. Ultimately, this analytical approach not only enhances the validity of research findings but also supports the development of effective strategies based on empirical evidence.

The Basics of Experimental Groups

In research, understanding the distinction between experimental groups is essential for accurate findings. An experimental group consists of participants exposed to a variable being tested, while a control group serves as the baseline for comparison. This design enhances the reliability of results by isolating the effects of the independent variable. To conduct a thorough group comparison analysis, researchers need to ensure that both groups are similar in characteristics, minimizing biases.

The selection of participants plays a crucial role in the integrity of the study. Random assignment helps to ensure that individuals in both groups do not display pre-existing differences. This allows researchers to draw valid conclusions regarding the impact of the experimental treatment. Analyzing data from both groups provides insights into whether the intervention produces the expected changes. Effective comparison between these groups is foundational for advancing scientific knowledge. Understanding these basics will guide you through interpreting research outcomes with confidence.

Definition and Purpose

Understanding the experimental and control groups is essential in any Group Comparison Analysis. The experimental group receives the treatment or intervention, while the control group serves as a baseline for comparison. This structure is pivotal in determining the effectiveness of a given treatment and minimizes bias, ensuring the results are reliable.

The purpose of utilizing these groups lies in establishing a clear cause-and-effect relationship. By comparing outcomes from both groups, researchers can identify any significant differences attributable to the treatment. This comparison not only enhances the validity of findings but also influences data-driven decisions in various fields, including healthcare and marketing. Ultimately, the insight gained from this method fosters informed strategies that can lead to improved outcomes, whether in product development or user experience.

Designing an Experimental Group: Group Comparison Analysis

Designing an experimental group involves carefully planning each aspect to ensure valid results through group comparison analysis. This analysis is crucial for distinguishing the effects of a treatment or intervention from the natural variability found in any population. To effectively design your experimental group, you need to determine the characteristics that will make it comparable to the control group.

A proper comparison requires selection criteria such as age, gender, and baseline characteristics. This helps ensure that differences in outcomes arise solely from the intervention rather than from pre-existing variances. Next, consider randomization; randomly assigning participants reduces bias and enhances the study's reliability. Lastly, maintaining consistency in treatment delivery is essential. This ensures that everyone in the experimental group receives the same intervention, thus allowing for an accurate analysis of effects. By following these principles, your group comparison analysis can yield insightful and actionable outcomes.

The Role of Control Groups in Research

Control groups play a vital role in research by providing a benchmark to which experimental groups can be compared. Through group comparison analysis, researchers can discern the effects of an intervention by measuring outcomes against the control group that does not receive the treatment. This approach ensures that any observed changes in the experimental group can be more confidently attributed to the treatment rather than other external factors.

Moreover, control groups help minimize bias and variability in research outcomes. By allowing researchers to assess how participants behave under standard conditions, it becomes easier to isolate the impact of the experimental variable. Understanding these dynamics improves the reliability of results, making findings more valid and generalizable. Therefore, incorporating control groups in studies is essential for achieving accurate and trustworthy conclusions that can inform future practices or theories.

Definition and Purpose of Control Groups in Group Comparison Analysis

Control groups are essential in group comparison analysis, serving as benchmarks for experimental outcomes. These groups consist of participants who do not receive the treatment or intervention under investigation, allowing researchers to isolate the impact of specific variables. By comparing the results from the experimental group against the control group, researchers can determine the effectiveness of the intervention in a more precise manner.

The purpose of control groups is to minimize biases and ensure valid conclusions. They help in identifying whether observed changes in the experimental group are genuinely caused by the treatment or merely due to external factors. Additionally, control groups enable replication of studies, which is vital for affirming findings and fostering scientific credibility. In summary, control groups are indispensable tools in group comparison analysis, providing clarity and enhancing the reliability of research outcomes.

Examples of Control Group Usage

Control groups are essential in various fields, enabling researchers to validate their findings by providing a baseline for comparison. For instance, in a clinical trial assessing a new medication, one group receives the drug while a control group receives a placebo. This setup allows for a clearer understanding of the drug's effectiveness versus no treatment at all.

In market research, control groups allow analysts to examine consumer behavior under different conditions. A common example is testing two marketing strategies: one group receives traditional ads, while the control group is exposed to digital campaigns. Group comparison analysis reveals which method resonates better with the audience, helping to refine marketing approaches and optimize future campaigns. Through these examples, it's evident that control groups are invaluable in ensuring scientific rigor and making informed decisions across various domains.

Conclusion: The Importance of Group Comparison Analysis in Research

Group Comparison Analysis serves as a critical tool for researchers, allowing them to discern the differences between experimental and control groups. By methodically comparing these groups, researchers can assess the effectiveness of interventions or treatments. This type of analysis provides vital insights, facilitating a deeper understanding of how variables impact outcomes.

Furthermore, the importance of this analysis extends beyond mere statistical significance. It fosters evidence-based decision-making, ensuring that findings are reliable and applicable in real-world settings. Ultimately, understanding the dynamics between different groups equips researchers with the knowledge to make informed conclusions, driving advancements in various fields of study.

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Psychology Discussion

Top 2 experiments on attention | experimental psychology.

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List of top two psychological experiments on attention!

Experiment # 1. Span of Attention – Visual:

At any given moment there are several stimuli in the environment competing for our attention. However, our sense organs can respond to only a limited number of them at the same time. This limit is known as span of attention. The span varies from individual to individual, from sense organ to sense organ, and also according to the nature of the stimuli. The earliest psychologist to be interested in the problem was Sir William Hamilton, who made a very crude experimental attempt to study the problem.

An advance was made on Hamilton’s method by Jevons, the logician. However, real scientific experimental work on the problem was started by J.M. Cattell, who used the tachistoscope for this experiment. After Cattell, a number of experimenters have studied the span of attention under different conditions. Later experimenters have distinguished between span of attention and span of apprehension and also found that span of apprehension is greater than span of attention.

To determine the span of attention for the following type of Visual stimuli:

1. Single dots

2. Grouped dots

3. Nonsense Syllables (meaningless combinations of letters)

4. Meaningful words, and

5. Numbers.

Materials Required :

A tachistoscope of the falling-door type, exposure cards having the following materials printed on them:

(i) Each set of 3 Cards bearing 3 to 10 single dots. (i.e., 3 cards with 3 dots, 3 cards with 4 dots, etc.). The dots are to be arranged in different patterns.

(ii) Cards with 3 dots in each group ranging from 3 groups to 10 groups (having 3 dots corresponding to a single dot in the earlier set of cards.

(iii) Cards with nonsense syllables having 3 letter syllables to 10 letter syllables, 3 different combinations of letters at each level (3 cards with 3 letter syllables, 3 cards with 4 letter syllables etc.).

(iv) Cards with meaningful words, again ranging from 3 letter words to 12 letter words (different words at each stage).

(v) Cards with numbers ranging from 3 digit numbers to 10 digit numbers (again 3 cards at each stage with 3 different numbers having the same number of digits).

Description of the Apparatus:

There are different types of tachistoscopes. Falling-door type is the one which usually has a fixed exposure time. There are other tachistoscopes which are operated electrically and the exposure is variable and adjustable (camera- shutter types). For the present experiment, the simple falling-door type is adequate.

It consists of a wooden screen with a window in the middle which is covered by a movable falling shutter. This shutter can be closed or opened with the help of a lever which is behind the screen at the top. The exposure time is usually 1/10th of a second. It is sufficient to enable the subject to take a quick glance at the exposed material and at the same time short enough to prevent him from reading or memorising it.

The subject is seated in front of the tachistoscope such that he has a good view of the window. The experimenter sits on the other side of the apparatus keeping the five separate sets of cards with him. The sets are shuffled separately and kept ready for the experiment. The experiment has to be done separately for each of the five sets.

First Set of Cards:

Instructions to the Subject :

“Observe this window carefully. I will say ‘ready’ and open this window. You will see a card with a number of dots. Try to find out how many dots are there. The card will be exposed only for a short time”.

The experimenter then shuffles the set of cards with single dots and exposes them one after the other, each time giving the ‘ready’ signal. After presenting each card, he makes a note of the actual number of dots as well as the subject’s response. The complete set is exposed once and then exposed for a second time. The subject thus views each card twice and therefore there are 6 stimuli for each level, i.e., 6 exposures for 3 dots, 6 exposures for 4 dots, etc.

After exposing all the cards the experimenter finds out how many times the subject has responded correctly for each level out of the possible 6 times.

Tabulate the results as follows:

Now we are ready to determine the span. For experimental purposes the span can be defined as the maximum number of dots to which at least 75 per cent of correct responses are made, viz. if a subject responds 100 per cent correctly to three dots, 83.3 per cent to four dots and 66.67 per cent to five dots, his span lies between 4 and 5. The span can now be determined by interpolation between 4 and 5.

Procedure with the Other Sets of Cards:

The procedure for the other sets is essentially the same excepting for the instructions, which are as following:

i. Instructions for Groups of Dots:

“This time instead of single dots you will see small separate groups of 3 dots each. After seeing each card, tell me how many groups of dots are there in each card”.

ii. Instructions for Nonsense Syllables :

“In this series you will see some syllables instead of dots. After seeing each card, write down the syllables as correctly as possible.”

iii. Instructions for Meaningful Words :

“Here on each card you will find a familiar and meaningful word. Try to write down the word you see on each card.”

iv. Instructions for Numbers :

“In this set, instead of words or dots you will find numbers; as before you will have to write down the number you see”.

After exposing all the sets determine the span in each case as illustrated in the case of dots. The whole experiment can be done in two sessions. Otherwise the subject is likely to get bored and fatigued.

(1) Study individual variations in the span for the different types.

(2) Compare the Spans:

There will be some interesting findings with respect to the differences in the attention spans between single dots and groups of dots. The subject who has a span of 6 single dots may also have a span of 6 for groups of dots though the latter actually includes 18 dots. This is because of the factor of grouping. Each group of dots is responded to as a single stimulus, because of the factor of organisation.

Similarly the span for meaningful words will be usually much higher than the span for nonsense syllables, though both are made up of same number of letters of alphabet. This is because of the factor of meaning and familiarity. In the case of meaningful words and numbers there is apprehension or understanding in addition to mere attention. Furthermore, the factor of familiarity is helpful.

Application:

This experiment has a number of practical applications. A very common illustration is the registration numbers given to automobiles. Usually, automobile numbers do not exceed four digits. This is because the traffic constable would be unable to note down the registration number of automobiles violating traffic rules if the number exceeds four digits. However, the letters of alphabet before the numbers are perceived because they are grouped separately.

Experiment # 2. Distraction of Attention :

When we are attending to some stimulus or work, any noise or other type of disturbance tends to affect the efficiency of our attention. This phenomenon of irrelevant stimuli interfering with our attentive process is called ‘distraction’. Not all stimuli can distract out attention, viz., the ticking of a table clock on our study table does not ordinarily disturb us. Sometimes even strong stimuli do not disturb us when we are prepared for it.

One experiment showed that students working on some problems could, to a large extent, resist distractions of different types by putting in more effort. Baker employing dance music as distractor found that in many instances, the subject did better when music was played. Morgan in his classical experiments proved that subjects can soon get used to a distracting influence, and that often efficiency is lost when distracting influence is removed.

Introspective reports, however, show that subjects feel a greater strain and have to put in greater effort under distracting conditions to maintain the same level of efficiency of attention. Experiments on distraction are usually carried out as group experiments.

To determine the effect of extraneous and irrelevant stimuli on the work efficiency.

Material Required :

A long list of arithmetic problems of uniform difficulty, a sound proof room fitted with number of buzzers, bells, bright lights, etc., to serve as visual and auditory distractions.

Procedure :

The experiment is done under four conditions:

1. Controlled condition.

2. Auditory distraction.

3. Visual distraction.

4. Combination of visual and auditory distraction.

The experiment can be conducted by adopting any one of the following experimental designs:

Experimental Design 1:

Different groups of subjects are assigned to the four conditions.

Experimental Design 2:

The performance of all the subjects under controlled conditions, without any kind of deliberate distraction, is assessed and on the basis of these scores, the subjects are grouped into three matched groups. Each one of these groups is assigned to each one of the three conditions of distraction.

Experimental Design 3:

The performance of each subject is assessed under all the four conditions.

In the first experimental design, the subjects are selected and assigned to the four conditions by following the method of randomisation.

In the second experimental design, the subjects are categorised into three matched groups by following any one of the techniques of matching the groups, and each one of these groups is assigned to one experimental condition by following the method of randomisation.

In the third experimental design the subjects are categorised into four groups by following the method of randomisation and the performance of each one of these groups under all the four conditions is observed. However, the order of presentation of the four conditions should be counter-balanced.

Instructions to the Subjects :

Give the selected arithmetic problems to the subjects and ask them to solve them.

1. Controlled Condition :

For five minutes allow them to solve the problems under normal conditions, and then ask them to highlight the last problem they have solved.

2. Auditory Distraction :

Suddenly, at the end of 5 minutes, switch on the buzzers and the bells so that the room is filled with loud noises. The subjects have to continue solving the problems. Ask the subjects to indicate the last problem they have solved.

3. Visual Distraction :

At the end of five minutes switch off the buzzers but switch on the bright lights, flashing glaring lights of different colours and ask the subjects to mark the last problem they have solved.

4. Combination of Visual and Auditory Distraction :

At the end of five minutes, switch on both the buzzers and the lights and ask the subjects to highlight the last problem solved.

Now collect the answer sheets and correct them. Tabulate the number of problems attempted and the number correctly solved for each of the five-minute periods. Take the introspective report of the subject.

Compare the results under the four conditions. See whether work efficiency has, been affected. Analyse the introspective reports to find out the subjects inner reactions to various distractions. Also find out whether they had to put in greater effort to carry out the work under different conditions of distraction.

Tabulate group results as follows:

1. Calculate the Mean & SD under all the conditions for problems attempted as well as problems correctly solved.

2. Do all subjects show the same type of change under distraction?

3. Which condition is most distracting for the group and which the least?

4. Do all the subjects show the same trend of performance under all the four conditions?

It may be interesting to study the effect of preparedness of the subject for distraction.

Instruct the subjects and give them prior information about the occurrence of the distraction. This can be done by giving the instructions for all the conditions at the beginning or specifically before the start of each session studying the effect of a specified condition.

Applications :

Such experiments are useful in pinpointing factors that distract workers in factories, offices, etc. where the efficiency of the workers can be improved by eliminating the distracting conditions. Industrial psychologists have carried out several experiments on this subject. It has been found that minimisation of noise in the work situation facilitates the employees to concentrate better on their tasks resulting in better output. Further, excess of noise has also been found to lead to stress.

Related Articles:

• Attention: 4 Major Conditions of Attention (with diagram)
• 9 Commonly used Tests and Experiments to Assess Cognitive Processes
• Establishing Controls in Psychological Experiments | Experiments | Psychology
• Top 9 Experiments on Sensation | Experimental Psychology

Experiments , Experimental Psychology , Attention , Experiments on Attention

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Elucidation of the effect of brain cortex tetrapeptide Cortagen on gene expression in mouse heart by microarray

Affiliation.

• 1 Saint-Petersburg Institute of Bioregulation and Gerontology, RAMS, St.-Petersburg, Russia.
• PMID: 15159690

Objectives: Aging is associated with significant alterations in gene expression in numerous organs and tissues. Anti-aging therapy with peptide bioregulators holds much promise for the correction of age-associated changes, making a screening for their molecular targets in tissues an important question of modern gerontology. The synthetic tetrapeptide Cortagen (Ala-Glu-Asp-Pro) was obtained by directed synthesis based on amino acid analysis of natural brain cortex peptide preparation Cortexin. In humans, Cortagen demonstrated a pronounced therapeutic effect upon the structural and functional posttraumatic recovery of peripheral nerve tissue. Importantly, other effects were also observed in cardiovascular and cerebrovascular parameters.

Design: Based on these latter observations, we hypothesized that acute course of Cortagen treatment, large-scale transcriptome analysis, and identification of transcripts with altered expression in heart would facilitate our understanding of the mechanisms responsible for this peptide biological effects. We therefore analyzed the expression of 15,247 transcripts in the heart of female 6-months CBA mice receiving injections of Cortagen for 5 consecutive days was studied by cDNA microarrays.

Results: Comparative analysis of cDNA microarray hybridisation with heart samples from control and experimental group revealed 234 clones (1,53% of the total number of clones) with significant changes of expression that matched 110 known genes belonging to various functional categories. Maximum up- and down-regulation was +5.42 and -2.86, respectively.

Conclusion: Intercomparison of changes in cardiac expression profile induced by synthetic peptides (Cortagen, Vilon, Epitalon) and pineal peptide hormone melatonin revealed both common and specific effects of Cortagen upon gene expression in heart.

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Nutritional Impacts of Dietary Selenium, Iodine and their Interaction on Egg Performance, and Antioxidant Profile in Laying Longyuan Duck Breeders

• Open access
• Published: 24 August 2024

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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  

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.

Data Availability

All data generated or analyzed during this study are included in this article.

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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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Contributions

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.

Corresponding author

Correspondence to Wei Chen .

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The experimental protocol used in this study, including animal management, housing, and slaughter procedures, was approved by the Animal Care and Use Committee of the Guangdong Academy of Agricultural Sciences (ACUCGAAC 2019).

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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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Received : 01 March 2024

Accepted : 10 July 2024

Published : 24 August 2024

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

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