Understanding Independent and Dependent Variables in Research

Dependent Variable The variable that depends on other factors that are measured. These variables are expected to change as a result of an experimental manipulation of the independent variable or variables. It is the presumed effect.

Independent Variable The variable that is stable and unaffected by the other variables you are trying to measure. It refers to the condition of an experiment that is systematically manipulated by the investigator. It is the presumed cause.

Cramer, Duncan and Dennis Howitt. The SAGE Dictionary of Statistics. London: SAGE, 2004; Penslar, Robin Levin and Joan P. Porter. Institutional Review Board Guidebook: Introduction. Washington, DC: United States Department of Health and Human Services, 2010; “What are Dependent and Independent Variables?” Graphic Tutorial.

When conducting research, especially in scientific experiments identifying and working with variables is crucial. The two most important types of variables are independent variables and dependent variables. Understanding the difference between independent and dependent variables is key for designing valid experiments and analyzing data accurately. This article will explain independent and dependent variables in detail with examples of how they work in research studies.

What Are Variables in Research?

In research, a variable is any factor or characteristic that can vary or change. For example age gender, temperature, test scores, drug dosage, and time of day are all variables. Researchers are interested in studying variables to understand the relationships between them.

The basics of how variables work in research studies are

Researchers intentionally change independent variables to test their effects on dependent variables.
Dependent variables are expected to change as a result of changes in independent variables.
Researchers measure or observe dependent variables and analyze the results statistically to test hypotheses about the relationships between variables.

Independent Variables

An independent variable is the variable deliberately changed or manipulated by researchers to test the effect on other variables. It is the presumed “cause” in experiments. Independent variables are also called:

Explanatory variables – they explain outcomes
Predictor variables – they predict dependent variable values
Right-hand side variables – in regression equations

There are two types of independent variables:

Experimental Independent Variables

These are directly manipulated by the researcher. For example, a researcher might give different drug dosages to groups of patients to test the effects on cholesterol levels. Dosage is the independent variable changed directly.

Experiments test two or more “levels” of an independent variable to study its effects. Just testing a high and low level can show if the independent variable has an impact. Using multiple levels provides more detail on the relationships between variables.

Subject Variables

These are pre-existing characteristics of subjects that are used to split participants into groups. Examples are gender, ethnicity, age, income, etc. Subject variables cannot be manipulated or assigned randomly since they are inherent qualities of participants. Studying pre-existing groups is “quasi-experimental” and risks research biases.

Dependent Variables

A dependent variable changes in response to independent variable manipulations. It is the “effect” variable that depends on the independent variable. Dependent variables are also called:

Response variables – they respond to other variable changes
Outcome variables – they represent outcomes measured
Left-hand side variables – in regression equations

Researchers measure changes in dependent variables resulting from independent variable manipulation. Statistical analysis of the measurements allows testing of hypotheses about the relationships between the variables.

Identifying Independent vs. Dependent Variables

Here are some tips for identifying independent and dependent variables:

Independent Variables:

Are manipulated or controlled by the researcher
Are presumed causes of effects
Come before dependent variables in time order

Dependent Variables:

Change in response to independent variables
Are the outcomes or effects studied
Are measured after changes to independent variables

Examples of Independent and Dependent Variables

Here are some examples of independent and dependent variables in research studies:

A medical trial tests a new drug. The drug dosage given to patients is the independent variable. Patient health outcomes are the dependent variables.
An education study compares teaching methods. Teaching method is the independent variable. Student test scores are the dependent variable.
A psychology experiment studies the impact of room temperature on cognitive test performance. Room temperature is the independent variable controlled by researchers. Scores on the cognitive tests are the dependent variable.
A research project investigates differences in income based on education level. Education level is the independent variable. Income is the dependent variable expected to differ depending on education level.

Why Identifying Variables is Important

Identifying the independent and dependent variables accurately is crucial for valid research. Mixing up which factors are independent vs. dependent leads to faulty research design, inaccurate conclusions, and wasted time and resources.

Independent variables must be identified correctly because these are the presumed “cause” factors that researchers manipulate. Researchers risk manipulating the wrong factors or not identifying the real causes of effects if independent variables are misidentified.

Dependent variables must also be properly identified because these are the “effects” that are measured. Incorrect identification means researchers fail to measure and analyze the true outcomes of the independent variable changes as intended.

Relationships Between Independent and Dependent Variables

There are several ways independent and dependent variables can relate in research:

Correlation – There is an association between variables but this does not prove causation. Correlations can be positive or negative.
Positive correlation – As one variable increases, the other also increases. As one decreases, the other also decreases.
Negative correlation – As one variable increases, the other decreases. The variables change in opposite directions.
No correlation – There is no relationship between the variables. When one variable changes, the other does not change predictably.
Causation – Manipulation of the independent variable directly causes changes in the dependent variable. This is the ideal relationship researchers want to establish through experiments.

Proving definitive causation between variables is difficult. Confounding variables and limitations of research designs can undermine claims of causation. Correlational studies do not prove cause-and-effect.

Analyzing Data on Independent and Dependent Variables

Researchers use statistical analysis to analyze the data measuring changes in dependent variables resulting from manipulating independent variables:

Descriptive statistics – Describe data on variables with statistics like means, medians, standard deviations, frequencies, etc.
Inferential statistics – Use tests to generalize findings from a sample to a population. Common tests are t-tests, ANOVA, regression analysis, etc.
Statistical significance – Results are statistically significant if they are unlikely to occur by chance. Allows generalizing results to the population.
Effect size – Measures the strength or magnitude of relationships between variables. Allows assessing the practical significance of results.

Examples of Analyzing Variable Relationships

A study on tutoring examines differences in test scores between a group receiving tutoring versus a control group. An independent samples t-test can determine if the mean score difference is statistically significant.
Researchers investigate effects of class size on student achievement. Regression analysis allows assessing how much test scores change based on class size differences, while controlling other factors.
An experiment looks at memory retention after consuming caffeine. ANOVA tests if memory test performance differs significantly across the caffeine dosage groups.
A drug trial collects cholesterol measurements before and after treatment. A paired samples t-test can determine if cholesterol decreased significantly pre-post treatment.

Key Takeaways About Research Variables

Independent and dependent variables form the foundation of scientific research studies.
Independent variables are the presumed causes researchers manipulate.
Dependent variables are the effects hypothesized to result from independent variables.
Researchers measure changes in dependent variables and use statistics to analyze the relationships.
Identifying variables correctly is crucial for valid research design and accurate conclusions.

independent dependent variables research studies

Identifying Dependent and Independent Variables

Dont feel bad if you are confused about what is the dependent variable and what is the independent variable in social and behavioral sciences research. However, its important that you learn the difference because framing a study using these variables is a common approach to organizing the elements of a social sciences research study in order to discover relevant and meaningful results. Specifically, it is important for these two reasons:

You need to understand and be able to evaluate their application in other peoples research.
You need to apply them correctly in your own research.

A variable in research simply refers to a person, place, thing, or phenomenon that you are trying to measure in some way. The best way to understand the difference between a dependent and independent variable is that the meaning of each is implied by what the words tell us about the variable you are using. You can do this with a simple exercise from the website, Graphic Tutorial. Take the sentence, “The [independent variable] causes a change in [dependent variable] and it is not possible that [dependent variable] could cause a change in [independent variable].” Insert the names of variables you are using in the sentence in the way that makes the most sense. This will help you identify each type of variable. If youre still not sure, consult with your professor before you begin to write.

Fan, Shihe. “Independent Variable.” In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; “What are Dependent and Independent Variables?” Graphic Tutorial; Salkind, Neil J. “Dependent Variable.” In Encyclopedia of Research Design, Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349;

Independent,Dependent, and Control Variables

What is a dependent variable in research?

In research, a variable is any characteristic, number, or quantity that can be measured or counted in experimental investigations. One is called the dependent variable, and the other is the independent variable. In research, the independent variable is manipulated to observe its effect, while the dependent variable is the measured outcome.

What are independent and dependent variables in experiments?

Here are several examples of independent and dependent variables in experiments: In a study to determine whether how long a student sleeps affects test scores, the independent variable is the length of time spent sleeping while the dependent variable is the test score. You want to know which brand of fertilizer is best for your plants.

How do you distinguish between independent and dependent variables?

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

What is an independent variable in psychology?

In psychology, the independent variable is the variable the experimenter manipulates or changes and is assumed to directly affect the dependent variable. It’s considered the cause or factor that drives change, allowing psychologists to observe how it influences behavior, emotions, or other dependent variables in an experimental setting.