control variable in research

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What Is a Control Variable? Definition and Examples

A control variable is any factor that is controlled or held constant during an experiment . For this reason, it’s also known as a controlled variable or a constant variable. A single experiment may contain many control variables . Unlike the independent and dependent variables , control variables aren’t a part of the experiment, but they are important because they could affect the outcome. Take a look at the difference between a control variable and control group and see examples of control variables.

Importance of Control Variables

Remember, the independent variable is the one you change, the dependent variable is the one you measure in response to this change, and the control variables are any other factors you control or hold constant so that they can’t influence the experiment. Control variables are important because:

• They make it easier to reproduce the experiment.
• The increase confidence in the outcome of the experiment.

For example, if you conducted an experiment examining the effect of the color of light on plant growth, but you didn’t control temperature, it might affect the outcome. One light source might be hotter than the other, affecting plant growth. This could lead you to incorrectly accept or reject your hypothesis. As another example, say you did control the temperature. If you did not report this temperature in your “methods” section, another researcher might have trouble reproducing your results. What if you conducted your experiment at 15 °C. Would you expect the same results at 5 °C or 35 5 °C? Sometimes the potential effect of a control variable can lead to a new experiment!

Sometimes you think you have controlled everything except the independent variable, but still get strange results. This could be due to what is called a “ confounding variable .” Examples of confounding variables could be humidity, magnetism, and vibration. Sometimes you can identify a confounding variable and turn it into a control variable. Other times, confounding variables cannot be detected or controlled.

Control Variable vs Control Group

A control group is different from a control variable. You expose a control group to all the same conditions as the experimental group, except you change the independent variable in the experimental group. Both the control group and experimental group should have the same control variables.

Control Variable Examples

Anything you can measure or control that is not the independent variable or dependent variable has potential to be a control variable. Examples of common control variables include:

• Duration of the experiment
• Size and composition of containers
• Temperature
• Sample volume
• Experimental technique
• Chemical purity or manufacturer
• Species (in biological experiments)

For example, consider an experiment testing whether a certain supplement affects cattle weight gain. The independent variable is the supplement, while the dependent variable is cattle weight. A typical control group would consist of cattle not given the supplement, while the cattle in the experimental group would receive the supplement. Examples of control variables in this experiment could include the age of the cattle, their breed, whether they are male or female, the amount of supplement, the way the supplement is administered, how often the supplement is administered, the type of feed given to the cattle, the temperature, the water supply, the time of year, and the method used to record weight. There may be other control variables, too. Sometimes you can’t actually control a control variable, but conditions should be the same for both the control and experimental groups. For example, if the cattle are free-range, weather might change from day to day, but both groups have the same experience. When you take data, be sure to record control variables along with the independent and dependent variable.

• Box, George E.P.; Hunter, William G.; Hunter, J. Stuart (1978). Statistics for Experimenters : An Introduction to Design, Data Analysis, and Model Building . New York: Wiley. ISBN 978-0-471-09315-2.
• Giri, Narayan C.; Das, M. N. (1979). Design and Analysis of Experiments . New York, N.Y: Wiley. ISBN 9780852269145.
• Stigler, Stephen M. (November 1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

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• What Are Control Variables | Definition & Examples

What Are Control Variables? | Definition & Examples

Published on 4 May 2022 by Pritha Bhandari . Revised on 16 June 2023.

A control variable is anything that is held constant or limited in a research study. It’s a variable that is not of interest to the study’s aims but is controlled because it could influence the outcomes.

Variables may be controlled directly by holding them constant throughout a study (e.g., by controlling the room temperature in an experiment), or they may be controlled indirectly through methods like randomisation or statistical control (e.g., to account for participant characteristics like age in statistical tests).

Table of contents

Why do control variables matter, how do you control a variable, control variable vs control group, frequently asked questions about control variables.

Control variables enhance the internal validity of a study by limiting the influence of confounding and other extraneous variables . This helps you establish a correlational or causal relationship between your variables of interest.

Aside from the independent and dependent variables , all variables that can impact the results should be controlled. If you don’t control relevant variables, you may not be able to demonstrate that they didn’t influence your results. Uncontrolled variables are alternative explanations for your results.

Control variables in experiments

In an experiment , a researcher is interested in understanding the effect of an independent variable on a dependent variable. Control variables help you ensure that your results are solely caused by your experimental manipulation.

The independent variable is whether the vitamin D supplement is added to a diet, and the dependent variable is the level of alertness.

To make sure any change in alertness is caused by the vitamin D supplement and not by other factors, you control these variables that might affect alertness:

• Timing of meals
• Caffeine intake
• Screen time

Control variables in non-experimental research

In an observational study or other types of non-experimental research, a researcher can’t manipulate the independent variable (often due to practical or ethical considerations ). Instead, control variables are measured and taken into account to infer relationships between the main variables of interest.

To account for other factors that are likely to influence the results, you also measure these control variables:

• Marital status

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There are several ways to control extraneous variables in experimental designs, and some of these can also be used in observational or quasi-experimental designs.

Random assignment

In experimental studies with multiple groups, participants should be randomly assigned to the different conditions. Random assignment helps you balance the characteristics of groups so that there are no systematic differences between them.

This method of assignment controls participant variables that might otherwise differ between groups and skew your results.

It’s possible that the participants who found the study through Facebook have more screen time during the day, and this might influence how alert they are in your study.

Standardised procedures

It’s important to use the same procedures across all groups in an experiment. The groups should only differ in the independent variable manipulation so that you can isolate its effect on the dependent variable (the results).

To control variables, you can hold them constant at a fixed level using a protocol that you design and use for all participant sessions. For example, the instructions and time spent on an experimental task should be the same for all participants in a laboratory setting.

• To control for diet, fresh and frozen meals are delivered to participants three times a day.
• To control meal timings, participants are instructed to eat breakfast at 9:30, lunch at 13:00, and dinner at 18:30.
• To control caffeine intake, participants are asked to consume a maximum of one cup of coffee a day.

Statistical controls

You can measure and control for extraneous variables statistically to remove their effects on other variables.

“Controlling for a variable” means modelling 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.

A control variable isn’t the same as a control group . Control variables are held constant or measured throughout a study for both control and experimental groups, while an independent variable varies between control and experimental groups.

A control group doesn’t undergo the experimental treatment of interest, and its outcomes are compared with those of the experimental group. A control group usually has either no treatment, a standard treatment that’s already widely used, or a placebo (a fake treatment).

Aside from the experimental treatment, everything else in an experimental procedure should be the same between an experimental and control group.

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.

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 .

‘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.

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.

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Home » Control Variable – Definition, Types and Examples

Control Variable – Definition, Types and Examples

Table of Contents

Control Variable

Definition :

Control variable, also known as a “constant variable,” is a variable that is held constant or fixed during an experiment or study to prevent it from affecting the outcome. In other words, a control variable is a variable that is kept the same or held constant to isolate the effects of the independent variable on the dependent variable.

For example, if you were conducting an experiment to test how temperature affects plant growth, you might want to control variables such as the amount of water, the amount of sunlight, and the type of soil to ensure that these variables do not interfere with the results. By controlling these variables, you can isolate the effect of temperature on plant growth and draw more accurate conclusions from the experiment.

Types of Control Variables

Types of Control Variables are as follows:

Environmental Control Variables

These are variables related to the physical environment in which the experiment is conducted, such as temperature, humidity, light, and sound.

Participant Control Variables

These are variables related to the participants in the experiment, such as age, gender, prior knowledge, or experience.

Experimental Control Variables

These are variables that the researcher manipulates or controls to ensure that they do not affect the outcome of the experiment. For example, in a study on the effects of a new medication, the researcher might control the dosage, frequency, or duration of the treatment.

Procedural Control Variables

These are variables related to the procedures or methods used in the experiment, such as the order in which tasks are completed, the timing of measurements, or the instructions given to participants.

Equipment Control Variables

These are variables related to the equipment or instruments used in the experiment, such as calibration, maintenance, or proper functioning.

How to Control a Variable

To control a variable in a scientific experiment, you need to ensure that it is kept constant or unchanged throughout the experiment. Here are some steps to help you control a variable:

Identify the Variable

Start by identifying the variable that you want to control. This can be an environmental, subject, procedural, or instrumentation variable.

Determine the Level of Control Needed

Depending on the variable, you may need to exert varying levels of control. For example, environmental variables may require you to control the temperature, humidity, and lighting in your experiment, while subject variables may require you to select a specific group of participants that meet certain criteria.

Establish a Standard Level

Determine the standard level or value of the variable that you want to control. For example, if you are controlling the temperature, you may set the temperature to a specific degree and ensure that it is maintained at that level throughout the experiment.

Monitor the Variable

Throughout the experiment, monitor the variable to ensure that it remains constant. Use appropriate equipment or instruments to measure the variable and make adjustments as necessary to maintain the desired level.

Document the Process

Document the process of controlling the variable to ensure that the experiment is replicable. This includes documenting the standard level, monitoring procedures, and any adjustments made during the experiment.

Examples of Control Variables

Here are some examples of control variables in Scientific Experiments and Research:

• Environmental Control Variables Example: Suppose you are conducting an experiment to study the effect of light on plant growth. You would want to control environmental factors such as temperature, humidity, and soil nutrients. In this case, you might keep the temperature and humidity constant and use the same type and amount of soil for all the plants.
• Subject Control Variables Example : If you are conducting an experiment to study the effect of a new medication on blood pressure, you would want to control subject variables such as age, gender, and health status. In this case, you might select a group of participants with similar ages, genders, and health conditions to ensure that these variables do not affect the results.
• Procedural Control Variables Example : Suppose you are conducting an experiment to study the effect of distraction on reaction time. You would want to control procedural variables such as the time of day, the order of the tasks, and the instructions given to the participants. In this case, you might ensure that all participants perform the tasks in the same order, at the same time of day, and receive the same instructions.
• Instrumentation Control Variables Example : If you are conducting an experiment to study the effect of a new measurement device on the accuracy of readings, you would want to control instrumentation variables such as the type and calibration of the device. In this case, you might use the same type and model of the device and ensure that it is calibrated before each use.

Applications of Control Variable

Control variables are widely used in scientific research across various fields, including physics, biology, psychology, and engineering. Here are some applications of control variables:

• In medical research , control variables are used to ensure that any observed effects of a new treatment or medication are due to the treatment and not some other variable. By controlling subject variables such as age, gender, and health status, researchers can isolate the effects of the treatment and determine its effectiveness.
• In environmental research , control variables are used to study the effects of changes in the environment on various species or ecosystems. By controlling environmental variables such as temperature, humidity, and lighting, researchers can determine how different species adapt to changes in the environment.
• In psychology research, control variables are used to study the effects of different interventions or therapies on cognitive or behavioral outcomes. By controlling procedural variables such as the order of tasks, the length of time allotted for each task, and the instructions given to participants, researchers can isolate the effects of the intervention and determine its effectiveness.
• In engineering research, control variables are used to study the effects of different design parameters on the performance of a system or device. By controlling instrumentation variables such as the type of measurement device used and the calibration of instruments, researchers can ensure that the measurements are accurate and reliable.

Purpose of Control Variable

The purpose of a control variable in an experiment is to ensure that any observed changes or effects are a result of the manipulation of the independent variable and not some other variable. By keeping certain variables constant, researchers can isolate the effects of the independent variable and determine whether it has a significant effect on the dependent variable.

Control variables are important because they help to increase the reliability and validity of the experiment. Reliability refers to the consistency and reproducibility of the results, while validity refers to the accuracy and truthfulness of the results. By controlling variables, researchers can reduce the potential for extraneous or confounding variables that can affect the outcome of the experiment and increase the likelihood that the results accurately reflect the effect of the independent variable on the dependent variable.

Characteristics of Control Variable

Control variables have the following characteristics:

• Constant : Control variables are kept constant or unchanged throughout the experiment. This means that their values do not vary or change during the experiment. Keeping control variables constant helps to ensure that any observed effects or changes are due to the manipulation of the independent variable and not some other variable.
• Independent : Control variables are independent of the independent variable being studied. This means that they do not affect the relationship between the independent and dependent variables. By controlling for independent variables, researchers can isolate the effect of the independent variable and determine its impact on the dependent variable.
• Documented: Control variables are documented in the experiment. This means that their values and methods of control are recorded and reported in the results section of the research paper. By documenting control variables, researchers can demonstrate the rigor and transparency of their study and allow other researchers to replicate their methods.
• Relevant: Control variables are relevant to the research question. This means that they are chosen based on their potential to affect the outcome of the experiment. By selecting relevant control variables, researchers can reduce the potential for extraneous or confounding variables that can affect the outcome of the experiment and increase the reliability and validity of the results.
• Varied : Control variables can be varied across different conditions or groups. This means that different levels of control may be needed depending on the research question or hypothesis being tested. By varying control variables, researchers can test different hypotheses and determine the factors that affect the outcome of the experiment.

Advantages of Control Variable

The advantages of using control variables in an experiment are:

• Increased accuracy : Control variables help to increase the accuracy of the results by reducing the potential for extraneous or confounding variables that can affect the outcome of the experiment. By controlling for these variables, researchers can isolate the effect of the independent variable on the dependent variable and determine whether it has a significant impact.
• Increased reliability : Control variables help to increase the reliability of the results by reducing the variability in the experiment. By keeping certain variables constant, researchers can ensure that any observed changes or effects are due to the manipulation of the independent variable and not some other variable.
• Reproducibility: Control variables help to increase the reproducibility of the results by ensuring that the same results can be obtained when the experiment is repeated. By documenting and reporting control variables, researchers can demonstrate the rigor and transparency of their study and allow other researchers to replicate their methods.
• Generalizability : Control variables help to increase the generalizability of the results by reducing the potential for bias and increasing the external validity of the experiment. By controlling for relevant variables, researchers can ensure that their findings are applicable to a broader population or context.
• Causality : Control variables help to establish causality by ensuring that any observed changes or effects are due to the manipulation of the independent variable and not some other variable. By controlling for confounding variables, researchers can increase the internal validity of the experiment and establish a cause-and-effect relationship between the independent and dependent variables.

Disadvantages of Control Variable

There are some potential disadvantages or limitations of using control variables in an experiment:

• Complexity : Controlling for multiple variables can make an experiment more complex and time-consuming. This can increase the likelihood of errors and reduce the feasibility of the experiment, especially if the control variables require a lot of resources or are difficult to measure.
• Artificiality : Controlling for variables can make the experimental conditions artificial and not reflective of real-world situations. This can reduce the external validity of the experiment and limit the generalizability of the findings to real-world settings.
• Limited scope : Controlling for specific variables can limit the scope of the experiment and make it difficult to generalize the results to other situations or populations. This can reduce the external validity of the experiment and limit its practical applications.
• Assumptions: Controlling for variables requires making assumptions about which variables are relevant and how they should be controlled. These assumptions may not be valid or accurate, and the results of the experiment may be affected by uncontrolled variables that were not considered.
• Cost : Controlling for variables can be costly, especially if the control variables require additional resources or equipment. This can limit the feasibility of the experiment, especially for researchers with limited funding or resources.

Limitations of Control Variable

There are several limitations of using control variables in an experiment, including:

• Not all variables can be controlled : There may be some variables that cannot be controlled or manipulated in an experiment. For example, some variables may be too difficult or expensive to measure or control, or they may be affected by factors outside of the researcher’s control.
• Interaction effects : Control variables can interact with each other, which can lead to unexpected results. For example, controlling for one variable may have a different effect when another variable is also controlled, or when the two variables interact with each other. These interaction effects can be difficult to predict or control for.
• Over-reliance on statistical significance: Controlling for variables can increase the statistical significance of the results, but this may not always translate to practical significance or real-world significance. Researchers should interpret the results of an experiment in light of the practical significance, not just the statistical significance.
• Limited generalizability : Controlling for variables can limit the generalizability of the results to other populations or situations. If the control variables are not representative of other populations or situations, the results of the experiment may not be applicable to those contexts.
• May mask important effects : Controlling for variables can mask important effects that are related to the independent variable. By controlling for certain variables, researchers may miss important interactions between the independent variable and the controlled variable, which can limit the understanding of the causal relationship between the two.

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Statistics By Jim

Making statistics intuitive

Control Variables: Definition, Uses & Examples

By Jim Frost 4 Comments

What is a Control Variable?

Control variables, also known as controlled variables, are properties that researchers hold constant for all observations in an experiment. While these variables are not the primary focus of the research, keeping their values consistent helps the study establish the true relationships between the independent and dependent variables. The capacity to control variables directly is highest in experiments that researchers conduct under lab conditions. In observational studies, researchers can’t directly control the variables. Instead, they record the values of control variables and then use statistical procedures to account for them.

In science, researchers assess the effects that the independent variables have on the dependent variable. However, other variables can also affect the outcome. If the scientists do not control these other variables, they can distort the primary results of interest. In other words, left uncontrolled, those other factors become confounders that can bias the findings. The uncontrolled variables may be responsible for the changes in the outcomes rather than your treatment or experimental variables. Consequently, researchers control the values of these other variables.

Suppose you are performing an experiment involving different types of fertilizers and plant growth. Those are your primary variables of interest. However, you also know that soil moisture, sunlight, and temperature affect plant growth. If you don’t hold these variables constant for all observations, they might explain the plant growth differences you observe. Consequently, moisture, sunlight, and temperature are essential control variables for your study.

If you perform the study in a controlled lab setting, you can control these environmental conditions and keep their values constant for all observations in your experiment. That way, if you do see plant growth differences, you can be more confident that the fertilizers caused them.

When researchers use control variables, they should identify them, record their values, and include the details in their write-up. This process helps other researchers understand and replicate the results.

Related posts : Independent and Dependent Variables and Confounding Variables

Control Variables and Internal Validity

By controlling variables, you increase the internal validity of your research. Internal validity is the degree of confidence that a causal relationship exists between the treatment and the difference in outcomes. In other words, how likely is it that your treatment caused the differences you observe? Are the researcher’s conclusions correct? Or, can changes in the outcome be attributed to other causes?

If the relevant variables are not controlled, you might need to attribute the changes to confounders rather than the treatment. Control variables reduce the impact of confounding variables.

Controlled Variable Examples

How to Control Variables in Science

Scientists can control variables using several methods. In some cases, variables can be controlled directly. For example, researchers can control the growing conditions for the fertilizer experiment. Or use standardized procedures and processes for all subjects to reduce other sources of variation. These efforts attempt to eliminate all differences between the treatment and control groups other than the treatments themselves.

However, sometimes that’s not possible. Fortunately, there are other approaches.

Random assignment

In some experiments, there can be too many variables to control. Additionally, the researchers might not even know all the potential confounding variables. In these cases, they can randomly assign subjects to the experimental groups. This process controls variables by averaging out all traits across the experimental groups, making them roughly equivalent when the experiment begins. The randomness helps prevent any systematic differences between the experimental groups. Learn more in my post about Random Assignment in Experiments .

Statistical control

Directly controlled variables and random assignment are methods that equalize the experimental groups. However, they aren’t always feasible. In some cases, there are too many variables to control. In other situations, random assignment might not be possible. Try randomly assigning people to smoking and non-smoking groups!

Fortunately, statistical techniques, such as multiple regression analysis , don’t balance the groups but instead use a model that statistically controls the variables. The model accounts for confounding variables.

In multiple regression analysis, including a variable in the model holds it constant while the treatment variable fluctuates. This process allows you to isolate the role of the treatment while accounting for confounders. You can also use ANOVA and ANCOVA.

For more information, read my posts, When to Use Regression and ANOVA Overview .

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Reader Interactions

July 13, 2024 at 2:19 am

Sir you are doing a good job. much appreciated. Could you please tell us how to read the values of control variables like ranges and what do they mean. For instance how to read this (F=1.83; p= 0.07). Thank YOU

February 28, 2024 at 2:09 pm

In your explanation of control variables you use the example of a research study of plant fertilizers and their growth, wanting to control for moisture, sunshine and temperature. You state “Consequently, moisture, sunlight, and temperature are essential control variables for your study. These variables can be controlled by keeping their values constant for all observations in your experiment. You do not go further as to how you control for these values, particularly when such variables are continually changing. Al Wassler

February 28, 2024 at 2:13 pm

Presumably, this experiment would occur in a lab setting where you can control these variables. Plants would be raised with the same humidity, soil moisture, and light conditions.

I’ll add some text to the article to clarify that. Thanks!

January 26, 2023 at 7:00 pm

I have a question please about when a control variable is also itself part of the dependent variable. I see this referred to in the medical research literature as ‘mathematical coupling’, where – for example – the beats per minute (BPM) is the dependent variable and researchers want to put minutes also as a control variable. This seems to create a problem because ‘minutes’ appears on both sides of the equation, and the medical literature talks about spurious correlation, and the model needing to be redesigned. But do you have a simple text or reference – ideally just plain statistics/OLS rather than linked to medical research – where this could be explained in theory terms ? What goes wrong in the regression when a variable is both a control variable and part of the dependent variable (perhaps as part of a ratio or measurement of change)? I just haven’t found a textbook reference that says definitively ‘you can’t have the same variable in both sides of the regression simultaneously’, so I’m not sure whether this violates OLS and so is something to avoid entirely (with a new model design or different research question) or to live with.

Any help would be great, thank you for your work,

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Research Variables 101

Independent variables, dependent variables, control variables and more

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | January 2023

If you’re new to the world of research, especially scientific research, you’re bound to run into the concept of variables , sooner or later. If you’re feeling a little confused, don’t worry – you’re not the only one! Independent variables, dependent variables, confounding variables – it’s a lot of jargon. In this post, we’ll unpack the terminology surrounding research variables using straightforward language and loads of examples .

Overview: Variables In Research

What (exactly) is a variable?

The simplest way to understand a variable is as any characteristic or attribute that can experience change or vary over time or context – hence the name “variable”. For example, the dosage of a particular medicine could be classified as a variable, as the amount can vary (i.e., a higher dose or a lower dose). Similarly, gender, age or ethnicity could be considered demographic variables, because each person varies in these respects.

Within research, especially scientific research, variables form the foundation of studies, as researchers are often interested in how one variable impacts another, and the relationships between different variables. For example:

• How someone’s age impacts their sleep quality
• How different teaching methods impact learning outcomes
• How diet impacts weight (gain or loss)

As you can see, variables are often used to explain relationships between different elements and phenomena. In scientific studies, especially experimental studies, the objective is often to understand the causal relationships between variables. In other words, the role of cause and effect between variables. This is achieved by manipulating certain variables while controlling others – and then observing the outcome. But, we’ll get into that a little later…

The “Big 3” Variables

Variables can be a little intimidating for new researchers because there are a wide variety of variables, and oftentimes, there are multiple labels for the same thing. To lay a firm foundation, we’ll first look at the three main types of variables, namely:

• Independent variables (IV)
• Dependant variables (DV)
• Control variables

What is an independent variable?

Simply put, the independent variable is the “ cause ” in the relationship between two (or more) variables. In other words, when the independent variable changes, it has an impact on another variable.

For example:

• Increasing the dosage of a medication (Variable A) could result in better (or worse) health outcomes for a patient (Variable B)
• Changing a teaching method (Variable A) could impact the test scores that students earn in a standardised test (Variable B)
• Varying one’s diet (Variable A) could result in weight loss or gain (Variable B).

It’s useful to know that independent variables can go by a few different names, including, explanatory variables (because they explain an event or outcome) and predictor variables (because they predict the value of another variable). Terminology aside though, the most important takeaway is that independent variables are assumed to be the “cause” in any cause-effect relationship. As you can imagine, these types of variables are of major interest to researchers, as many studies seek to understand the causal factors behind a phenomenon.

Need a helping hand?

What is a dependent variable?

While the independent variable is the “ cause ”, the dependent variable is the “ effect ” – or rather, the affected variable . In other words, the dependent variable is the variable that is assumed to change as a result of a change in the independent variable.

Keeping with the previous example, let’s look at some dependent variables in action:

• Health outcomes (DV) could be impacted by dosage changes of a medication (IV)
• Students’ scores (DV) could be impacted by teaching methods (IV)
• Weight gain or loss (DV) could be impacted by diet (IV)

In scientific studies, researchers will typically pay very close attention to the dependent variable (or variables), carefully measuring any changes in response to hypothesised independent variables. This can be tricky in practice, as it’s not always easy to reliably measure specific phenomena or outcomes – or to be certain that the actual cause of the change is in fact the independent variable.

As the adage goes, correlation is not causation . In other words, just because two variables have a relationship doesn’t mean that it’s a causal relationship – they may just happen to vary together. For example, you could find a correlation between the number of people who own a certain brand of car and the number of people who have a certain type of job. Just because the number of people who own that brand of car and the number of people who have that type of job is correlated, it doesn’t mean that owning that brand of car causes someone to have that type of job or vice versa. The correlation could, for example, be caused by another factor such as income level or age group, which would affect both car ownership and job type.

To confidently establish a causal relationship between an independent variable and a dependent variable (i.e., X causes Y), you’ll typically need an experimental design , where you have complete control over the environmen t and the variables of interest. But even so, this doesn’t always translate into the “real world”. Simply put, what happens in the lab sometimes stays in the lab!

As an alternative to pure experimental research, correlational or “ quasi-experimental ” research (where the researcher cannot manipulate or change variables) can be done on a much larger scale more easily, allowing one to understand specific relationships in the real world. These types of studies also assume some causality between independent and dependent variables, but it’s not always clear. So, if you go this route, you need to be cautious in terms of how you describe the impact and causality between variables and be sure to acknowledge any limitations in your own research.

What is a control variable?

In an experimental design, a control variable (or controlled variable) is a variable that is intentionally held constant to ensure it doesn’t have an influence on any other variables. As a result, this variable remains unchanged throughout the course of the study. In other words, it’s a variable that’s not allowed to vary – tough life 🙂

As we mentioned earlier, one of the major challenges in identifying and measuring causal relationships is that it’s difficult to isolate the impact of variables other than the independent variable. Simply put, there’s always a risk that there are factors beyond the ones you’re specifically looking at that might be impacting the results of your study. So, to minimise the risk of this, researchers will attempt (as best possible) to hold other variables constant . These factors are then considered control variables.

Some examples of variables that you may need to control include:

• Temperature
• Time of day
• Noise or distractions

Which specific variables need to be controlled for will vary tremendously depending on the research project at hand, so there’s no generic list of control variables to consult. As a researcher, you’ll need to think carefully about all the factors that could vary within your research context and then consider how you’ll go about controlling them. A good starting point is to look at previous studies similar to yours and pay close attention to which variables they controlled for.

Of course, you won’t always be able to control every possible variable, and so, in many cases, you’ll just have to acknowledge their potential impact and account for them in the conclusions you draw. Every study has its limitations , so don’t get fixated or discouraged by troublesome variables. Nevertheless, always think carefully about the factors beyond what you’re focusing on – don’t make assumptions!

Other types of variables

As we mentioned, independent, dependent and control variables are the most common variables you’ll come across in your research, but they’re certainly not the only ones you need to be aware of. Next, we’ll look at a few “secondary” variables that you need to keep in mind as you design your research.

• Moderating variables
• Mediating variables
• Confounding variables
• Latent variables

Let’s jump into it…

What is a moderating variable?

A moderating variable is a variable that influences the strength or direction of the relationship between an independent variable and a dependent variable. In other words, moderating variables affect how much (or how little) the IV affects the DV, or whether the IV has a positive or negative relationship with the DV (i.e., moves in the same or opposite direction).

For example, in a study about the effects of sleep deprivation on academic performance, gender could be used as a moderating variable to see if there are any differences in how men and women respond to a lack of sleep. In such a case, one may find that gender has an influence on how much students’ scores suffer when they’re deprived of sleep.

It’s important to note that while moderators can have an influence on outcomes , they don’t necessarily cause them ; rather they modify or “moderate” existing relationships between other variables. This means that it’s possible for two different groups with similar characteristics, but different levels of moderation, to experience very different results from the same experiment or study design.

What is a mediating variable?

Mediating variables are often used to explain the relationship between the independent and dependent variable (s). For example, if you were researching the effects of age on job satisfaction, then education level could be considered a mediating variable, as it may explain why older people have higher job satisfaction than younger people – they may have more experience or better qualifications, which lead to greater job satisfaction.

Mediating variables also help researchers understand how different factors interact with each other to influence outcomes. For instance, if you wanted to study the effect of stress on academic performance, then coping strategies might act as a mediating factor by influencing both stress levels and academic performance simultaneously. For example, students who use effective coping strategies might be less stressed but also perform better academically due to their improved mental state.

In addition, mediating variables can provide insight into causal relationships between two variables by helping researchers determine whether changes in one factor directly cause changes in another – or whether there is an indirect relationship between them mediated by some third factor(s). For instance, if you wanted to investigate the impact of parental involvement on student achievement, you would need to consider family dynamics as a potential mediator, since it could influence both parental involvement and student achievement simultaneously.

What is a confounding variable?

A confounding variable (also known as a third variable or lurking variable ) is an extraneous factor that can influence the relationship between two variables being studied. Specifically, for a variable to be considered a confounding variable, it needs to meet two criteria:

• It must be correlated with the independent variable (this can be causal or not)
• It must have a causal impact on the dependent variable (i.e., influence the DV)

Some common examples of confounding variables include demographic factors such as gender, ethnicity, socioeconomic status, age, education level, and health status. In addition to these, there are also environmental factors to consider. For example, air pollution could confound the impact of the variables of interest in a study investigating health outcomes.

Naturally, it’s important to identify as many confounding variables as possible when conducting your research, as they can heavily distort the results and lead you to draw incorrect conclusions . So, always think carefully about what factors may have a confounding effect on your variables of interest and try to manage these as best you can.

What is a latent variable?

Latent variables are unobservable factors that can influence the behaviour of individuals and explain certain outcomes within a study. They’re also known as hidden or underlying variables , and what makes them rather tricky is that they can’t be directly observed or measured . Instead, latent variables must be inferred from other observable data points such as responses to surveys or experiments.

For example, in a study of mental health, the variable “resilience” could be considered a latent variable. It can’t be directly measured , but it can be inferred from measures of mental health symptoms, stress, and coping mechanisms. The same applies to a lot of concepts we encounter every day – for example:

• Emotional intelligence
• Quality of life
• Business confidence
• Ease of use

One way in which we overcome the challenge of measuring the immeasurable is latent variable models (LVMs). An LVM is a type of statistical model that describes a relationship between observed variables and one or more unobserved (latent) variables. These models allow researchers to uncover patterns in their data which may not have been visible before, thanks to their complexity and interrelatedness with other variables. Those patterns can then inform hypotheses about cause-and-effect relationships among those same variables which were previously unknown prior to running the LVM. Powerful stuff, we say!

Let’s recap

In the world of scientific research, there’s no shortage of variable types, some of which have multiple names and some of which overlap with each other. In this post, we’ve covered some of the popular ones, but remember that this is not an exhaustive list .

To recap, we’ve explored:

• Independent variables (the “cause”)
• Dependent variables (the “effect”)
• Control variables (the variable that’s not allowed to vary)

If you’re still feeling a bit lost and need a helping hand with your research project, check out our 1-on-1 coaching service , where we guide you through each step of the research journey. Also, be sure to check out our free dissertation writing course and our collection of free, fully-editable chapter templates .

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A Practical Guide to Choosing the Right Control Variables for Your Research

Research is a dynamic process, where scientists strive to unravel the mysteries of the world through systematic inquiry. In this pursuit, control variables play a crucial role in shaping the reliability and validity of research findings. This blog serves as a practical guide to aid researchers in the thoughtful selection of control variables.

Table of Contents

“Control variables, often referred to as covariates, are elements in a study that are intentionally held constant or systematically manipulated to examine their impact on the relationship between independent and dependent variables. These variables act as safeguards against confounding factors, ensuring that the observed effects can be attributed more accurately to the independent variable under investigation.

Importance of Selecting the Right Control Variables

The choice of control variables is not arbitrary; it demands careful consideration and a deep understanding of the research context. The significance of selecting the right control variables cannot be overstated, as these elements serve as the bedrock for establishing the internal validity of a study.

Internal validity refers to the accuracy of causal inferences within an experiment – the extent to which changes in the dependent variable can be confidently attributed to manipulating the independent variable.

By meticulously selecting control variables, researchers can minimize the risk of alternative explanations, ensuring that observed effects are more likely to reflect true causal relationships.

How Control Variables Enhance Research Validity

Research validity is a multifaceted concept that encompasses various dimensions, including internal, external, construct, and statistical validity. Control variables primarily enhance internal validity by minimizing the influence of extraneous variables that could introduce bias or confound the results.

Researchers create a more controlled and precise experimental environment by strategically incorporating control variables. This, in turn, allows for a clearer understanding of the relationship between the independent and dependent variables, bolstering the overall validity of the research findings.

In essence, control variables act as gatekeepers, fortifying the integrity of the research process and paving the way for more robust and trustworthy scientific conclusions.

Understanding Control Variables

Control variables, also known as covariates, are integral components of experimental design and statistical analysis in research. Their primary purpose is to add precision to investigations by accounting for potential confounding factors that might otherwise distort the interpretation of results. 

For instance, imagine a study examining the impact of a new drug on patients’ recovery time after surgery. The type of anesthesia used, the patient’s age, and pre-existing health conditions are all factors that could influence the recovery time. 

By identifying and controlling for these variables, researchers can more confidently attribute any observed changes in recovery time to the specific effects of the drug being studied.

How Control Variables Differ from Independent and Dependent Variables

To grasp the role of control variables, it is essential to differentiate them from independent and dependent variables. The researcher manipulates or selects independent variables to observe their effect on the dependent variable.

On the other hand, dependent variables are the outcomes or responses measured in the experiment, dependent on the changes in the independent variable.

Control variables, however, are not the variables of primary interest. Instead, they are chosen to minimize the influence of extraneous variables that might interfere with the relationship between the independent and dependent variables. While independent and dependent variables are central to the research question, control variables act as safeguards to ensure the integrity and validity of the study.

Examples of Control Variables

Control variables are versatile and their selection depends on the specifics of each study. 

In social science research, control variables may include demographic factors like age, gender, and socioeconomic status.

In experimental studies in the physical sciences, factors such as temperature, humidity, or pressure might be controlled to isolate the effects of the manipulated variables.

Consider a psychological study exploring the impact of a new therapy on reducing anxiety levels. Control variables in this scenario could include the participants’ previous experiences with therapy, baseline anxiety levels, or even the time of day the therapy sessions are conducted.

These variables, when controlled, allow the researcher to attribute any observed changes in anxiety levels more confidently to the therapeutic intervention.

Criteria for Selecting Control Variables

The following are the criteria for selecting the right control variables.

Relevance to the Research Question

One of the foremost considerations when selecting control variables is their relevance to the research question or thesis statement . The chosen control variables should have a logical and theoretical connection to the study, aligning with the overarching objectives. 

Researchers must carefully evaluate whether the control variables are likely to influence the relationship between the independent and dependent variables. A judicious selection based on relevance ensures that the controlled factors contribute meaningfully to the study’s internal validity.

Potential Confounding Factors

Control variables act as a shield against confounding factors—variables that might distort the observed relationship between the independent and dependent variables. Identifying potential confounding factors requires an understanding of the subject and a thorough literature review. 

Researchers must anticipate variables that could muddy the waters and strategically incorporate them as control variables to isolate the effects of the independent variable accurately.

Feasibility and Practicality

While researchers aim for inclusivity in control variable selection, practical considerations cannot be ignored. Feasibility and practicality play a pivotal role in the decision-making process. 

Researchers must assess whether the chosen control variables are measurable, obtainable, and manageable within the constraints of the study. Pragmatic decisions ensure that the research remains feasible without compromising the overall quality and validity.

Balance Between Inclusivity and Specificity

Achieving a delicate balance between inclusivity and specificity is crucial in control variable selection. Including too few control variables may leave the study vulnerable to lurking confounders, while an overly exhaustive list may complicate the analysis and risk diluting the primary focus. 

Researchers must strike a balance, aiming for inclusivity without sacrificing the specificity necessary to draw meaningful and precise conclusions from the data.

Common Pitfalls in Control Variable Selection

Here are some common pitfalls in control variable selection.

Overlooking Relevant Variables

One common pitfall in control variable selection is overlooking variables that could significantly impact the study’s outcomes. Researchers may inadvertently omit relevant factors that, when unaccounted for, introduce bias or confound the results. 

Rigorous literature reviews and a comprehensive understanding of the research domain are crucial in avoiding this oversight.

Including Unnecessary Variables

Conversely, the inclusion of unnecessary variables poses another challenge. Researchers may be tempted to incorporate a multitude of control variables without clear theoretical or empirical justification. 

This not only complicates the study unnecessarily but can also lead to overfitting models, reducing the generalizability of findings. Prudent selection is key to avoiding this pitfall.

Confusing Control Variables with Mediators or Moderators

Control variables should not be confused with mediators or moderators . Mediators explain how an independent variable affects a dependent variable, while moderators influence the strength or direction of the relationship between the independent and dependent variables. 

Confusing these concepts can lead to misinterpretation of results and compromise the overall integrity of the study. Researchers must delineate between control variables, mediators, and moderators to ensure accurate analyses.

Strategies for Identifying Control Variables

You can identify control variables with the help of the following strategies.

Literature Review and Prior Research

A robust literature review is a cornerstone for identifying relevant control variables. Existing research provides valuable insights into potential factors that could confound or influence the relationships under investigation. 

By examining similar studies and drawing on the collective knowledge within the field, researchers can identify common control variables used by peers and gain a better understanding of the variables that warrant consideration in their own work.

Preliminary Data Analysis

Conducting preliminary data analysis can unearth patterns and relationships that may guide the selection of control variables. Exploratory data analysis allows researchers to identify potential confounding factors by examining correlations, patterns, and outliers.

By scrutinizing the data before formal analysis, researchers can make informed decisions about which variables to control for, refine their study design, and ensure a more robust research paper approach.

Expert Consultation and Peer Feedback

Seeking input from experts in the field and obtaining peer feedback can provide valuable perspectives on control variable selection. Collaborating with colleagues who have expertise in the subject or statistical methods can offer fresh insights and help researchers consider variables they might have overlooked. 

Peer review processes also serve as a checkpoint, allowing external experts to assess the validity and appropriateness of chosen control variables.

Documentation and Transparency

Thorough documentation of control variable choices is essential for the transparency and replicability of research. Researchers should meticulously record the rationale behind each control variable selection, detailing the theoretical or empirical basis for inclusion. 

This documentation serves as a critical reference point for both internal and external stakeholders, aiding in the understanding and evaluation of the study’s design and validity.

Case Studies

Here are some case studies to help you better understand control variables.

Examining real-world examples of well-selected control variables can provide valuable insights into effective research practices. In a study investigating the impact of a nutritional intervention on weight loss, well-chosen control variables might include participants’ baseline body mass index (BMI), exercise habits, and pre-existing medical conditions.

 These control variables help ensure that observed changes in weight can be confidently attributed to the nutritional intervention, minimizing the influence of extraneous factors.

In another example, a social science study exploring the effects of a community development program may appropriately control for demographic factors such as income, education level, and employment status. By doing so, the researchers can isolate the specific impact of the intervention on community outcomes without the interference of socioeconomic disparities.

Analysis of Studies with Inadequate Control Variable Selection

Conversely, inadequate control variable selection can compromise the validity of study findings. For instance, a study examining the effectiveness of a new teaching method in improving student performance may fall short if it fails to control for factors like students’ prior academic achievement, socio-economic background, or teacher-student ratios.

In such cases, the observed improvements in student performance may be confounded by these uncontrolled variables, making it challenging to attribute the effects solely to the teaching method.

Similarly, a health-related study investigating the impact of a wellness program may encounter issues if it neglects to control for participants’ pre-existing health conditions or lifestyle factors. Without proper controls, the study risks drawing inaccurate conclusions about the program’s effectiveness.

Lessons Learned from Real-World Examples

Analyzing case studies with both effective and inadequate control variable selection provides valuable lessons for researchers. It underscores the importance of understanding the research context and the critical role that control variables play in ensuring the internal validity of a study. 

Researchers can learn to anticipate potential confounding factors, appreciate the complexity of real-world scenarios, and recognize the significance of meticulous control variable selection in generating trustworthy research outcomes.

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Practical Tips for Implementing Control Variables

With the help of these tips, you can implement control variables. 

Tip 1: Monitoring and Adjusting Control Variables During the Research Process

The research process is dynamic, and unforeseen variables may emerge. Researchers should adopt a proactive approach to monitor and adjust control variables as necessary throughout the study. 

Regularly assessing the relevance and impact of control variables allows researchers to adapt to changing circumstances, ensuring that the study remains robust and that unexpected confounding factors are addressed promptly.

Tip 2: Using Statistical Techniques to Assess the Impact of Control Variables

Statistical techniques can aid researchers in assessing the impact of control variables on study outcomes. Regression analysis, for example, allows researchers to examine how changes in the independent variable relate to changes in the dependent variable while holding control variables constant. 

This analysis helps quantify the contribution of each variable and ensures that control variables are appropriately considered in the interpretation of results.

Tip 3: Considerations for Longitudinal or Experimental Studies

Longitudinal or experimental studies present unique challenges in control variable selection. In longitudinal studies, where data is collected over an extended period, researchers must carefully choose control variables that account for changes over time. 

In experimental studies, the manipulation of variables introduces complexities that require strategic control variable selection. Researchers should be attuned to their study design, ensuring that control variables are relevant and measurable, and effectively mitigate potential confounding factors specific to their experimental or longitudinal context.

Frequently Asked Questions

What are the examples of variable control.

Examples of variable control include maintaining consistent temperature in a scientific experiment, controlling for participants’ age and gender in social research, or standardizing testing conditions to isolate the impact of an independent variable on a dependent variable.

What are 3 controlled variables?

• Temperature: Ensuring a constant temperature in an experiment to isolate the effects of other variables.
• Time: Controlling the duration of an experiment to prevent time-related influences on the dependent variable.
• Light: Standardizing light conditions to eliminate its impact on experimental outcomes.

What are system control variables?

System control variables are parameters or factors intentionally regulated or kept constant in a system to observe the impact of independent variables. By controlling these elements, researchers can isolate and assess the effects of specific variables on the system’s behaviour or outcomes.

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Control Variables: Problematic Issues and Best Practices

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Schjoedt and Sangboon hold a positivist ideology. In this chapter they discuss an important aspect of the unit of analysis strategy in research designs: How does one account for or control factors that the researcher is aware of in the model but are beyond the focus of a within-groups or between-groups comparison? In other words, control factors are confounding, moderating, or mediating variables. The reason it is important to identify and control (or account for) these factors is so that the researcher can generalize to other populations, that is, by identifying the confounding factors that are present but are beyond the unit of analysis interest. When participants are samples for a between-group unit of analysis comparison, individual attributes in each participant often differ. Designing control variables is one approach among others to address this.

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Experimental Design - Independent, Dependent, and Controlled Variables

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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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What Is the Difference Between a Control Variable and Control Group?

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In experiments, controls are factors that you hold constant or don't expose to the condition you are testing. By creating a control, you make it possible to determine whether the variables alone are responsible for an outcome. Although control variables and the control group serve the same purpose, the terms refer to two different types of controls which are used for different kinds of experiments.

Why Experimental Controls Are Necessary

A student places a seedling in a dark closet, and the seedling dies. The student now knows what happened to the seedling, but he doesn't know why. Perhaps the seedling died from lack of light, but it might also have died because it was already sickly, or because of a chemical kept in the closet, or for any number of other reasons. 

In order to determine why the seedling died, it is necessary to compare that seedling's outcomes to another identical seedling outside the closet. If the closeted seedling died while the seedling kept in sunshine stayed alive, it's reasonable to hypothesize that darkness killed the closeted seedling. 

Even if the closeted seedling died while the seedling placed in sunshine lived, the student would still have unresolved questions about her experiment. Might there be something about the particular seedlings that caused the results she saw? For example, might one seedling have been healthier than the other to start with?

To answer all of her questions, the student might choose to put several identical seedlings in a closet and several in the sunshine. If at the end of a week, all of the closeted seedlings are dead while all of the seedlings kept in​ the sunshine are alive, it is reasonable to conclude that the darkness killed the seedlings.

Definition of a Control Variable

A control variable is any factor you control or hold constant during an experiment. A control variable is also called a controlled variable or constant variable. 

If you are studying the effect of the amount of water on seed germination, control variables might include temperature, light, and type of seed. In contrast, there may be variables you can't easily control, such as humidity, noise, vibration, and magnetic fields.

Ideally, a researcher wants to control every variable, but this isn't always possible. It's a good idea to note all recognizable variables in a lab notebook for reference.

Definition of a Control Group

A control group is a set of experimental samples or subjects that are kept separate and aren't exposed to the independent variable .

In an experiment to determine whether zinc helps people recover faster from a cold, the experimental group would be people taking zinc, while the control group would be people taking a placebo (not exposed to extra zinc, the independent variable).

A controlled experiment is one in which every parameter is held constant except for the experimental (independent) variable. Usually, controlled experiments have control groups. Sometimes a controlled experiment compares a variable against a standard.

• Scientific Method Vocabulary Terms
• The Role of a Controlled Variable in an Experiment
• Understanding Simple vs Controlled Experiments
• What Are the Elements of a Good Hypothesis?
• What Is a Controlled Experiment?
• Six Steps of the Scientific Method
• DRY MIX Experiment Variables Acronym
• What Is an Experimental Constant?
• Scientific Method Flow Chart
• Scientific Variable
• What Are Examples of a Hypothesis?
• What Is a Testable Hypothesis?
• Null Hypothesis Examples
• What Is a Hypothesis? (Science)
• Scientific Hypothesis Examples
• Random Error vs. Systematic Error

Chapter 3 -- Introducing a Control Variable (Multivariate Analysis)

Human behavior is usually too complicated to be studied with only two variables. Often we will want to consider sets of three or more variables (called multivariate analysis ). We will want to consider three or more variables when we have discovered a relationship between two variables and want to find out 1) if this relationship might be due to some other factor, 2) how or why these variables are related, or 3) if the relationship is the same for different types of individuals. In each situation, we identify a third variable that we want to consider. This is called the control or the test variable . (Although it is possible to use several control variables simultaneously, we will limit ourselves to one control variable at a time.) To introduce a third variable, we identify the control variable and separate the cases in our sample by the categories of the control variable. For example, if the control variable is age divided into these two categories--younger and older, we would separate the cases into two groups. One group would consist of individuals who are younger and the other group would be those who are older. We would then obtain the crosstabulation of the independent and dependent variables for each of these age groups. Since there are two categories in this control variable, we obtain two partial tables , each containing part of the original sample. (If there were three categories in our control variable, for example, young, middle aged, and old, we would have three partial tables.) The process of using a control variable in the analysis is called elaboration and was developed at Columbia University by Paul Lazarsfeld and his associates. There are several different types of outcomes to the elaboration process. We will discuss each briefly. Table 2.3 showed that females were more likely than males to say they were willing to vote for a woman. Let's introduce a control variable and see what happens. In this example we are going to use age as the control variable. Table 3.1 is the three-variable table with voting preference as the dependent variable, sex as the independent variable, and age as the control variable. When we look at the older respondents (the left-hand partial table), we discover that this partial table is very similar to the original two-variable table (Table 2.3). The same is true for the younger respondents (the right-hand partial table). Each partial table is very similar to the original two-variable table. This is often referred to as replication because the partial tables repeat the original two-variable table (see Babbie 1997: 393-396). It is not necessary that they be identical; just that each partial table be basically the same as the original two-variable table. Our conclusion is that age is not affecting the relationship between sex and voting preference. In other words, the difference between males and females in voting preference is not due to age. Table 3.1 -- Voting Preference by Sex Controlling for Age   Older Younger   Male  %  Female  %  Total  %  Male  %  Female  %  Total  %  Voting Preference             Willing to Vote for a Woman 43.8  56.1  49.0  44.2  55.8  52.9  Not Willing to Vote for a Woman 56.2  43.9  51.0  55.8  44.2    100.0  100.0  100.0  100.0  100.0  100.0    (240)  (180)  (420)  (120)  (360)  (480)  Since this is a hypothetical example, imagine a different outcome. Suppose we introduce age as a control variable and instead of getting Table 2.1, we get Table 3.2. How do these two tables differ? In Table 3.2, the percentage difference between males and females has disappeared in both of the partial tables. This is called explanation because the control variable, age, has explained away the original relationship between sex and voting preference. (We often say that the relationship between the two variables is spurious , not genuine.) When age is held constant, the difference between males and females disappears. The difference in the relationship does not have to disappear entirely, only be reduced substantially in each of the partial tables. This can only occur when there is a relationship between the control variable (age) and each of the other two variables (sex and voting preference). Next, we are interested in how or why the two variables are related. Suppose females are more likely than males to vote for a woman and that this difference cannot be explained away by age or by any other variable we have considered. We need to think about why there might be such a difference in the preferences of males and females. Perhaps females are more often liberal Table 3.2 -- Voting Preference by Sex Controlling for Age   Older Younger   Male %  Female %  Total %  Male %  Female %  Total %  Voting Preference             Willing to Vote for a Woman 32.9  33.9  33.3  65.8  66.9  66.7  Not Willing to Vote for a Woman 67.1  66.1  66.7  34.2  33.1  33.3    100.0 100.0  100.0  100.0  100.0  100.0    (240)  (180)  (420)  (120)  (360)  (480)  than males, and liberals are more likely to say they would vote for a woman. So we introduce liberalism/conservatism as a control variable in our analysis. If females are more likely to support a woman because they are more liberal, then the difference between the preferences of men and women should disappear or be substantially reduced when liberalism/conservatism is held constant. This process is called interpretation because we are interpreting how one variable is related to another variable. Table 3.3 shows what we would expect to find if females supported the woman because they were more liberal. Notice that in both partial tables, the differences in the percentages between men and women has disappeared. (It is not necessary that it disappears entirely, but only that it is substantially reduced in each of the partial tables.) Table 3.3 -- Voting Preference by Sex Controlling for Liberalism/Conservatism   Older Younger   Male %  Female %  Total %  Male %  Female %  Total %  Voting Preference             Willing to Vote for a Woman 32.9  33.9  33.3  65.8  66.9  66.7  Not Willing to Vote for a Woman 67.1  66.1  66.7  34.2  33.1  33.3    100.0  100.0  100.0  100.0  100.0  100.0    (240)  (180)  (420)  (120)  (360)  (480)  Finally, let's focus on the third of the situations outlined at the beginning of this section--whether the relationship is the same for different types of individuals. Perhaps the relationship between sex and voter preference varies with other characteristics of the individuals. Maybe among whites, females are more likely to prefer women candidates than the males are, but among blacks, there is little difference between males and females in terms of voter preference. This is the outcome shown in Table 3.4. This process is called specification because it specifies the conditions under which the relationship between sex and voter preference varies. In the earlier section on bivariate analysis, we discussed the use of chi square. Remember that chi square is a test of independence used to determine if there is a relationship between two variables. Chi square is used in multivariate analysis the same way it is in bivariate analysis. There will be a separate value of chi square for each partial table in the multivariate analysis. You should keep a number of warnings in mind. Chi square assumes that the expected frequencies for each cell are five or larger. As long as 80% of these expected frequencies are five or larger and no single expected frequency is very small, we don't have to worry. However, the expected frequencies often drop below five when the number of cases in a column or row gets too small. If this should occur, you will have to either recode (i.e., combine columns or rows) or eliminate a column or row from the table. Table 3.4 -- Voting Preference by Sex Controlling for Race   White African American   Male %  Female %  Total %  Male %  Female %  Total %  Voting Preference             Willing to Vote for a Woman 42.9  56.5  51.2  50.0  50.0  50.0  Not Willing to vote for a Woman 57.1  43.5  48.8  50.0  50.0  50.0    100.00  100.00  100.00  100.00  100.00  100.00    (310)  (490)  (800)  (50)  (50) (100)  Another point to keep in mind is that chi square is affected by the number of cases in the table. With a lot of cases it is easy to reject the null hypothesis of no relationship. With a few cases, it can be quite hard to reject the null hypothesis. Also, consider the percentages within the table. Look for patterns. Do not rely on any single piece of information. Look at the whole picture. We have concentrated on crosstabulation and chi square. There are other types of statistical analysis such as regression and log-linear analysis. When you have mastered these techniques, look at some other types of analysis. REFERENCES AND SUGGESTED READING Methods of Social Research Riley, Matilda White. 1963. Sociological Research I: A Case Approach . New York: Harcourt, Brace and World. Survey Research and Sampling Babbie, Earl R. 1990. Survey Research Methods (2 nd Ed.). Belmont, CA: Wadsworth. Babbie, Earl R. 1997. The Practice of Social Research (8 th Ed.). Belmont, CA: Wadsworth.   Statistical Analysis K noke, David, and George W. Bohrnstedt. 1991. Basic Social Statistics . Itesche, IL: Peacock. Riley, Matilda White. 1963. Sociological Research II Exercises and Manual . New York: Harcourt, Brace & World. Norusis, Marija J. 1997. SPSS 7.5 Guide to Data Analysis . Upper Saddle River, New Jersey: Prentice Hall. Elaboration and Causal Analysis Hirschi, Travis and Hanan C. Selvin. 1967. Delinquency Research--An Appraisal of Analytic Methods . New York: Free Press. Rosenberg, Morris. 1968. The Logic of Survey Analysis . New York: Basic Books. Data Sources The Field Institute. 1985. California Field Poll Study, July, 1985 . Machine-readable codebook. The Field Institute. 1991. California Field Poll Study, September, 1991 . Machine-readable codebook. The Field Institute. 1995. California Field Poll Study, February, 1995. Machine-readable codebook.

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Controlled Variables

Controlled variables are variables that is sometimes overlooked by researchers, but it is usually far more important than the dependent or independent variables.

This article is a part of the guide:

• Experimental Research
• Pretest-Posttest
• Third Variable
• Research Bias
• Independent Variable

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• 1 Experimental Research
• 2.1 Independent Variable
• 2.2 Dependent Variable
• 2.3 Controlled Variables
• 2.4 Third Variable
• 3.1 Control Group
• 3.2 Research Bias
• 3.3.1 Placebo Effect
• 3.3.2 Double Blind Method
• 4.1 Randomized Controlled Trials
• 4.2 Pretest-Posttest
• 4.3 Solomon Four Group
• 4.4 Between Subjects
• 4.5 Within Subject
• 4.6 Repeated Measures
• 4.7 Counterbalanced Measures
• 4.8 Matched Subjects

A failure to isolate the controlled variables, in any experimental design , will seriously compromise the internal validity . This oversight may lead to confounding variables ruining the experiment , wasting time and resources, and damaging the researcher's reputation.

In any experimental design, a researcher will be manipulating one variable , the independent variable , and studying how that affects the dependent variables .

Most experimental designs measures only one or two variables at a time. Any other factor, which could potentially influence the results , must be correctly controlled. Its effect upon the results must be standardized, or eliminated, exerting the same influence upon the different sample groups .

For example, if you were comparing cleaning products, the brand of cleaning product would be the only independent variable measured. The level of dirt and soiling, the type of dirt or stain, the temperature of the water and the time of the cleaning cycle are just some of the variables that must be the same between experiments. Failure to standardize even one of these controlled variables could cause a confounding variable and invalidate the results.

Control Groups

In many fields of science, especially biology and behavioral sciences, it is very difficult to ensure complete control , as there is a lot of scope for small variations.

Biological processes are subject to natural fluctuations and chaotic rhythms. The key is to use established operationalization techniques, such as randomization and double blind experiments . These techniques will control and isolate these variables, as much as possible. If this proves difficult, a control group is used, which will give a baseline measurement for the unknown variables.

Sound statistical analysis will then eliminate these fluctuations from the results. Most statistical tests have a certain error margin built in, and repetition and large sample groups will eradicate the unknown variables.

There still needs to be constant monitoring and checks, but due diligence will ensure that the experiment is as accurate as is possible.

The Value of Consistency

It is important to ensure that all these possible variables are isolated, because a type III error may occur if an unknown factor influences the dependent variable . This is where the null hypothesis is correctly rejected, but for the wrong reason.

In addition, inadequate monitoring of controlled variables is one of the most common causes of researchers wrongly assuming that a correlation leads to causality .

Controlled variables are the road to failure in an experimental design , if not identified and eliminated. Designing the experiment with controls in mind is often more crucial than determining the independent variable .

Poor controls can lead to confounding variables , and will damage the internal validity of the experiment.

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Mastering Control Variables in Research: What You Need to Know

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A control variable maintains a consistent state throughout an experiment or research, aiding the examination of relationships between various variables. Its stability ensures fair comparisons in test results and prevents any distortion, enabling researchers to gain clearer insights into variable relationships.

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Introduction

In research, scientists use control variables with independent and dependent variables to analyze data. This practice aids researchers in isolating the effects of variables apart from the primary ones under study. Researchers have the ability to directly or indirectly manage these factors.

Direct control involves maintaining variables at a constant level throughout the research or experiment, such as stabilizing temperature conditions. Indirect control methods, on the other hand, utilize statistical techniques to manage variables.

What is a control variable?

Control variables, also called constant variables, are parts of an experiment that researchers keep the same on purpose. These factors are important in making sure that outside influences do not affect the research results too much. 

In research designs, researchers typically assess the impact of an independent variable on a dependent variable. To accurately measure the relationship, it’s important to control other variables. This is done by managing extraneous or standardized variables.

Control variables in experiments

In experiments, researchers and scientists manipulate the independent variable to understand how an independent variable influences a dependent variable. These variables are essential for maintaining the integrity of experimental outcomes, ensuring fairness, and minimizing biases induced by experimental manipulation. This practice enhances the internal validity of the study by reducing the impact of confounding variables.

Control variables in non-experimental research

In research, we sometimes cannot change the independent variable. We can use different variables to understand the connections between the main variables we are studying. 

These additional variables provide valuable insights into how the main variables interact with each other. By analyzing these relationships, we can gain a better understanding of the overall research findings. They enhance the analysis and provide deeper insight into the research dynamics.

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Why do control variables matter?

It is important to control factors that could affect the results of a research project. These factors include independent and dependent variables . Understanding this is crucial for the success of the project.

Failure to control these variables could obscure the true impact of the independent variable on the outcomes. It’s hard to know if the results are from the variable or other factors, so the findings may not be trustworthy.

Control over variables is essential because even minor fluctuations in the research conditions can significantly sway the results. Moreover, regulating variables improves the coherence of the study and aids in establishing a distinct relationship between the independent and dependent variables.

To illustrate, consider an investigation into whether soil quality affects plant growth. Here, soil quality serves as the independent variable, while the rate of plant growth represents the dependent variable. Without controlling for soil quality, the validity of the study’s findings could be compromised.

Examples of control variables

Examples of elements managed in scientific inquiries are crucial for ensuring the accuracy and reliability of research findings. Consider the following scenarios:

Investigating the Impact of Soil Quality on Plant Growth:

Controlled variables include:

• Temperature: Maintaining a consistent temperature to isolate the effect of soil quality on plant growth.
• Amount of light: Ensuring uniform light exposure across experimental conditions.
• Amount of water: Controlling water levels to eliminate its differences as a factor affecting plant growth.

Examining the Effect of Caffeine on Memory Recall:

Controlled variables encompass:

• Participant age: Keeping participant age constant to isolate the impact of caffeine on memory recall.
• Noise in the environment: Controlling environmental noise levels to prevent interference with memory recall tasks.
• Type of memory test: Using the same memory test for everyone to see how caffeine affects memory.

Exploring Perception of Water Images in Individuals with fear of water (aquaphobia) or water aversion:

• Computer screen brightness: Maintaining consistent brightness levels to avoid potential effects on perception.
• Room lighting: Ensuring uniform lighting conditions to eliminate variations in visual perception.
• Visual stimuli sizes: Standardizing the size of water images presented to participants to isolate the influence of aquaphobia on perception.

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How do you control a variable

Various methods can achieve the control of extraneous variables in experimental, quasi-experimental, observational, and research studies.  Among them, the following approaches are particularly effective:

Random assignment method

This method proves most beneficial in experimental studies involving multiple groups. It aims to manage participant variables that may differ between groups and potentially bias the outcomes. By randomly assigning participants to different groups or conditions, this approach mitigates systematic differences, ensuring a more balanced comparison.

Standardized procedures

Ensuring uniformity in procedures among all groups or conditions is crucial for maintaining consistency. By adhering to standardized protocols, researchers can uphold uniformity throughout the experiment or study. This involves keeping all factors constant for every participant, thereby minimizing the influence of extraneous variables on the outcomes.

Experimentation entails more than just altering one factor and observing the outcome. In truth, research encompasses a multitude of influencing factors. These factors play a crucial role in maintaining stability and consistency, thereby facilitating unbiased and transparent comparisons.

In numerous experiments, even slight variations in specific factors can result in inaccuracies and biased findings. Voxco’s research toolkit empowers researchers to manage variables more effectively, enabling them to attain desired outcomes in their experiments.

• Distinction Between Control Variable and Control Group:

It’s important to distinguish between a control variable and a control group in experimental research. While a control variable is a factor deliberately held constant, a control group serves as a comparison group that does not receive the experimental treatment.

A control group does not receive the experimental treatment. Instead, researchers compare their results to those of the group that did receive the treatment. Typically, a control group receives either no treatment, a standard treatment widely used, or a placebo (a sham treatment).

In an experimental procedure, everything besides the treatment being tested should remain consistent between the experimental and control groups

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A control variable refers to an aspect deliberately maintained constant during an experiment to facilitate the evaluation of relationships among various variables.

What is a Controlled Variable?

A controlled variable in an experiment is the one that the researcher holds constant or controls. 

It is also known as a constant or control variable. 

The constant factor is not included in the experiment. It is not an independent or dependent variable. However, a controlled variable is important because it can affect the experiment’s result.

Why is a Controlled Variable Important?

A controlled variable affects the results of an experiment, even though it is not the main focus. It serves a crucial role in ensuring the reliability and accuracy of results.

What are the examples of control variables?

When we define control variables, we can easily identify which parts of the experiment can be controlled. 

In a plant growth experiment, temperature is a control variable if it stays the same throughout the experiment. Other control variables include light, experiment duration, water amount, and plant pot size.

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• Research Methodology

What Are Control Variables? Definition, Uses & Examples

The knowledge of control variables is extremely important for the students and academic professionals which will give us critical insight into how it improves our research outcome. So, first, we will start with the control variables’ definitions and examples associated with them.  

A control variable is an experimental element which is constant or limited throughout the course of the research investigation. More often, the control variables may not have a direct interest in the aim and objectives of the study, but it tends to have a significant influence on the resulting outcome of the research. 

Not just the control variables definition , but we will put forth the examples of it for better clarity. 

For example, to evaluate the effect of soil quality on plant growth, the temperature, light and water are held constant during an experiment, which is referred to as the controlled variables. 

Similarly, to investigate the relationship between happiness and income, we measure the control variables of age, health and marital status. 

Let us provide some more examples for better understanding!!

In research studies and experiments, the aim is to understand the impact of an independent variable on a dependent variable. The control variables ensure to keep the experimental results are fair and unskewed devoid of any experimental manipulation. 

The above examples indicate that control variables ensure the results obtained are solely dependent on the experimental evaluation. The variables independent or dependent, are not the primary focus of any research, rather keeping their values constant throughout helps in the establishment of true correlations between the dependent and independent variables. Now don’t get confused between the control variables and control groups as they strike a stark distinction. 

In the research methodology , the use of control variables must be identified with recorded values to evaluate the results with precision. Moreover, the implication of control variables increases the internal validity of your research study which is otherwise a pretty difficult task to attain. To be specific, internal validity improves the degree of confidence in the differences you observe in the findings and attain the correct conclusions. 

Why is it important to have control variables?  

Very simple!! If researchers do not have control variables planned in the research methodology , it will become difficult to figure out or prove their exact impact on the results. It is crucial to find out whether the results of the research are an effect of the independent variable to justify experimental errors. Moreover, controlled variations are important because even the slightest variations in the research findings could have a significant influence on the results. Another major advantage of control variables points out the convenience of reproducing any research study while creating a strong relationship between the dependent and independent variables. 

 Taking over the examples set above, while we try to determine the effect of soil quality on plant growth, the independent variable refers to the soil quality whereas the dependent variable indicates the rate of plant growth. Hence, if we do not have control over the soil quality, we may end up with skewed results which may distort the actual outcome of the study.  

Approaches to control variables in Research  

You can make use of several approaches to control the variables in a research study. In some scenarios, variables can be controlled directly or by using standardized procedures which will be discussed further. 

• Some experiments can have several variables to control whereas in some cases the researchers may not be aware of all the potential variables that need to be controlled. Now, this sounds confusing, isn’t it? The random process controls of variables ensure to average of all the associated traits across the experimental groups which make them roughly equivalent while the experiment begins to start. Such random process controls prevent the occurrence of systematic differences between the multiple variable groups. 

Nevertheless, the direct approach and random control of the variables are effective in equalizing the experimental groups, however, it may not be feasible always. So we apply the statistical approaches for better clarity in the process. 

• Statistical techniques such as multiple regression do not create a balance between variable groups, rather it employs a robust model which can statistically control the variables. For example, the multiple regression analysis includes a variable within the model and holds it constant while the treatment variables keep on fluctuating. You can implement ANCOVA and ANOVA for the same process. 

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The effect of extended cold storage and the use of extenders on motility and swimming kinematics of the sperm of shortnose sturgeon

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Shortnose sturgeon are listed as a species of special concern in Canada and as endangered in the US. Increasing knowledge about this species, particularly in the area of reproductive biology, will better the management of wild populations and aid in the development of assisted reproduction protocols. However, access to wild sperm is limited, so short-term and long-term storage of sperm from sturgeon is crucial for reproductive studies. Here we report on testing and development of a short-term storage protocol for shortnose sturgeon. Milt samples were collected from wild shortnose sturgeon caught in the Wolastoq River. Subsets of semen were mixed with different extenders with or without oxygen; control treatments without extenders were also run. We used computer-assisted sperm analysis (CASA) to determine sperm motility and swimming kinematics for the different treatments. All groups were examined immediately after collection and treatment application, and then 1, 2, and 7 days after storage in a fridge (4°C) for experiment 1, and days 1, 3, 7, 10, 14, 17, 21, and 24 for experiment 2. The response variables motility, curvilinear velocity (VCL), linearity (LIN), and wobble (WOB) showed an overall decrease over time with differences between extender treatments. While untreated milt maintained some motility up to day 21, the addition of an extender reduced decline in motility and improved longevity up to day 24. Milt treated with the Park and Chapman extender had the slowest motility decline of extenders used, and milt treated with the modified Tsvetkova extender showed less potential for contamination.

Competing Interest Statement

The authors have declared no competing interest.

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Article Contents

Research highlights:, 1. toward a social chatbot to improve well-being, 2. can chatbots stimulate intimate self-disclosure, 3. perceived anonymity, 4. fear of judgment, 5. trust in the interaction partner, 6. self-disclosure and emotional well-being, 7. control variables, 10. discussion, data availability, a. appendix a.

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Digital Confessions: The Willingness to Disclose Intimate Information to a Chatbot and its Impact on Emotional Well-Being

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Emmelyn A J Croes, Marjolijn L Antheunis, Chris van der Lee, Jan M S de Wit, Digital Confessions: The Willingness to Disclose Intimate Information to a Chatbot and its Impact on Emotional Well-Being, Interacting with Computers , Volume 36, Issue 5, September 2024, Pages 279–292, https://doi.org/10.1093/iwc/iwae016

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Chatbots have several features that may stimulate self-disclosure, such as accessibility, anonymity, convenience and their perceived non-judgmental nature. The aim of this study is to investigate if people disclose (more) intimate information to a chatbot, compared to a human, and to what extent this enhances their emotional well-being through feelings of relief. An experiment with a 2 (human vs. chatbot) by 2 (low empathetic vs. high empathetic) design was conducted ( N  = 286). Results showed that there was no difference in the self-reported intimacy of self-disclosure between the human and chatbot conditions. Furthermore, people perceived less fear of judgment in the chatbot condition, but more trust in the human interactant compared to the chatbot interactant. Perceived anonymity was the only variable to directly impact self-disclosure intimacy. The finding that humans disclose equally intimate information to chatbots and humans is in line with the CASA paradigm, which states that people can react in a social manner to both computers and humans.

• There is no difference in intimate self-disclosure between a human and a chatbot interaction partner

• People experience less fear of judgment when talking to a chatbot

• People have more trust in a human interaction partner

• When people feel more anonymous, they self-disclose more intimately

The use of chatbots—conversational programs designed to show humanlike behavior by mimicking text- or voice-based conversations (e.g. Abdul-Kader and Woods, 2015 )—in different domains has increased exponentially over the past years. A recent development is the rise of social chatbots used for therapeutic purposes, also called mental health chatbots. Examples of these are Woebot, Tess, Wysa and Replika. The primary goal of such mental health chatbots is to be a virtual companion to its users and to monitor the user’s mood, by guiding them in disclosing their emotions and needs ( D'alfonso et al. , 2017 ). Woebot, e.g. was developed at Stanford University to help people suffering from depression or anxiety by monitoring the user’s mood and making use of cognitive behavioral therapy. The number of chatbots created to improve people’s emotional well-being is increasing, which illustrates the need in society for such chatbots. Therefore, it is important to better understand the social and emotional processes while interacting with these social chatbots.

One of the crucial factors in improving one’s well-being is people’s willingness to disclose personal information (e.g., Pennebaker, 1995 ; Sloan, 2010 ), so-called self-disclosure ( Joinson, 2001 ). By disclosing personal information, people are able to receive adequate help from family members, friends or professionals (e.g., Colognori et al., 2012 ). However, disclosing personal information can be perceived as risky and stigmatizing, especially when it concerns intimate or very personal information, which can hinder individuals from stepping forward to seek help from professionals or family and friends to disclose their inner feelings (e.g., Vogel and Wester, 2003 ; Eisenberg et al., 2009 ). Chatbots have several features that may stimulate self-disclosure and help-seeking by people in need, such as 24/7 accessibility, anonymity, convenience and their perceived non-judgmental nature ( Skjuve and Brandtzæg, 2018 ). One study shows that self-disclosure to a chatbot can be equally beneficial as self-disclosing to a human ( Ho et al., 2018 ). Another study shows that humans reciprocate the self-disclosure of a dialog system ( Ravichander and Black, 2018 ).

Self-disclosure can also benefit individuals by decreasing their stress symptoms and increasing positive affect (e.g., Kahn et al. , 2001 ). However, in order to further improve well-being, it is important for the interaction partner to react in an empathetic manner to the person’s disclosure of information ( Shenk and Fruzzetti, 2011 ; Reis et al. , 2017 ). It is known that disclosers need to believe that their conversation partner understands them before the positive impact of feeling understood, and hence the relief, can take place ( Reis et al., 2017 ). Research consistently shows that interpersonal processes such as empathy and warmth are essential factors to improve well-being ( Lambert and Barley, 2001 ). However, a chatbot is a computer program that cannot demonstrate true empathy as it does not have the capacity to understand human emotions and inner feelings ( Bickmore and Picard, 2005 ). Therefore, the chatbot’s responses can be perceived as inauthentic and hence not truly empathetic. In contrast, research shows that as long as a virtual agent appears to be empathetic and is accurate in the feedback it gives, it can achieve similar effects compared to a human who displays true empathy ( Klein et al. , 2002 ).

In sum, research shows that there is potential in the use of social chatbots to improve the user’s well-being. Moreover, studies highlight the importance of self-disclosure in improving well-being. Although there are studies on self-disclosure in human-chatbot communication and its beneficial effects (e.g. Ho et al. , 2018 ), what is currently lacking is a comparison of the (beneficial) effects of self-disclosure when interacting with a human, compared to a chatbot, and what underlying processes may enhance relief (and whether these differ in human–human and human–chatbot communication). Therefore, the aim of this study is to investigate if people disclose (more) intimate information to a chatbot (compared to a human) and to what extent this enhances their emotional well-being by means of relief and what underlying processes explain this effect.

Social chatbots have become popular in the last few years. The primary goal of such chatbots is to be a virtual companion to its users and to monitor the user’s mood, by guiding them in disclosing their emotions and needs ( D'alfonso et al. , 2017 ). Woebot, e.g. aims to help people who are suffering from depression or anxiety as it helps to monitor the user’s mood ( https://woebothealth.com ). Tess is another example of a popular social chatbot. According to her developers, Tess coaches its users through difficult times with the aim to build resilience via social chats similar to interacting with a friend or a coach ( https://x2ai.com ). Another popular chatbot is Wysa, which anonymously helps users with their anxiety and feelings of isolation. The chatbot Wysa is free, but if users also want to talk to a real counselor, they have to pay a monthly fee. According to the website, Wysa has helped over 2.5 million people and in 2020 the bot won the Orcha best App in Health & Care ( https://wysa.com ). In the users’ reviews, you see anecdotal evidence of the positive impact these apps have on their well-being.

Based on the popularity of these social chatbots and on the anecdotal evidence from their users, it seems that many users find these chatbots helpful. Chatbots can provide very efficient, familiar and easy one-on-one interactions with their users ( Vaidyam et al. , 2021 ). Furthermore, these interactions are low threshold and are often perceived as enjoyable ( Følstad and Brandtzaeg, 2020 ). Interacting with social chatbots is becoming more and more common and can provide a solution for several issues such as understaffing and waiting lists, as they are cost-efficient, adaptable and scalable (i.e. they are able to provide personalized advice to many people at the same time). These chatbots also have certain attractive features, such as continuous accessibility, convenience and their perceived non-judgmental nature.

However, the functionality of the social chatbots that are currently available is generally limited. Chatbots such as Wysa are more focused on tasking people with assignments rather than engaging in conversation ( Fitzpatrick et al. , 2017 ). Through conversation, it is possible to build a trusting bond ( Lambert and Barley, 2001 ), which in turn could enhance willingness to disclose ( Corritore et al. , 2003 ), and build an emotional connection between a user and a chatbot ( Savin-Baden et al. , 2013 ). Furthermore, scientific evidence on the use and impact of those chatbots is scarce. Previous studies, for instance, had a small sample size, and used the Wizard of Oz method (i.e. a method where participants are told that they will interact with a chatbot, when in actuality they will interact with a human interlocutor) to investigate differences in the perception of humans vs. chatbots (e.g. Bell et al. , 2019 ; Ho et al., 2018 ). While this method is effective, it is not suitable to gauge the current conversational capabilities of chatbots and contrast them with humans. Therefore, research on the possibilities and impossibilities of the use of chatbots to improve people’s well-being is needed.

To study the potential impact of social chatbots on emotional well-being, it is first necessary to determine if humans are willing to disclose their inner feelings to a chatbot. Self-disclosure is a necessary step in improving well-being. Specifically, disclosing one’s inner (hidden) feelings, secrets, memories and immediate experiences can enhance relief and, in turn, improve one’s mood ( Farber, 2006 ). The willingness to disclose depends on several factors such as the anticipated utility (i.e. the perceived value of the outcome to the individual for disclosing), but also the anticipated risks (i.e. the perceived risks of self-disclosing; Vogel and Wester, 2003 ). The person disclosing the information might be fearful that information is shared with others or they might feel ashamed and be afraid that the recipient is being judgmental or critical ( Farber, 2006 ).

Based on Derlega and Grzelak’s (1979) functional theory of self-disclosure, self-disclosure is a strategic behavior that individuals use to achieve their personal goals. The authors identified five goals that people may achieve: self-expression (venting negative emotions), self-clarification (clarifying one’s own identity and opinions), social validation (gaining social support and acceptance), relationship development (development and/or maintenance of personal relationships) and social control (using information to gain control). Following this functional theory, Omarzu (2000) designed a disclosure decision model, to explain which factors affect disclosure decision-making (see Fig. 1 ). This model proposes that people pursue strategic goals when self-disclosing and disclose different types of information depending on various media functions and situational cues. For example, a relational development goal is more accessible in a romantic setting (situational cue) compared to an office setting ( Bazarova and Choi, 2014 ). Furthermore, the disclosure decision model poses that subjective risk influences self-disclosure intimacy in particular. Subjective risk refers to the potential risks anticipated by the discloser, such as social rejection ( Omarzu, 2000 ). According to this model, as subjective risk increases, self-disclosure intimacy decreases.

The disclosure decision model.

Although according to the functional theory of self-disclosure situational cues are believed to activate individual disclosure goals, the disclosure decision model does not account for the underlying mechanisms that underlie the activation process ( Omarzu, 2000 ). Based on earlier research on self-disclosure in (online) interpersonal communication (e.g. Antheunis et al. , 2012 ; Joinson, 2001 ) and human–chatbot interactions (e.g. Ho et al. , 2018 ; Croes and Antheunis, 2021 ), we have identified three possible underlying mechanisms that may play a role in the activation of self-disclosure to a chatbot, namely perceived anonymity, fear of judgment of the interaction partner and trust in the interaction partner.

A first underlying mechanism in the elicitation of self-disclosure is anonymity. Feelings of anonymity stimulate self-disclosure (see a meta-analysis of Clark-Gordon et al. , 2019 ). As people feel more anonymous, their public self-awareness decreases, which reduces identifiability or accountability concerns ( Scott, 1998 ), which in turn results in feelings of disinhibition and can result in more intimate self-disclosure (e.g. Antheunis et al. , 2007 ; Clark-Gordon et al. , 2019 ; Joinson, 2001 ). This process is oftentimes associated with the stranger on the train phenomenon in which people disclose their inner feelings to unknown travel companions on a train ( Antheunis et al. , 2007 ).

Due to the potentials risks, such as stigmatization, associated with disclosing very personal/intimate information with others ( Link et al. , 1991 ), people can be hesitant to disclose personal information (e.g. Lucas et al. , 2017 ). The fear of being stigmatized can act as a barrier to disclosing one’s inner feelings, thoughts and symptoms ( Lucas et al. , 2017 ). Feeling more anonymous can reduce that barrier.

It is likely that individuals feel more anonymous when interacting with a chatbot compared to a human. Hence, they might feel more disinhibited and dare to disclose more intimate information than they would to a human. For example, research on reporting sensitive information shows that assessment by virtual agents, as they afford anonymity, increases the level of (honest) reporting, such as on suicide ( Greist et al. , 1973 ) and posttraumatic stress disorder ( Lucas et al. , 2017 ). Furthermore, since a chatbot is an artificial being, people view it as good at keeping secrets, as it cannot share the information with others ( Skjuve and Brandtzæg, 2018 ). Thus, the artificial nature of a chatbot and its lack of feelings means that people are likely to feel more anonymous which means people are more likely to open up to a chatbot, compared to another human. Therefore, we pose the following hypothesis:

H1: (i) Individuals feel more anonymous when interacting with a chatbot, compared to a human interlocutor, which in turn leads to (ii) more intimate self-disclosure to a chatbot compared to a human interlocutor.

Another underlying mechanism in the elicitation of self-disclosure is a lack of fear of judgment, which also closely relates to perceived anonymity. Humans sometimes avoid disclosing intimate information to other humans out of a fear of negative evaluation (e.g. disapproval, social rejection, stigma, embarrassment), even more so when it is information that might reflect poorly on the self (e.g. Afifi and Guerrero, 2000 ; Lane and Wegner, 1995 ). Hence, the fear of negative evaluations hinders humans to disclose intimate information to other humans.

Chatbots might be perceived as non-judgmental as they do not think or form judgments on their own ( Lucas et al. , 2014 ). Therefore, individuals might feel more at ease to disclose personal information to a chatbot compared to another human without being judged or embarrassing their interaction partner ( Skjuve and Brandtzæg, 2018 ). This can be beneficial when disclosing potential stigmatizing information, or very intimate information. There is some empirical evidence pointing in that direction. Weisband and Kiesler (1996) found in a meta-analysis that computer administered assessment methods result in more personal self-disclosure than non-computerized methods (i.e. with a human). More recently, using virtual human interviewers, Lucas et al. (2014) showed that virtual humans (avatars) increase the willingness to disclose in situations in which the fear of a negative evaluation is more prominent. Comparable results were found by Kang and Gratch (2010) but only for socially anxious people. When disclosing intimate information to another person, individuals can be afraid of the other person’s moral judgments. This can lead to them abstaining from self-disclosing information that violates certain morals ( Mou and Xu, 2017 ).

This does not appear to be the case when talking to a chatbot, where fear of judgment may decrease or disappear altogether due to a chatbot’s inability to think or form opinions. For this reason, we expect the following:

H2: (i) Individuals experience less fear of judgment when interacting with a chatbot, compared to a human interlocutor, which in turn leads to (ii) more intimate self-disclosure to a chatbot compared to a human interlocutor.

The third underlying mechanism in the effect of conversation partner on self-disclosure is trust in the interaction partner (the object of trust), which is formed through elements as honesty and psychological safety ( Tillmann-Healy, 2003 ). When someone trusts the interaction partner, they will feel more at ease to self-disclose ( Burgoon and Hale, 1984 ; Lee and Choi, 2017 ). There is currently no consensus in the literature if humans trust a human interaction partner more than a chatbot interaction partner. On the one hand, human–chatbot communication is likely to foster a sense of trust because of the artificial nature of the object of trust (i.e. the chatbot) and hence confidential nature of the interaction. As mentioned above, because of the artificial nature of chatbots, they are believed to be good at keeping secrets. This inherently suggests that artificial interaction partners, like chatbots, can be trusted more, compared to a human interaction partner ( Skjuve and Brandtzæg, 2018 ). More specifically, when disclosing intimate information to a chatbot, people can trust that this information will not be passed on to others. Thus, the characteristic of the chatbot—confidentiality and artificiality—signals trustworthiness, which fosters trust in the chatbot as an interaction partner.

However, on the other hand, there are several reasons to believe that humans will trust a human interaction partner more compared to a chatbot. A first issue that is at stake is that of moral agency. Corritore et al. (2003) discuss in their work the approach to define the relationship between the trustor (e.g. the human user) and technologies as an object of trust (e.g. the chatbot as the interaction partner). According to philosophers, technologies cannot be seen as moral agents, which can be defined as objects that have intentions and free will ( Solomon and Flores, 2001 ). Technologies do not have intentionality nor free will, and hence cannot be trustworthy. Contrary to this perspective is that technologies can be seen as social actors. Corritore et al. (2003) stated that in order to be trusted technologies do not have to be moral agents, it is enough to be a social actor (see work of Reeves and Nass (1996) and Nass et al. (1995 , 1996 )).

A second issue is that humans have concerns related to privacy and security of their personal data in a chatbot interaction. In interactions with a chatbot this data is frequently stored automatically as it is used to improve the chatbot’s communication, and this can hinder feelings of trust ( Følstad et al. , 2018 ). Users can perceive a risk that the information on the computer will not be stored well and hence their data can be accessed by ill-intentioned people. This risk perception might even be stronger when having a personal interaction with the chatbot instead of a more functional interaction (e.g. customer service). Since the argumentation of trust in chatbot as an interaction partner (vs. a human) is conflicting, we cannot formulate a hypothesis. In its place, we pose a research question:

RQ1: (i) Do individuals trust a chatbot more than a human interlocutor, and does this in turn lead to (ii) more intimate self-disclosure to a chatbot compared to a human interlocutor?

In this study, we first want to investigate if humans are willing to disclose their inner feelings to a chatbot. Based on the underlying processes (i.e. perceived anonymity, fear of judgment, trust in interaction partner) that potentially take place when humans interact with a chatbot versus a human, we might expect that humans are willing to self-disclose to a chatbot interaction partner, however it is not clear if they will disclose more intimate information to a chatbot compared to human interaction partner. Therefore, we formulate a research question:

RQ2: (i) Are people willing to disclose intimate personal information to a chatbot and (ii) do they disclose more intimate personal information to a chatbot or to a human?

A second step to investigate is if the self-disclosed intimate information to a chatbot also enhances the discloser’s emotional well-being, by means of relief. Self-disclosure can benefit individuals by decreasing their stress symptoms and increasing positive affect (e.g. Kahn et al. , 2001 ). This can be explained by the cognitive processing of writing about personal matters ( Pennebaker, 1993 , 1995 ), also referred to as therapeutic writing or expressive writing paradigm. If people disclose about emotional experiences (e.g. loss, a shameful secret) the negative affect will be reduced as by writing it down one turns the negative emotions and feelings (the affect) into something cognitive instead and they will reevaluate the event and/or the emotion. The transition from affect to cognition can reduce the intensity of the emotion ( Lieberman et al. , 2007 ) and hence give some relief.

In line with the positive intrapersonal effect of self-disclosure explained by the expressive writing paradigm, there is also a catharsis effect defined for a positive inter personal effect of self-disclosure. Relief might be experienced after disclosing intimate information, mostly when the information elicits strong emotions, like shame, fear or worries. In 1935, Freud, 1935 referred to this as the catharsis effect of self-disclosure: ‘Disclosure of distress directly reduces such negative affect through a catharsis effect.’ ( Derlaga, & Berg, 1987 , p. 233). Because the discloser is openly expressing negative emotions, these emotions are depleted more quickly instead of letting them aggravate. Ample research has found that self-disclosure can improve a person’s emotional state by diminishing negative affect and stress, and growing feelings of relief (e.g., Omarzu, 2000 ; Farber et al. , 2004 ; Pennebaker and Chung, 2007 ; Ho et al. , 2018 ). Therefore, we expect that:

H3: Self-disclosure intimacy will enhance perceived relief.

This positive effect of self-disclosure on relief can be strengthened if the interaction partner responds in an empathetic manner ( Shenk and Fruzzetti, 2011 ; Reis et al. , 2017 ). If the interaction partner shows that they understand the discloser, a sense of belonging and acceptance is created and areas of the brain are activated that are associated with connectivity and reward ( Reis et al. , 2017 ). Reis and colleagues ( Reis and Shaver, 1988 ; Reis et al. , 2017 ) clearly state that feeling understood, exceeds just recognizing the disclosed information. Feeling understood is established when disclosers really get the impression that the interaction partner understands them. A chatbot, however, is a computer program that cannot demonstrate true empathy as it does not have the capacity to understand human emotions and inner feelings ( Bickmore and Picard, 2005 ). The chatbot’s responses can therefore be perceived as inauthentic and hence not truly empathetic. In contrast, research shows that as long as a virtual agent appears to be empathetic and is accurate in the feedback it gives, it can achieve similar effects compared to a human who displays true empathy ( Klein et al. , 2002 ; Ho et al. , 2018 ). Although another study shows that the most reduction of stress and worries was in the human condition, empathetic responses of both humans and a chatbots do contribute to reduction of stress and worries ( Meng and Dai, 2021 ). Thus, we expect a moderating effect on the self-disclosure intimacy effect on relief if the interaction partners respond in an empathetic manner. Our final hypothesis reads:

H4: The effect of self-disclosure intimacy on perceived relief is contingent upon the perceived empathy of the interaction partner.

Our proposed hypotheses may also be impacted by age, gender and/or alcohol use. Although we do not formulate hypotheses about the potential effects of these variables, they may have an influence on the dependent variables in our study. Specifically, research shows that younger people (18–25 years old) may feel less inhibited to self-disclose due to lower levels of privacy concerns and higher levels of trust, compared to older people, which may be because of their comfort with using technology ( Lappeman et al., 2023 ). Additionally, gender has been found to impact self-disclosure, with women often disclosing more (intimately) than men (e.g. Dindia and Allen, 1992 ). Finally, we included alcohol use as a control variable because alcohol consumption can impact self-disclosure as it makes people more disinhibited (e.g. Caudill et al. , 1987 ; Lyvers et al. , 2020 ).

8.1. Sample and design

A total of 286 (60% female, 40% male) visitors of a large three-day music festival between 16 and 61 years of age ( M  = 26.23; SD = 7.20) participated in our experiment. The sample is rather skewed for level of education, as the majority was higher educated. Participants were asked for their highest level of education (current or completed) and almost half of the participants were (former) university students (47.9%), (former) applied university students (31.8%), (former) high school students (10.1%) and (former) intermediate vocational education students (8%).

For this study, we adopted a 2 (human versus chatbot) by 2 (non-empathetic versus empathetic) between subject experimental design. In our analyses, we recoded the four conditions so that we only directly compared the human vs. chatbot conditions. Therefore, we did not directly compare the empathic vs. non-empathic conditions. We included this condition in our design to ensure more variation in terms of empathy. Instead, we include the self-report measurement of empathy as a moderator (see H4), as this gives a clearer overview of how empathic participants felt their interaction partner actually came across. The participants were randomly assigned to one of the four conditions. In all the conditions, they had a one-on-one interaction and were asked to confess something to either a human confederate or a chatbot. We used the chat function of the Discord platform in all conditions (see Fig. 2 for details). As the task of the human confederate was intensive, we trained six confederates, which were allocated to 3-hour time slots during the 3 days of data collection (see Appendix A for more detailed confederate instructions and the questions that were asked in all conditions). For the experiment, a chatbot was developed which was used in the chatbot condition. The chatbot is a modular and open-source chatbot (for details, see AUTHORS and url [ANONYMIZED]).

Screenshot of a (simulated) conversation between chatbot (PRIESTESS) and user (Biechthok 1) on the Discord platform that was used for this study.

In both the chatbot and the human condition, the same procedure was followed, using a script with predefined questions and answers (see Appendix A for the questions). For the human condition, an extra interface was created to help with the conversation flow, and answer content (see Fig. 3 ). At the start of the conversation, the chatbot/confederate asks icebreaker questions (e.g., W hat do you think of [name of the festival] thus far? Which artists have you seen? ). The chatbot interprets users’ answers to these icebreaker questions using predefined lexicons and Dutch sentiment analysis tool Pattern ( De Smedt and Daelemans, 2012 ). This means that every message being sent to the chatbot is scanned for words that may convey the direct answer to the question, or may convey a positive or negative sentiment. The chatbot then uses this information to pick the best answer from a list of preprogrammed answers. Furthermore, the chatbot tries to respond to the users’ self-disclosed personal information in the empathetic condition in an empathetic manner. This is done using LIWC ( Pennebaker et al ., 2015 ), a program that uncovers underlying topics in text. More specifically, LIWC scans answers for words that provides information about the topic which the answer was focused on (e.g. family, work, food). This information is then used to pick the most appropriate answer from a list of preprogrammed answers.

Screenshot of the chat interface that confederates could use as support for their interactions.

8.2. Procedure

This procedure was reviewed and approved by the university Research Ethics and Data Management Committee (REDC # 2019192). This experiment was conducted at a large, annual 3-day music and performing arts festival with over 50 000 visitors in August 2019, resulting in data from a naturalistic setting. The festival’s program consists of a broad variety of music acts, ranging from dance (e.g. Paul Kalkbrenner) to hardcore punk (e.g. Turnstile) and from popular music for the young (e.g. Billie Eilish) and the elderly (e.g. Giorgo Moroder). Hence, the visitors of the festival are pretty heterogenous. While the festival’s main focus is on live music, the festival also offers cinema, theatre, cabaret, literature and the possibility to take part in various scientific experiments at what is called ‘Lowlands Science’. The teaser for our study was Digital Confessions, in which we asked people via posters if they wanted to confess a secret digitally. The visitors that were interested in participating could do so voluntarily.

The visitors of the festival that wanted to participate in our study were thoroughly briefed after which they gave consent. Participants were randomly assigned to one of the four conditions, and they were clearly told beforehand whether they were going to confess to a chatbot or a human, depending on the assigned condition. Next, they were led to a cubicle in which they were seated in front of a laptop (see Fig. 4 for the study setup). One of the researchers then typed ‘start’ in the chat window, which started the interaction. After this cue was entered, either the chatbot or the confederate in the human condition started the interaction by asking some introductory questions about the festival and the bands they have seen to increase the depth of the interaction ( Berger and Calabrese, 1975 ). This part of the conversation was scripted and both the human and the chatbot followed the same script.

Set up of the confessional booths.

After these chitchat questions about the festival and bands, participants were asked to confess/tell their secret. The response of the chatbot or human confederate depended on the condition they were in. In the non-empathetic condition, they responded with ‘Thank you for sharing your secret. Is there anything else you want to say?’ after which the participants were thanked for their participation. In the empathetic condition, the conversation partner (human or chatbot) responded empathetically—either automatically (in the chatbot condition, using LIWC) or manually (in the human condition, choosing from several options from a script) on the disclosed topic and also asked, ‘how do you feel after disclosing your secret?’ After ending the chat, participants were sent the link of the questionnaire. When the participants finished the questionnaire, they were tested on their alcohol level with a breath analyzer device. And after that, they were debriefed (i.e. they were told about the exact topic of study) and thanked for their participation.

8.3. Self-report measurement

8.3.1. fear of judgment.

To measure fear of judgment, we used four items of the Fear of Negative Evaluation Scale ( Leary, 1983 ) that were slightly adapted to the situation of this experiment. The items were introduced by ‘During the conversation….’ followed by ‘…I worried what kind of impression I made on her,’ ‘…I worried what she was thinking about me,’ ‘…I worried what she was thinking of me,’ and ‘…I was afraid she was judging me.’ The response categories for each of the items ranged from 1 ( completely disagree ) to 5 ( completely agree ). The four items formed a one-dimensional scale (explained variance 86%), with a Cronbach’s Alpha of .94 ( M  = 2.22, SD  = 1.04).

8.3.2. Trust in interaction partner

To measure trust, we used four items from the Individualized Trust Scale (ITS) of Wheeless and Grotz (1977) . The items were on a five-point semantic differential scale. The items were introduced by ‘My conversation partner was…’ followed by: Unreliable—Reliable, Untrustworthy—Trustworthy, Insincere—Sincere, Malevolent—Benevolent . These four items formed a one-dimensional scale (explained variance 58%) with a Cronbach’s Alpha of .75 ( M  = 3.51, SD  = 0.80).

8.3.3. Perceived anonymity

To measure perceived anonymity a four-item scale was constructed based on Rains (2007) , Qian and Scott (2007) , and Hite et al. (2014) . Participants were asked to indicate their feelings of anonymity during the conversation. The items were: ‘During the conversation I felt I was anonymous,’ ‘During the conversation I felt I was unrecognizable,’ ‘During the conversation I felt I could not be identified,’ and ‘During the conversation I felt I could share more about myself because she did not know me.’ The items loaded on a one-dimensional scale (explained variance 64%), with a Cronbach’s alpha of .81 ( M  = 3.31, SD = 0.95).

8.3.4. Perceived self-disclosure intimacy

The perceived level of intimacy of the participant’s self-disclosure was measured by four bipolar items based on the work of Rubin and Shenker (1978) and Lin and Utz (2017) . Participants were asked to rate the disclosed secret on a five-point scale. The items were ‘Not at all intimate – Very intimate,’ ‘Very impersonal – Very personal,’ ‘Trivial – Important,’ and ‘Not confidential at all – Very confidential.’ The items formed a one-dimensional scale (explained variance 67%), with a Cronbach’s alpha of .84 ( M  = 3.27, SD = 1.00).

8.3.5. Perceived empathy

The perceived empathy was measured by four items based on Stiff et al. (1988) . The items were ‘The interaction partner said the right thing to make me feel better,’ ‘The interaction partner responded appropriately to my feelings and emotions,’ ‘The interaction partner came across as empathetic,’ and ‘The interaction partner said the right thing at the right time.’ The response categories ranged from 1 ( completely disagree ) to 5 ( completely agree ). All items loaded on a one-dimensional scale (explained variance 68%) with a good Cronbach’s alpha of .843 ( M  = 2.87, SD = 0.84).

8.3.6. Relief

The measurement of relief was based on a measurement used by Ho et al. (2018) , which the addition of one extra item. Thus, the final scale consisted of three items, which were ‘I feel more optimistic now that I have confessed my secret,’ ‘I feel better now that I have confessed my secret,’ and ‘I feel relieved now that I have confessed my secret’ (extra item). The response categories for each of the items ranged from 1 ( completely disagree ) to 5 ( completely agree ). The items formed a one-dimensional scale (explained variance 85%), with a Cronbach’s alpha of .91 ( M  = 2.65, SD = 0.95).

8.3.7. Alcohol use

To measure if and how much alcohol participants had consumed, we did an alcohol test after the experiment. The participants had to blow in a breathalyzer, measuring the alcohol in their breath. Out of 286 participants, 178 did not consume any alcohol. For those who did have alcohol, the alcohol ranged from 0.05 to 2.07 per mile.

8.4. Content analysis: self-disclosure intimacy

The conversations in all four conditions were logged and saved, and the confessions were coded for intimacy of self-disclosure by two judges. The average length of the confessions was 32.89 words (SD = 41.65). All 286 confessions were divided evenly among the two judges who received extensive training with a codebook, which was discussed among the judges and contained examples as illustrations. After receiving these instructions, both judges coded the same 64 confessions (20%). The remaining confessions were divided evenly among both judges after intercoder reliability was deemed sufficient. For self-disclosure and intimacy of self-disclosure, Kappa was calculated as a measure for intercoder reliability, with the benchmark by Landis and Koch (1977) to determine strength of agreement.

First, self-disclosure was coded by assigning each confession to either a self-disclosure (1) or no confession (i.e. other) (2). Self-disclosure was operationalized as a confession revealing personal information about the self, telling something about the person, describing the person in some way or referring to the person’s experiences, thoughts or feelings ( Antheunis et al. , 2012 ; Tidwell and Walther, 2002 ). An example of a self-disclosure in the current study is ‘I had a really good date last week’. Confessions that could not be coded as a self-disclosure were coded as ‘other’. These were so-called ‘empty confessions’, such as ‘I do not really have anything to confess’ or ‘I don’t know what to confess’. These ‘confessions’ were excluded from further analyses. Intercoder reliability was perfect for self-disclosure ( κ  = 1).

Next, the judges coded the degree of intimacy of each disclosure, also known as the depth ( Tidwell and Walther, 2002 ). Altman and Taylor’s (1973) classification scheme was used to rate each disclosure as either low (i), medium (ii) or high (iii) in intimacy. This classification scheme consists of three layers. The first layer is the peripheral layer, which is concerned with biographical information such as age, gender, height and other basic information. An example is ‘My girlfriend and I are living together’. The second layer is the intermediate layer, which is concerned with opinions, attitudes and values, e.g. ‘I really dislike my roommate’. The final layer is the core layer, which consists of personal beliefs, fears, emotions and things people are ashamed of ( Antheunis et al., 2012 ; Tidwell and Walther, 2002 ). An example is ‘I am afraid that I am no longer in love with my boyfriend’. Intercoder reliability for intimacy of self-disclosure was perfect ( κ  = 1).

To test the first two hypotheses and RQ1–RQ2, a mediation analysis was performed using a PROCESS analysis (model 4). All analyses were conducted twice: with the self-report measure of self-disclosure intimacy and with the coded variable of self-disclosure intimacy. We used bootstrapping to test the mediated effects for significance based on 10 000 bootstrap samples, accompanied by 95% bias corrected and accelerated confidence intervals (BCa CI’s). In the analyses the categorical condition variable was recoded into a dummy variable (i.e. 0 = chatbot, and 1 = human).

H1 proposed that (i) individuals feel more anonymous when interacting with a chatbot, compared to a human interlocutor, which in turn leads to (ii) more intimate self-disclosure to a chatbot compared to a human interlocutor. The results for the self-report data showed that the condition variable did not significantly impact perceived anonymity ( b  = −0.18, SE = 0.11, P  = .114). Next, the analysis revealed a significant effect of perceived anonymity on self-reported self-disclosure intimacy, b  = 0.28, SE = 0.06, P  < .001. This showed that perceived anonymity enhanced perceived intimate self-disclosure. Furthermore, the analysis revealed that anonymity did not significantly mediate the effect of condition on self-reported self-disclosure intimacy, b  = 0.05, SE = 0.03, 95% BCa CI [−.12, .01]). For the coded data, the findings showed that the condition did not significantly impact perceived anonymity, b  = −0.18, SE = 0.11, P  = .122 and coded self-disclosure intimacy, b  = 0.06, SE = 0.06, P  = .305. Moreover, anonymity was not a significant mediator either, b  = −0.01, SE = 0.01, 95% BCa CI [−.05, .01]. Thus, for self-reported self-disclosure intimacy, hypothesis 1a was rejected and 1b was supported. For the coded data the entire first hypothesis was rejected. The means are shown in Table 1 .

Means and standard deviations for all variables.

Note. Standard deviations appear in brackets below means.

The results of the mediation analysis are visualized in Figs 5 and 6 .

Observed model (part 1; mediation) explaining the effects for self-reported self-disclosure intimacy.

Observed model (part 1; mediation) explaining the effects for coded self-disclosure intimacy.

H2 posed that (i) individuals experience less fear of judgment when interacting with a chatbot, compared to a human interlocutor, which in turn leads to (ii) more intimate self-disclosure to a chatbot compared to a human interlocutor. The analysis for the self-report data showed that condition significantly impacted fear of judgment, b  = 0.38, SE = 0.12, P  = .002. People experienced more fear of judgment with a human interlocutor, compared to a chatbot. Fear of judgment did not significantly impact self-disclosure intimacy, b  = 0.07, SE = 0.06, P  = .194. Moreover, fear of judgment did not significantly mediate the effect of condition on self-disclosure intimacy, b  = 0.03, SE = 0.02, 95% BCa CI [−.02, .08]. For the coded data, the findings also showed that the condition significantly impacted fear of judgment, b  = 0.38, SE = 0.12, P  = .002. Furthermore, fear of judgment did not significantly impact self-disclosure intimacy, b  = 0.00, SE = 0.05, P  = .971 and was not a significant mediator either, b  = 0.00, SE = 0.02, 95% BCa CI [−.04, .04]. Therefore, for both the self-reported and the coded data, hypothesis 2 was only partially supported.

RQ1 asked whether (i) individuals trust a chatbot more than a human interlocutor, and whether this leads to (ii) more intimate self-disclosure to a chatbot compared to a human interlocutor. The results showed that the condition significantly impacted perceived trust for the self-report data ( b  = 0.42, SE = 0.09, P  < .001). Individuals trusted the human interaction partner more than the chatbot. Trust did not significantly impact self-reported self-disclosure intimacy ( b  = 0.13, SE = 0.08, P  = .085) and was not a significant mediator either ( b  = 0.06, SE = 0.04, 95% BCa CI [−.01, .14]). Furthermore, for the coded data, the condition was also found to significantly impact trust, b  = 0.43, SE = 0.09, P  < .001. Trust did not significantly impact the coded self-disclosure variable, b  = −0.07, SE = 0.05, P  = .359 and was not a significant mediator for this variable either, b  = −0.03, SE = 0.03, 95% BCa CI [−.10, .03].

To test H3 and H4, a moderation analysis was performed using PROCESS (model 1), where self-disclosure intimacy was entered as a predictor to relief, and perceived empathy was entered as the moderator. The analysis for the self-report data showed that self-disclosure intimacy did not significantly impact relief, b  = 0.20, SE = 0.17, P  = .233. The interaction effect between self-disclosure intimacy and empathy was not significant either, b  = −0.00, SE = 0.06, P  = .983. Regarding the coded data, the analysis showed that the coded self-disclosure intimacy variable did not significantly impact relief, b  = −0.12, SE = 0.23, P  = .587. The interaction effect between self-disclosure intimacy and empathy was not significant either, b  = 0.06, SE = 0.08, P  = .404. Thus, for both the self-reported perceived self-disclosure and the coded self-disclosure variable, H3 and H4 were not supported. The results are visualized in Figs 7 and 8 .

Observed model (part 2; moderated mediation) explaining perceived empathy as a moderator in the self-disclosure—relief effect for self-reported self-disclosure intimacy.

Observed model (part 2; moderated mediation) explaining perceived empathy as a moderator in the self-disclosure—relief effect for coded self-disclosure intimacy.

Regarding RQ2, we analyzed the direct effect of condition on self-disclosure intimacy. The results showed that this effect was not significant for the self-report data, b  = 0.03, SE = 0.12, P  = .794. These findings suggest that people do disclose intimate information, but the disclosed information is equally intimate when disclosed to the chatbot, compared to the human interlocutor. However, for the coded data, we did find a significant effect, b  = 0.35, SE = 0.11, P  = .002. Specifically, the results showed that people disclosed more intimate information to a human interlocutor ( M  = 2.34; SD = 0.82) compared to a chatbot ( M  = 2.04; SD = 0.91).

9.1. Control variables

We also controlled our analyses for gender and the age of the participants, as well as alcohol use. Here, we only mention the significant effects. The analysis with the self-report data showed that age significantly impacted trust (RQ1), b  = −0.01, SE = 0.01, P  = .026. This shows that as age increases, trust in the interaction partner decreases. Alcohol use also significantly impacted trust, b  = −0.37, SE = 0.14, P  = .009; the higher the participants’ alcohol use, the less they trusted their interaction partner. Furthermore, with the addition of the variable alcohol use, there was a significant mediating effect of condition on intimate self-disclosure via trust (RQ1), b  = 0.07, SE = 0.04, 95% BCa CI [.00, .15].

For the coded data, we found similar results. Specifically, we found that age significantly impacted trust (RQ1), b  = −0.01, SE = 0.01, P  = .028. This shows that as age increased, trust in the interaction partner decreased. Alcohol use also significantly impacted trust, b  = −0.36, SE = 0.14, P  = .009; the higher the participants’ alcohol use, the less they trusted their interaction partner. Furthermore, for the self-report data the results showed that age significantly impacted intimate self-disclosure (RQ2), b  = 0.02, SE = 0.01, P  = .027. Specifically, as age increased, people disclosed more intimate information.

In this study, we examined whether people are willing to disclose intimate information to a chatbot and whether they disclose more intimate information to a chatbot, compared to another human. In line with our first hypothesis, we found that perceived anonymity enhances perceived intimate self-disclosure (H1b). We only found this effect for the self-report data and not for the coded data. Previous research also showed that perceived anonymity stimulates self-disclosure, as people feel more disinhibited (e.g. Antheunis et al. , 2007 ; Joinson, 2001 ). However, our findings also show that people feel equally anonymous when communicating with a human via CMC as when communicating to a chatbot. This can be explained by the fact that the number of cues were exactly the same in both conditions. The only difference was that the participants knew they were talking to either a human or a bot, but the interaction interface was exactly the same.

Furthermore, these findings add to Derlega and Grzelak’s (1979) functional theory of self-disclosure and Omarzu’s (2000) disclosure decision model, which propose that situational cues activate individual disclosure goals. Specifically, in this study, participants confessed a secret in a private, confessional setting, on a laptop in a text-based conversational interface. These situational cues, which were the same in both the human and chatbot conditions, may have activated specific individual goals (i.e. self-expression, relief of distress) and enhanced self-disclosure through perceived anonymity, irrespective of the conversation partner. Neither the functional theory of self-disclosure or the disclosure decision model take into account underlying mechanisms that may explain how self-disclosure is activated and our findings show that perceived anonymity may play an important role in the activation process. This may, however, depend on which self-disclosure goal is activated in a particular setting.

Second, in line with our expectations, we found that the participants in our study perceived the chatbot as less judgmental compared to the human interlocutor, which means they experienced less fear of negative evaluation when making their confession. Although our study confirms that people perceive a chatbot as non-judgmental, this did not enhance intimate self-disclosure. It may be that fear of judgment is only a determinant among people who are socially anxious and are more inhibited to self-disclose. Specifically, Kang and Gratch (2010) found that socially anxious people, who experience more fear of judgment, disclose greater intimate information about themselves when talking to a virtual human. Thus, it may be that for the sample in the present study, which was a general sample of people who voluntarily participated in the experiment and hence were already willing to tell a secret, fear of judgment was not a significant predictor of self-disclosure intimacy.

Regarding trust, previous research showed conflicting findings. Although there is evidence that trust enhances self-disclosure (e.g., Burgoon and Hale, 1984 ; Lee and Choi, 2017 ), there is no consensus in the argumentation in the literature if humans trust another human interaction partner more compared to a chatbot. Our findings showed that individuals trusted the human interaction partner more than the chatbot, for which there are several reasons. First, from a philosophical standpoint, technologies cannot be viewed as moral agents and hence objects of trust, because they do not have free will or intentionality ( Solomon and Flores, 2001 ), even when they act as social actors. Second, there may be privacy and security concerns when talking to a chatbot that inhibit trust ( Følstad et al. , 2018 ). When interacting with a chatbot, personal data, including the content of the interactions, are often stored and used to improve the chatbot, which can impede trust, especially with social chatbots, as conversations can be quite personal. Our results underscore the potential negative impact of privacy and security concerns in chatbot communication.

When controlling our analyses for alcohol use, we found that the more alcohol individuals consumed, the less they trusted their interaction partner. Research confirms that that consuming alcohol can make people more disinhibited, which can enhance self-disclosure (e.g., Caudill et al. , 1987 ; Fillmore, 2007 ; Lyvers et al. , 2020 ). However, since alcohol use was only included as a control variable in the present study, future research should dive further into the impact this variable has on self-disclosure intimacy and other relevant variables. Furthermore, with the inclusion of this control variable, we found a positive, mediation effect: when talking to another human, people felt more trust, which increased intimate self-disclosure. This can be explained by the level of suspicion. People might be more suspicious toward new technologies (e.g. chatbots) than toward humans.

Finally, based on previous research we expected that self-disclosure intimacy would enhance positive affect and decrease feelings of stress ( Kahn et al. , 2001 ). Disclosing intimate, emotional experiences by writing (or typing) it down can reduce emotional intensity by allowing individuals to reevaluate the experience or emotion, which can provide relief ( Lieberman et al. , 2007 ). Specifically, when one openly expresses negative emotions, these emotions dwindle more quickly which can enhance feelings of relief (e.g. Farber et al. , 2004 ). Our findings do not corroborate previous research; in this study self-disclosure intimacy did not enhance relief. Furthermore, this experienced relief was not contingent upon the perceived empathy of the interaction partner, which is also what we expected (H4). This may be explained by the fact that the confessions in the present study were overwhelmingly positive; 189 out of the 286 confessions were positive, 82 were negative, and 14 were coded as being neutral. Previous research shows that especially sharing disclosures that evoke negative emotion relieves stress ( Bazarova and Choi, 2014 ). In contrast, positive disclosures are found to enhance a feeling of connection between two people ( Utz, 2015 ). Since the majority of the disclosures in the present study were positive, this may explain why self-disclosure did not enhance relief.

10.1. Theoretical and practical implications

Our study has several implications for future theory and research. First, our study has implications for research on humans’ social behavior with chatbots, as we not only investigated the willingness to self-disclose toward a chatbot (a computer) versus a human, but we also considered relevant underlying mechanisms in the process of self-disclosure (i.e. anonymity, trust in the interaction partner, fear of judgment). Humans disclose (equally) intimate information to chatbots versus humans, at least according to their own perceptions. This is in line with the CASA paradigm ( Nass and Moon, 2000 ), stating that people can react in a social manner to computers the same way they do to humans. The underlying processes are not straightforward, nor comparable to the underlying mechanisms that play a role in self-disclosure to humans. We find that an important feature of human-chatbot communication is that humans experience less fear of judgment, compared to in interactions with another human. However, humans trust a chatbot less compared to a human interaction partner. Future research should further investigate if that is because the lack of moral agency, because of privacy concerns, or if there are other reasons.

Second, this study extends Derlega and Grzelak’s (1979) functional theory of self-disclosure and Omarzu’s (2000) disclosure decision model, which propose that self-disclosure is a strategic behavior people use to achieve personal goals. Specifically, the theory posits that the default goal most people have for self-disclosure, is social approval: people want to be liked by others. As a result, the content of people’s disclosures is generally socially acceptable and approved by the recipient ( Omarzu, 2000 ). The theory has been criticized for not accounting for underlying mechanisms that may account for the activation of those personal goals. The present research not only tests this theory in a unique, confessional setting, where other goals besides social approval are likely salient (e.g. relief of distress), but also shows that perceived anonymity may play an important role in explaining why people self-disclose in this particular setting. Specifically, previous research shows that when people feel anonymous, this reduces identifiability or accountability concerns ( Scott, 1998 ) and results in feelings of disinhibition ( Clark-Gordon et al., 2019 ). In line with previous research and the findings of the current study, the functional theory of self-disclosure can be extended to include perceived anonymity as an underlying mechanism in the activation of (intimate) self-disclosure.

This study also has implications for practice, in particular regarding the effectiveness of social chatbots in improving well-being. This study showed some first potential for using chatbot applications in improving mental well-being, which could potentially facilitate the mental healthcare sector, which currently deals with understaffing, long waiting lists and increasing costs. This also partly explains the popularity of social chatbots like Woebot and Wysa, which can help people who are anxious and/or depressed. The results of our study shed some light on the potential of these chatbots as a solution to shortages in the mental healthcare sector, as this study indicated that people are willing to disclose intimate information to the chatbot, which is a first requirement for successful therapy. Also, another important plus is that people experience less fear of judgment with the chatbot, which is important when sharing intimate topics, or topics people feel ashamed of. These aspects, combined with other advantages, such as 24/7 availability, low costs, show some potential for implementing such interventions in healthcare. However, to actually implement a successful chatbot intervention more requirements should be met, amongst which empathy is crucial. An empathetic response of the therapist can enhance the patient’s well-being. Our findings showed that there was no moderating effect of empathy on the self-disclosure—relief effect, but the perceived empathy was still the highest in the human condition, which is in line with Meng and Dai (2021) . Future research should develop and test chatbots that are able to respond in an empathic and adequate manner.

10.2. Limitations and suggestions for future research

Although our study has shed light on some first steps in investigating people’s willingness to disclose intimate information to a social chatbot, we recognize some limitations. First, the contrast between our conditions (talking to chatbot vs. human via text-based CMC) might not be large enough to find clear differences in the underlying mechanisms for eliciting self-disclosure. For example, we did not find a difference in perceived anonymity between the conditions. It is known that communicating with another human via text-based CMC enhances perceptions of anonymity compared to face-to-face communication (see Clark-Gordon et al. , 2019 ). In order to be sure if the anonymity feature of chatbot communication does (not) exist, future research should compare face-to-face with chatbot communication.

Second, we measured the impact of self-disclosure on emotional state via relief. This was done by means of a confession task in the experiment. We thought that confessions are oftentimes secrets that can weigh heavily on the discloser’s shoulders, which enhance relief after confessing. However, we noticed that ample of the secrets shared were positive in nature, which usually does not evoke relief. Due to this focus on relief instead of also on other positive emotional effects, we cannot be conclusive about that part of our study. Future research should further investigate this in several regards. Not only should a broader measurement on emotional state be included, but research should also be done on the capability of the chatbot to respond in an appropriate empathetic manner.

Finally, it should be noted that the study was administered through a laptop. This is in contrast with most of the common social chatbot applications (e.g., Woebot, Tess, Wysa and Replika) that are predominantly developed for and accessed through a smartphone. While the effect of the medium used to access a chatbot is currently an understudied topic, some evidence seems to suggest that the impact of medium is potentially large on constructs such as user experience and behavioral intention (in favor of smartphones compared to other devices; Persons et al. , 2021 ). These results suggest that the levels of disclosure that were found in this study may be enhanced when a smartphone device is used.

The data underlying this article will be shared on reasonable request to the corresponding author. Requests to access the datasets should be directed to Emmelyn Croes, [email protected] .

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Questions asked in all conditions.

Good morning/good afternoon. I am a female priest, and I am on the other side of the [music festival name] site. My name is Maria. What is your name?

So, <name>, can you tell me where you are from?

Have you been to [music festival name] before?

How are you liking [music festival name] this year?

Which artists have you seen at [music festival name] this year?

Hey < name>, I enjoyed getting to know you better. But, of course, you came here to share a secret. Do you have a secret you’d like to share with me?

Can you like to tell more about how you feel about the secret?*.

Is there anything else you want to share about your secret?

Thank you for sharing your secret! That’s it. Glad you wanted to participate in our study. You will soon receive a questionnaire from one of the researchers.

* Note. This question was only asked in the ‘high perceived understanding’ conditions.

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Methodology

• Types of Variables in Research & Statistics | Examples

Types of Variables in Research & Statistics | Examples

Published on September 19, 2022 by Rebecca Bevans . Revised on June 21, 2023.

In statistical research , a variable is defined as an attribute of an object of study. Choosing which variables to measure is central to good experimental design .

If you want to test whether some plant species are more salt-tolerant than others, some key variables you might measure include the amount of salt you add to the water, the species of plants being studied, and variables related to plant health like growth and wilting .

You need to know which types of variables you are working with in order to choose appropriate statistical tests and interpret the results of your study.

You can usually identify the type of variable by asking two questions:

• What type of data does the variable contain?
• What part of the experiment does the variable represent?

Table of contents

Types of data: quantitative vs categorical variables, parts of the experiment: independent vs dependent variables, other common types of variables, other interesting articles, frequently asked questions about variables.

Data is a specific measurement of a variable – it is the value you record in your data sheet. Data is generally divided into two categories:

• Quantitative data represents amounts
• Categorical data represents groupings

A variable that contains quantitative data is a quantitative variable ; a variable that contains categorical data is a categorical variable . Each of these types of variables can be broken down into further types.

Quantitative variables

When you collect quantitative data, the numbers you record represent real amounts that can be added, subtracted, divided, etc. There are two types of quantitative variables: discrete and continuous .

Categorical variables

Categorical variables represent groupings of some kind. They are sometimes recorded as numbers, but the numbers represent categories rather than actual amounts of things.

There are three types of categorical variables: binary , nominal , and ordinal variables .

*Note that sometimes a variable can work as more than one type! An ordinal variable can also be used as a quantitative variable if the scale is numeric and doesn’t need to be kept as discrete integers. For example, star ratings on product reviews are ordinal (1 to 5 stars), but the average star rating is quantitative.

Example data sheet

To keep track of your salt-tolerance experiment, you make a data sheet where you record information about the variables in the experiment, like salt addition and plant health.

To gather information about plant responses over time, you can fill out the same data sheet every few days until the end of the experiment. This example sheet is color-coded according to the type of variable: nominal , continuous , ordinal , and binary .

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Experiments are usually designed to find out what effect one variable has on another – in our example, the effect of salt addition on plant growth.

You manipulate the independent variable (the one you think might be the cause ) and then measure the dependent variable (the one you think might be the effect ) to find out what this effect might be.

You will probably also have variables that you hold constant ( control variables ) in order to focus on your experimental treatment.

In this experiment, we have one independent and three dependent variables.

The other variables in the sheet can’t be classified as independent or dependent, but they do contain data that you will need in order to interpret your dependent and independent variables.

What about correlational research?

When you do correlational research , the terms “dependent” and “independent” don’t apply, because you are not trying to establish a cause and effect relationship ( causation ).

However, there might be cases where one variable clearly precedes the other (for example, rainfall leads to mud, rather than the other way around). In these cases you may call the preceding variable (i.e., the rainfall) the predictor variable and the following variable (i.e. the mud) the outcome variable .

Once you have defined your independent and dependent variables and determined whether they are categorical or quantitative, you will be able to choose the correct statistical test .

But there are many other ways of describing variables that help with interpreting your results. Some useful types of variables are listed below.

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

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

Research bias

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

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 .

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.

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 .

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).

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Role of vitamin d in oral lichen planus: a case control study.

1. Introduction

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García-Pola, M.; Rodríguez-Fonseca, L. Role of Vitamin D in Oral Lichen Planus: A Case Control Study. Nutrients 2024 , 16 , 2761. https://doi.org/10.3390/nu16162761

García-Pola M, Rodríguez-Fonseca L. Role of Vitamin D in Oral Lichen Planus: A Case Control Study. Nutrients . 2024; 16(16):2761. https://doi.org/10.3390/nu16162761

García-Pola, María, and Lucía Rodríguez-Fonseca. 2024. "Role of Vitamin D in Oral Lichen Planus: A Case Control Study" Nutrients 16, no. 16: 2761. https://doi.org/10.3390/nu16162761

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