what types of variables in a controlled experiment

What are the variables in a science experiment?

What are the Variables in a Science Experiment?

When conducting a science experiment, scientists aim to understand the underlying principles and relationships between variables. A variable is any factor, factor, or condition that can affect the outcome of an experiment. In this article, we will explore the different types of variables, their importance, and how to identify and control them.

Independent Variables

What is an Independent Variable?

An independent variable is a factor that is intentionally changed or manipulated by the experimenter to observe its effect on the dependent variable. This variable is also known as the "cause" or "cause-and-effect" variable. The independent variable is the factor that is being tested or controlled to determine its impact on the outcome of the experiment.

• Example: In a study on the effect of exercise on blood pressure, the independent variable is the amount of exercise, and the dependent variable is blood pressure.

Dependent Variables

What is a Dependent Variable?

A dependent variable is the factor that is being measured or observed in response to changes in the independent variable. This variable is also known as the "effect" or "result" variable. The dependent variable is the outcome or response being measured.

• Example: In the same study on the effect of exercise on blood pressure, the dependent variable is blood pressure.

Control Variables

What are Control Variables?

Control variables are those variables that are not being intentionally changed or manipulated by the experimenter. They are factors that could potentially affect the outcome of the experiment and must be controlled for to ensure the experiment is valid.

• Example: In a study on the effect of a new medication on symptoms of a disease, the control variables include factors such as diet, exercise, and environmental conditions that could also affect the outcome.

Types of Variables

There are several types of variables, including:

• Continuous variables : These variables can take on any value within a specific range, such as temperature or weight.
• Discrete variables : These variables can only take on specific values, such as whole numbers or categorical values, such as male or female.
• Nominal variables : These variables have no inherent scale or measurement, such as eye color or occupation.

Importance of Variables in Science Experiments

Variables play a crucial role in science experiments, as they help to:

• Identify cause-and-effect relationships : By manipulating the independent variable and measuring the resulting change in the dependent variable, scientists can identify the cause-and-effect relationships between variables.
• Test hypotheses : Variables help scientists to test their hypotheses and theories.
• Conduct controlled experiments : By controlling for control variables, scientists can ensure that the experiment is valid and that the results are due to the independent variable and not other factors.

How to Identify and Control Variables

To identify and control variables, scientists use the following steps:

• Conduct a literature review : Review existing research to identify the variables that have been studied previously and how they were controlled.
• Identify the research question : Clearly define the research question and the variables to be studied.
• Design the experimental protocol : Determine the experimental design, including the independent and dependent variables, and the control variables.
• Collect and analyze data : Collect and analyze the data, controlling for significant variables and ensuring that the results are due to the independent variable and not other factors.

In conclusion, variables are a crucial component of science experiments, helping to identify cause-and-effect relationships, test hypotheses, and conduct controlled experiments. By understanding the different types of variables, including independent, dependent, and control variables, scientists can design and conduct experiments that produce reliable and valid results. By following the steps to identify and control variables, scientists can ensure that their experiments are valid and produce meaningful results.

Table: Common Variable Types

Key Takeaways

• Variables are factors that can affect the outcome of a science experiment.
• Independent variables are intentionally changed or manipulated by the experimenter.
• Dependent variables are the outcomes or responses being measured.
• Control variables are factors that could potentially affect the outcome of the experiment.
• Understanding the different types of variables is crucial for designing and conducting effective science experiments.
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Types of Variables in Science Experiments

In a science experiment , a variable is any factor, attribute, or value that describes an object or situation and is subject to change. An experiment uses the scientific method to test a hypothesis and establish whether or not there is a cause and effect relationship between two variables: the independent and dependent variables. But, there are other important types of variables, too, including controlled and confounding variables. Here’s what you need to know, with examples.

The Three Main Types of Variables – Independent, Dependent, and Controlled

An experiment examines whether or not there is a relationship between the independent and dependent variables. The independent variable is the one factor a researcher intentionally changes or manipulates. The dependent variable is the factor that is measured, to see how it responds to the independent variable.

For example , consider an experiment looking to see whether taking caffeine affects how many words you remember from a list. The independent variable is the amount of caffeine you take, while the dependent variable is how many words you remember.

But, there are lot more potential variables you control (and usually measure and record) so you get the truest results from the experiment. The controlled variables are factors you hold steady so they don’t affect the results. In this experiment, examples include the amount and source of the caffeine (coffee? tea? caffeine tablets?), the time between taking the caffeine and recalling the words, the number and order of words on the list, the temperature of the room, and anything else you think might matter. Observing and recording controlled variables might not seem very important, but if someone goes to repeat your experiment and gets different results, it might turn out that a controlled variable has a bigger effect than you suspected!

Confounding Variables

A confounding variable is a variable that has a hidden effect on the results. Sometimes, once you identify a confounding variable, you can turn it into a controlled variable in a later experiment. In the coffee experiment, examples of confounding variables include a subject’s sensitivity to caffeine and the time of day that you conduct the experiment. Age and initial hydration levels are additional factors that may confound the results.

Other Types of Variables

Other types of variables get their names from special properties:

• Binary variable : A binary variable has exactly two states. Examples include on/off and heads/tails.
• Categorical or qualitative variable : A categorical or qualitative variable is one that does not have a numerical value. For example, if you compare the health benefits of walking, riding a bike, or driving a car, the modes of transport are descriptive and not numerical.
• Composite variable : A composite variable is a combination of multiple variable. Researchers use these for improving ease of data reporting. For example, a “good” water quality score includes samples that are low in turbidity, bacteria, heavy metals, and pesticides.
• Continuous variable : A continuous variable has an infinite number of values within a set range. For example, the height of a building ranges anywhere between zero and some maximum. When you measure the value, there is some level of error, often from rounding.
• Discrete variable : In contrast to a continuous variable, a discrete variable has a finite number of exact values. For example, a light is either on or off. The number of people in a room has an exact value (4 and never 3.91).
• Latent variable : A latent variable is one you can’t measure directly. For example, you can’t tell the salt tolerance of a plant, but can infer it by whether leaves appear healthy.
• Nominal variable : A nominal variable is a type of qualitative variable, where the attribute has a name or category instead of a number. For example, colors and brand names are nominal variables.
• Numeric or quantitative variable : This is a variable that has a numerical value. Length and mass are good examples.
• Ordinal variable : An ordinal variable has a ranked value. For example, rating a factor as bad, good, better, or best illustrates an ordinal system.
• Babbie, Earl R. (2009). The Practice of Social Research (12th ed.). Wadsworth Publishing. ISBN 0-495-59841-0.
• Creswell, John W. (2018). Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research (6th ed.). Pearson. ISBN 978-0134519364.
• Dodge, Y. (2008). The Concise Encyclopedia of Statistics. Springer Reference. ISBN 978-0397518371.
• Given, Lisa M. (2008). The SAGE Encyclopedia of Qualitative Research Methods . Los Angeles: SAGE Publications. ISBN 978-1-4129-4163-1.
• Kuhn, Thomas S. (1961). “The Function of Measurement in Modern Physical Science”. Isis . 52 (2): 161–193 (162). doi: 10.1086/349468

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25 Control Variables Examples

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Control variables, sometimes called “controlled” variables or “constant” variables, are elements within a study that researchers deliberately keep constant.

In a research study, it is often required to determine the possible impact of one or more independent variables on a dependent variable. To maintain the validity of the results, scientists keep certain variables in check, known as the control variables, ensuring they do not influence the study outcome.

Through careful control of these variables, scientists can prevent confounding effects, allowing for the clear understanding of the relationship between the independent and dependent variables (Scharrer & Ramasubramanian 2021; Knapp 2017).

Control Variables Examples

Here are some concrete examples to better understand the role of control variables:

1. Participant Age When studying the effect of a new teaching method on students’ mathematical abilities, the age of the participants (all students studied are in the 8th grade) remains a control variable.

2. Participant Gender In investigating the impact of a physical fitness program on participants’ cardiovascular health, researchers control for participants’ gender (only female participants are included).

3. Socioeconomic Status (SES) While examining the effect of job training programs on employment rates, scientists control the socioeconomic status of participants (all participants fall under the same socioeconomic category).

4. Educational Level In a research study examining the impact of management styles on worker productivity, educational level (all workers involved hold a Bachelor’s degree in their corresponding fields) is considered a control variable.

5. Cultural Background In studying the influence of music therapy on stress reduction, researchers maintain cultural background constant (only participants from a specific cultural group are included).

6. Time of Day If a researcher is testing the effect of caffeine on alertness, the time of day (all tests are conducted in the morning) is controlled to ensure that circadian rhythms do not confound results.

7. Previous Experience In evaluating the effectiveness of a new software tutorial, previous experience with the software (all participants are novice users) is hold constant to avoid confounding effects.

8. Medication Usage When researching the correlation between a balanced diet and blood pressure, medication usage (none of the participants are on any medication) is a control variable.

9. Sleep Quality In correlating cognitive performance and sleep patterns, sleep quality (all participants are healthy sleepers, as assessed by a sleep quality questionnaire) is maintained constant.

10. Hunger/Fullness While exploring the link between taste perception and caloric intake, researchers control for hunger/fullness (all tests are conducted two hours after a standardized meal) to eliminate any potential confounding effects.

11. Caffeine Intake When evaluating the impact of a mindfulness exercise on attention spans, caffeine intake (participants are required to abstain from caffeine on the day of the testing) is controlled.

12. Mental Health Status During a research study exploring the effects of exercise on sleep quality, the mental health status of participants (all participants do not have any known mental health issues as per a screening survey) is kept constant.

13. Motivation Level In research on the effectiveness of a language learning app, the motivation level (participants are all deemed to have a high level of motivation as assessed by a standardized motivational questionnaire) is a control variable.

14. Instructions Given When scientists are studying the effect of a new fitness routine on muscle strength, the instructions given (all participants receive the same detailed instructions about the exercises) remain consistent.

15. Testing Environment In studying the impact of ambient noise on focus and concentration, the testing environment (all testing is conducted in a silent room) is controlled for.

16. Researcher Presence While experimenting to assess the influence of color on memory recall, researcher presence (all testing happens without the presence of the researcher to avoid pressure or distraction) is kept constant.

17. Mode of Data Collection When comparing coping styles and resilience, mode of data collection (all data is collected through online self-report surveys) is controlled.

18. Order of Questionnaires or Tasks During a study to understand the relation between personality traits and career choices, the order of questionnaires or tasks (participants are all subjected to the tasks and questionnaires in the exact same order) is maintained same.

19. Familiarity with Technology In researching the benefits of virtual reality in improving social skills, the familiarity with technology (all participants have basic computer skills) is considered constant.

20. Expectations/Briefing In a study of the correlation between study habits and academic performance, expectations/briefing about the study (all participants receive the same briefing regarding what the study entails) is controlled to maintain uniformity.

21. Physical Activity Level In a study analyzing the correlation between diet and energy levels, the physical activity level of participants (all participants engage in a moderate level of daily physical activity) is controlled.

22. Stress Levels When researching the impact of sleep duration on cognitive functions , the stress level of participants (all participants have reported average stress levels on a standard stress scale) is kept constant.

23. Relationship Status In researching the influence of relationships on happiness levels, the relationship status of participants (all participants are single at the time of the study) is kept constant.

24. Number of Hours Worked Recently While examining the effect of work-life balance on the job satisfaction of employees, the number of hours worked recently (all employees have worked standard 40 hour weeks) is considered a control variable.

25. Current Emotional State In a study evaluating the impact of a relaxation technique on anxiety levels, the current emotional state of the participants (all participants have to record a neutral emotional state at the time of testing) is maintained constant.

Related: Quantitative Reasoning Examples

How to Control a Variable

Controlling a variable in a research study involves ensuring that it is kept constant or unchanged throughout the entire experiment.

This technique allows the researchers to focus on the potential relationship between the remaining variables, the independent variable(s) and the dependent variable (Sproull, 2002).

Here’s an outline of the process:

• Identify Potential Control Variables Before beginning the experiment, identify all the variables that might potentially affect the outcome of your research. This process can be informed by a literature review on similar studies, brainstorming sessions, or consultations with other professionals in the field.
• Define the Conditions of Control Set specific conditions for each control variable. For example, if you’re studying the effects of a new teaching method on student learning outcomes, the students’ grade level might be a control variable. You would then decide to limit your study to only 8th-grade students.
• Maintain Consistent Environment Ensure that the environment or conditions in which your research is carried out stay constant. Changes in external variables might indirectly alter your control variables.
• Monitor Regularly Record data related to your control variables regularly. If there are changes, they will need to be corrected or accounted for in your final analysis.
• Analyze the Confounding Effect Once your experiment is completed, you should perform a statistical analysis to ensure that your controlled variables did not influence the outcome.

By regularly monitoring and adjusting these variables, you can limit their influence on your study, increasing the odds that any observed effects are due to the independent variable(s).

It’s important to note that it’s not always possible to control every variable in a study and that’s okay. In such cases, it is important that the researchers are aware of these uncontrollable variables and can discuss their potential impact when interpreting the results.

Types of Control Variables: Positive and Negative

Positive and negative controls are two types of control groups in experimental research. They act as a benchmark and provide context for interpreting the results of the experiment.

• Positive control refers to a test where the outcome is already known from the onset. It is implemented to ensure that an experimental procedure is working as intended. It is crucial for validating the test results and serves as a benchmark for comparison. These controls are used across various disciplines, from biology to engineering, cultivates consistency, reliability, and accuracy in experimental work.
• Negative control is a test that anticipates a negative result. It is carried out to ensure that no change occurs when no experimental variable is introduced. The key purpose of such controls is to rule out other factors that might lead to a change in the outcome. Overall, negative controls add credence to the experimental process, helping to confirm that observed changes in the positive control or experimental test result from the factor being tested.

Both positive and negative controls contribute to experimental reliability and validity. They allow scientists to have confidence in their results by reducing the likelihood of experimental error. They also facilitate a better understanding of the experimental processes and outcomes, which is key in research and experimentation.

These controls are, in essence, safeguards against inaccurate or skewed results, ensuring that the conclusions drawn are as accurate as possible, thus avoiding misleading deductions.

Go Deeper: Positive Control vs Negative Control

Control vs Confounding Variables

Control Variables and Confounding Variables each have substantial importance in research studies, and need to be accounted for. Both types of variables can influence results, but they serve different roles in the research process.

• Control Variables: Control variables are the variables that researchers control throughout a study, usually by ensuring they remain consistent and unchanged throughout the study (Lock et al., 2020; Parker & Berman, 2016). By controlling these variables, researchers can reduce the number of extraneous factors that could interfere with the results, thereby minimizing potential error, ensuring the integrity of the experiment, and reducing the risk of false outcomes.
• Confounding Variables : Confounding variables may pose a risk to the validity of a study’s results (Nestor & Schutt, 2018). These are variables that researchers didn’t account for, and they may influence both the independent and dependent variables, making it hard to determine if the effects were caused by the independent variable or the confounder.

The primary difference between control and confounding variables is how they’re managed in a study. Control variables are identified and kept constant by the researcher to isolate the relationship between the independent and dependent variables (Boniface, 2019; Lock et al., 2020).

On the other hand, confounding variables are extraneous factors that can influence the study results and have not been controlled (Riegelman, 2020). While researchers aim to identify possible confounding variables before a study to control or account for them, they often become clear during or after the experiment, introducing uncertainty about causation between dependent and independent variables.

Control variables are critical to maintaining the integrity and validity of research studies. By carefully selecting and managing these variables, researchers can limit confounding influences, allowing them to focus on the relationship between the independent and dependent variables. Understanding control variables assists researchers in developing robust study designs and reliable findings.

Boniface, D. R. (2019). Experiment Design and Statistical Methods For Behavioural and Social Research . CRC Press. ISBN: 9781351449298.

Knapp, H. (2017). Intermediate Statistics Using SPSS. SAGE Publications.

Lock, R. H., Lock, P. F., Morgan, K. L., Lock, E. F., & Lock, D. F. (2020). Statistics: Unlocking the Power of Data (3rd ed.). Wiley.

Nestor, P. G., & Schutt, R. K. (2018). Research Methods in Psychology: Investigating Human Behavior . SAGE Publications.

Parker, R. A., & Berman, N. G. (2016). Planning Clinical Research . Cambridge University Press.

Riegelman, R. K. (2020). Studying a Study and Testing a Test (7th ed.). Wolters Kluwer Health.

Scharrer, E., & Ramasubramanian, S. (2021). Quantitative Research Methods in Communication: The Power of Numbers for Social Justice . Taylor & Francis.

Sproull, N. L. (2002). Handbook of Research Methods: A Guide for Practitioners and Students in the Social Sciences . Scarecrow Press.

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Experimental Design - Independent, Dependent, and Controlled Variables

Scientific experiments are meant to show cause and effect of a phenomena (relationships in nature).  The “ variables ” are any factor, trait, or condition that can be changed in the experiment and that can have an effect on the outcome of the experiment.

An experiment can have three kinds of variables: i ndependent, dependent, and controlled .

• The independent variable is one single factor that is changed by the scientist followed by observation to watch for changes. It is important that there is just one independent variable, so that results are not confusing.
• The dependent variable is the factor that changes as a result of the change to the independent variable.
• The controlled variables (or constant variables) are factors that the scientist wants to remain constant if the experiment is to show accurate results. To be able to measure results, each of the variables must be able to be measured.

For example, let’s design an experiment with two plants sitting in the sun side by side. The controlled variables (or constants) are that at the beginning of the experiment, the plants are the same size, get the same amount of sunlight, experience the same ambient temperature and are in the same amount and consistency of soil (the weight of the soil and container should be measured before the plants are added). The independent variable is that one plant is getting watered (1 cup of water) every day and one plant is getting watered (1 cup of water) once a week. The dependent variables are the changes in the two plants that the scientist observes over time.

Can you describe the dependent variable that may result from this experiment? After four weeks, the dependent variable may be that one plant is taller, heavier and more developed than the other. These results can be recorded and graphed by measuring and comparing both plants’ height, weight (removing the weight of the soil and container recorded beforehand) and a comparison of observable foliage.

Using What You Learned: Design another experiment using the two plants, but change the independent variable. Can you describe the dependent variable that may result from this new experiment?

Think of another simple experiment and name the independent, dependent, and controlled variables. Use the graphic organizer included in the PDF below to organize your experiment's variables.

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Controlled Experiment

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Associate Editor for Simply Psychology

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

This is when a hypothesis is scientifically tested.

In a controlled experiment, an independent variable (the cause) is systematically manipulated, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

The researcher can operationalize (i.e., define) the studied variables so they can be objectively measured. The quantitative data can be analyzed to see if there is a difference between the experimental and control groups.

What is the control group?

In experiments scientists compare a control group and an experimental group that are identical in all respects, except for one difference – experimental manipulation.

Unlike the experimental group, the control group is not exposed to the independent variable under investigation and so provides a baseline against which any changes in the experimental group can be compared.

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.

Randomly allocating participants to independent variable groups means that all participants should have an equal chance of participating 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.

What are extraneous variables?

The researcher wants to ensure that the manipulation of the independent variable has changed the changes in the dependent variable.

Hence, all the other variables that could affect the dependent variable to change must be controlled. These other variables are called extraneous or confounding variables.

Extraneous variables should be controlled were possible, as they might be important enough to provide alternative explanations for the effects.

In practice, it would be difficult to control all the variables in a child’s educational achievement. For example, it would be difficult to control variables that have happened in the past.

A researcher can only control the current environment of participants, such as time of day and noise levels.

Why conduct controlled experiments?

Scientists use controlled experiments because they allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.

Controlled experiments also follow a standardized step-by-step procedure. This makes it easy for another researcher to replicate the study.

Key Terminology

Experimental group.

The group being treated or otherwise manipulated for the sake of the experiment.

Control Group

They receive no treatment and are used as a comparison group.

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 that 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 participating in each condition.

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.

What is the control in an experiment?

In an experiment , the control is a standard or baseline group not exposed to the experimental treatment or manipulation. It serves as a comparison group to the experimental group, which does receive the treatment or manipulation.

The control group helps to account for other variables that might influence the outcome, allowing researchers to attribute differences in results more confidently to the experimental treatment.

Establishing a cause-and-effect relationship between the manipulated variable (independent variable) and the outcome (dependent variable) is critical in establishing a cause-and-effect relationship between the manipulated variable.

What is the purpose of controlling the environment when testing a hypothesis?

Controlling the environment when testing a hypothesis aims to eliminate or minimize the influence of extraneous variables. These variables other than the independent variable might affect the dependent variable, potentially confounding the results.

By controlling the environment, researchers can ensure that any observed changes in the dependent variable are likely due to the manipulation of the independent variable, not other factors.

This enhances the experiment’s validity, allowing for more accurate conclusions about cause-and-effect relationships.

It also improves the experiment’s replicability, meaning other researchers can repeat the experiment under the same conditions to verify the results.

Why are hypotheses important to controlled experiments?

Hypotheses are crucial to controlled experiments because they provide a clear focus and direction for the research. A hypothesis is a testable prediction about the relationship between variables.

It guides the design of the experiment, including what variables to manipulate (independent variables) and what outcomes to measure (dependent variables).

The experiment is then conducted to test the validity of the hypothesis. If the results align with the hypothesis, they provide evidence supporting it.

The hypothesis may be revised or rejected if the results do not align. Thus, hypotheses are central to the scientific method, driving the iterative inquiry, experimentation, and knowledge advancement process.

What is the experimental method?

The experimental method is a systematic approach in scientific research where an independent variable is manipulated to observe its effect on a dependent variable, under controlled conditions.

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Controlled Experiments | Methods & Examples of Control

Published on 19 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

In experiments , researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment , all variables other than the independent variable are controlled or held constant so they don’t influence the dependent variable.

Controlling variables can involve:

• Holding variables at a constant or restricted level (e.g., keeping room temperature fixed)
• Measuring variables to statistically control for them in your analyses
• Balancing variables across your experiment through randomisation (e.g., using a random order of tasks)

Table of contents

Why does control matter in experiments, methods of control, problems with controlled experiments, frequently asked questions about controlled experiments.

Control in experiments is critical for internal validity , which allows you to establish a cause-and-effect relationship between variables.

• Your independent variable is the colour used in advertising.
• Your dependent variable is the price that participants are willing to pay for a standard fast food meal.

Extraneous variables are factors that you’re not interested in studying, but that can still influence the dependent variable. For strong internal validity, you need to remove their effects from your experiment.

• Design and description of the meal
• Study environment (e.g., temperature or lighting)
• Participant’s frequency of buying fast food
• Participant’s familiarity with the specific fast food brand
• Participant’s socioeconomic status

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You can control some variables by standardising your data collection procedures. All participants should be tested in the same environment with identical materials. Only the independent variable (e.g., advert colour) should be systematically changed between groups.

Other extraneous variables can be controlled through your sampling procedures . Ideally, you’ll select a sample that’s representative of your target population by using relevant inclusion and exclusion criteria (e.g., including participants from a specific income bracket, and not including participants with colour blindness).

By measuring extraneous participant variables (e.g., age or gender) that may affect your experimental results, you can also include them in later analyses.

After gathering your participants, you’ll need to place them into groups to test different independent variable treatments. The types of groups and method of assigning participants to groups will help you implement control in your experiment.

Control groups

Controlled experiments require control groups . Control groups allow you to test a comparable treatment, no treatment, or a fake treatment, and compare the outcome with your experimental treatment.

You can assess whether it’s your treatment specifically that caused the outcomes, or whether time or any other treatment might have resulted in the same effects.

• A control group that’s presented with red advertisements for a fast food meal
• An experimental group that’s presented with green advertisements for the same fast food meal

Random assignment

To avoid systematic differences between the participants in your control and treatment groups, you should use random assignment .

This helps ensure that any extraneous participant variables are evenly distributed, allowing for a valid comparison between groups .

Random assignment is a hallmark of a ‘true experiment’ – it differentiates true experiments from quasi-experiments .

Masking (blinding)

Masking in experiments means hiding condition assignment from participants or researchers – or, in a double-blind study , from both. It’s often used in clinical studies that test new treatments or drugs.

Sometimes, researchers may unintentionally encourage participants to behave in ways that support their hypotheses. In other cases, cues in the study environment may signal the goal of the experiment to participants and influence their responses.

Using masking means that participants don’t know whether they’re in the control group or the experimental group. This helps you control biases from participants or researchers that could influence your study results.

Although controlled experiments are the strongest way to test causal relationships, they also involve some challenges.

Difficult to control all variables

Especially in research with human participants, it’s impossible to hold all extraneous variables constant, because every individual has different experiences that may influence their perception, attitudes, or behaviors.

But measuring or restricting extraneous variables allows you to limit their influence or statistically control for them in your study.

Risk of low external validity

Controlled experiments have disadvantages when it comes to external validity – the extent to which your results can be generalised to broad populations and settings.

The more controlled your experiment is, the less it resembles real world contexts. That makes it harder to apply your findings outside of a controlled setting.

There’s always a tradeoff between internal and external validity . It’s important to consider your research aims when deciding whether to prioritise control or generalisability in your experiment.

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

To design a successful experiment, first identify:

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

When designing the experiment, first decide:

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

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Bhandari, P. (2022, October 10). Controlled Experiments | Methods & Examples of Control. Scribbr. Retrieved 16 December 2024, from https://www.scribbr.co.uk/research-methods/controlled-experiments/

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Pritha Bhandari