When researchers manipulate a variable in a study the study is typically referred to as a(n)

Operationalizing variables

Before you start your experiment, you need to have a clear definition of, and strategy for, how each variable will be measured and recorded. This process is called variable operationalization.

Inhaltsverzeichnis Show

Operationalizing variables
What is an independent variable?
Types of independent variables
Experimental variables
Subject variables
What is a dependent variable?
Identifying independent vs. dependent variables
Recognizing independent variables
Recognizing dependent variables
Independent and dependent variables in research
Visualizing independent and dependent variables
Frequently asked questions about independent and dependent variables

For example, you are interested in studying attitudes towards food, visual attention, and food choice. In your first study, your objective is to investigate “the effect of personal health goals on visual attention to different food groups”.

The first part of your objective, “the effect of personal health goals…”, contains an independent variable. To operationalize it you need to ask yourself the following questions: What is a personal health goal? Can you quantify and measure it? Can you break it down into distinct categories? How will you collect and record its value? Due to the nature of this term, your variable will most likely be composed of two or more categories (e.g. lose weight, keep weight, gain weight etc.) and its value recorded by means of a questionnaire or interview. Notice that in this example, the independent variable is also an intrinsic attribute of a participant and thus particular to that individual, as a result, this variable can also be classified as a participant variable.

The next part of your study objective “…on visual attention to different food groups”, contains your dependent variable. Once again, you will need to ask yourself the following questions: What is visual attention? How do I measure it? How is food grouped? How will we represent the different groups in the stimuli? Which grouping strategy is relevant to my objective? The term “visual attention to” can be operationalized into one or more relevant visual behavior measures that can be quantified and measured continuously. For example, eye tracking metrics like fixation duration, fixation count, and dwell time can provide you with information about the visual engagement and bias towards different items in your stimulus. The “… different food groups” term will most likely be operationalized as food types aggregated into categories (e.g. vegetables, red meat, dairy) and displayed on an image stimulus. Since you are manipulating the content of the stimulus and the manipulation affects the context in which the behavior occurs as well as the viewing behavior itself, your stimulus categories will be part of your set of independent variables and simultaneously a stimulus variable.

Figure 3. Shows one possible outcome of the operationalization of the different variables in the example study. The objective of this study is to find out what effect personal health goals have on a person's visual attention to different food groups.

In the example above, the participant’s 'current hunger state' is a factor that may impact visual behavior and attention bias. If a test participant ate their meal a long time ago and feels hungry, their attention may become focused on high calorie food groups, even though they might normally not look to those food items when they're satisfied. If you decide to ignore it, it becomes an extraneous variable in your experiment and may impact the relationship between your independent and dependent variable. On the other hand, if you decide to address it and control for it, you will also have to operationalize it, e.g. you can measure it as the elapsed time since the last meal. If you then ask all your study participants to eat a meal within 1 hour before your test, this variable becomes a controlled variable, as you try to standardize the level of hunger across your independent variable groups.

Published on February 3, 2022 by Pritha Bhandari. Revised on May 4, 2022.

In research, variables are any characteristics that can take on different values, such as height, age, temperature, or test scores.

Researchers often manipulate or measure independent and dependent variables in studies to test cause-and-effect relationships.

The independent variable is the cause. Its value is independent of other variables in your study.
The dependent variable is the effect. Its value depends on changes in the independent variable.

Example: Independent and dependent variablesYou design a study to test whether changes in room temperature have an effect on math test scores.

Your independent variable is the temperature of the room. You vary the room temperature by making it cooler for half the participants, and warmer for the other half.

Your dependent variable is math test scores. You measure the math skills of all participants using a standardized test and check whether they differ based on room temperature.

What is an independent variable?

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

Independent variables are also called:

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

These terms are especially used in statistics, where you estimate the extent to which an independent variable change can explain or predict changes in the dependent variable.

Types of independent variables

There are two main types of independent variables.

Experimental independent variables can be directly manipulated by researchers.
Subject variables cannot be manipulated by researchers, but they can be used to group research subjects categorically.

Experimental variables

In experiments, you manipulate independent variables directly to see how they affect your dependent variable. The independent variable is usually applied at different levels to see how the outcomes differ.

You can apply just two levels in order to find out if an independent variable has an effect at all.

You can also apply multiple levels to find out how the independent variable affects the dependent variable.

Example: Independent variable levelsYou are studying the impact of a new medication on the blood pressure of patients with hypertension. Your independent variable is the treatment that you directly vary between groups.

You have three independent variable levels, and each group gets a different level of treatment.

You randomly assign your patients to one of the three groups:

A low-dose experimental group
A high-dose experimental group
A placebo group

A true experiment requires you to randomly assign different levels of an independent variable to your participants.

Random assignment helps you control participant characteristics, so that they don’t affect your experimental results. This helps you to have confidence that your dependent variable results come solely from the independent variable manipulation.

Subject variables

Subject variables are characteristics that vary across participants, and they can’t be manipulated by researchers. For example, gender identity, ethnicity, race, income, and education are all important subject variables that social researchers treat as independent variables.

It’s not possible to randomly assign these to participants, since these are characteristics of already existing groups. Instead, you can create a research design where you compare the outcomes of groups of participants with characteristics. This is a quasi-experimental design because there’s no random assignment.

Example: Quasi-experimental designYou study whether gender identity affects neural responses to infant cries.

Your independent variable is a subject variable, namely the gender identity of the participants. You have three groups: men, women and other.

Your dependent variable is the brain activity response to hearing infant cries. You record brain activity with fMRI scans when participants hear infant cries without their awareness.

After collecting data, you check for statistically significant differences between the groups. You find some and conclude that gender identity influences brain responses to infant cries.

What is a dependent variable?

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

In statistics, dependent variables are also called:

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

The dependent variable is what you record after you’ve manipulated the independent variable. You use this measurement data to check whether and to what extent your independent variable influences the dependent variable by conducting statistical analyses.

Based on your findings, you can estimate the degree to which your independent variable variation drives changes in your dependent variable. You can also predict how much your dependent variable will change as a result of variation in the independent variable.

Identifying independent vs. dependent variables

Distinguishing between independent and dependent variables can be tricky when designing a complex study or reading an academic paper.

A dependent variable from one study can be the independent variable in another study, so it’s important to pay attention to research design.

Here are some tips for identifying each variable type.

Recognizing independent variables

Use this list of questions to check whether you’re dealing with an independent variable:

Is the variable manipulated, controlled, or used as a subject grouping method by the researcher?
Does this variable come before the other variable in time?
Is the researcher trying to understand whether or how this variable affects another variable?

Recognizing dependent variables

Check whether you’re dealing with a dependent variable:

Is this variable measured as an outcome of the study?
Is this variable dependent on another variable in the study?
Does this variable get measured only after other variables are altered?

Independent and dependent variables in research

Independent and dependent variables are generally used in experimental and quasi-experimental research.

Here are some examples of research questions and corresponding independent and dependent variables.

Research question	Independent variable	Dependent variable(s)
Do tomatoes grow fastest under fluorescent, incandescent, or natural light?	Type of light the tomato plant is grown under	The rate of growth of the tomato plant
What is the effect of intermittent fasting on blood sugar levels?	Presence or absence of intermittent fasting	Blood sugar levels
Is medical marijuana effective for pain reduction in people with chronic pain?	Presence or absence of medical marijuana use	Frequency of pain Intensity of pain
To what extent does remote working increase job satisfaction?	Type of work environment (remote or in office)	Job satisfaction self-reports

For experimental data, you analyze your results by generating descriptive statistics and visualizing your findings. Then, you select an appropriate statistical test to test your hypothesis.

The type of test is determined by:

your variable types
level of measurement
number of independent variable levels.

You’ll often use t tests or ANOVAs to analyze your data and answer your research questions.

Visualizing independent and dependent variables

In quantitative research, it’s good practice to use charts or graphs to visualize the results of studies. Generally, the independent variable goes on the x-axis (horizontal) and the dependent variable on the y-axis (vertical).

The type of visualization you use depends on the variable types in your research questions:

A bar chart is ideal when you have a categorical independent variable.
A scatter plot or line graph is best when your independent and dependent variables are both quantitative.

Example: Results visualizationYou collect data on blood pressure before and after treatment for all participants over a period of 2 months.

To inspect your data, you place your independent variable of treatment level on the x-axis and the dependent variable of blood pressure on the y-axis.

You plot bars for each treatment group before and after the treatment to show the difference in blood pressure.

Based on your results, you note that the placebo and low-dose groups show little difference in blood pressure, while the high-dose group sees substantial improvements.

Frequently asked questions about independent and dependent variables

What’s the definition of an independent variable?

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

Independent variables are also called:

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

What’s the definition of a dependent variable?

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

In statistics, dependent variables are also called:

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

Can I include more than one independent or dependent variable in a study?

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

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

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

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