The Different Types Of Variables Used In Research And Statistics

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The Different Types of Variables Used in Research and Statistics

In today’s data-driven world, scientists and statisticians regularly conduct experiments to uncover relationships and trends. Scientists utilize these experiments to establish cause-and-effect links, while statisticians employ variables to symbolize the diverse and often unpredictable data encountered in their investigations.

Choosing the right variables is crucial for the success of any experiment, as it leads to clearer analyses and more reliable outcomes.

What Is a Variable?

A variable represents a characteristic, number, or quantity that can be controlled, manipulated, or measured during research or experiments. These variables can denote specific items, individuals, locations, or concepts and are often referred to as data items.

The selection of variables will depend on the intended outcome of the study. Every scientific experiment and statistical analysis will involve the examination of at least one variable.

Variables are termed as such because their values can fluctuate. Whether due to changes among the studied groups or shifts over time, a variable’s value may not remain constant throughout a single experiment.

Designing Experiments

As previously noted, all scientific experiments and statistical analyses revolve around controlling, manipulating, or measuring variables. Typically, these experiments are structured to explore the effects one variable has on another, establishing cause and effect.

When crafting an experiment, selecting the appropriate variables is essential. An incorrect choice can distort results and compromise the integrity of the study. Conversely, careful selection can enhance the smooth operation of the experiment and yield more accurate findings.

It’s important not only to identify the specific variables involved but also to classify their types. Understanding the type of variable helps in interpreting the results effectively.

It’s worth mentioning that categorizing variables can be subjective. Scientists and statisticians may have some flexibility in how they classify their experimental variables.

To ascertain the variable type, one must consider the data the variable represents and its role within the context of the experiment.

Independent, Dependent, and Control Variables

Every experiment or study typically encompasses independent, dependent, and control variables.

Independent variables: These variables are manipulated within the experiment. Their value is independent of other variables, meaning that changes in these variables do not occur due to other variables in the study. They are often termed experimental or predictor variables and are considered the cause in the cause-and-effect relationship.
Dependent variables: In contrast, dependent variables depend on other variables and can be altered or influenced by them. They represent the effect in the cause-and-effect scenario and are sometimes called outcome variables. Researchers typically seek to determine what influences changes in these variables.
Control variables: Control variables remain constant throughout the experiment and do not directly influence the other variables being measured. While they are not the primary focus of the experiment, they are crucial for achieving accurate results and facilitating reproducibility.

Qualitative Versus Quantitative Variables

Every variable included in an experiment must be classified as either qualitative or quantitative.

Qualitative variables: Also known as categorical variables, these variables lack numerical value and serve as nominal labels. For instance, eye color is a qualitative variable, as it is represented by a color rather than a number. These variables primarily describe characteristics of the data set and can be further divided into ordinal or nominal categories.
Quantitative variables: These variables possess numerical values and represent measurable quantities. They answer the questions of “how many” or “how much” and can be further categorized into continuous or discrete variables. Examples include population size, height, GPA, and the number of pets owned.

30 Other Variable Types Used in Experiments

This is not an exhaustive list, as the variety of variable types is extensive. Below are several common and less common variable types found in scientific experiments and statistical studies, along with a brief overview of their functions.

Active variable: A variable that can be manipulated by the experimenters.
Antecedent variable: A variable that precedes both independent and dependent variables.
Attribute variable: Also known as a passive variable, this variable is not manipulated during the experiment.
Binary variable: This type has only two possible values, typically represented as zero or one.
Categorical variable: Variables that can be grouped into larger categories, such as shoe brands.
Composite variable: A variable that consists of two or more related variables.
Confounding variable: A variable that can distort results by affecting both independent and dependent variables.
Continuous variable: A variable that can take any value within a given range.
Covariate variable: A variable that may influence the dependent variable alongside the independent variable.
Criterion variable: Another term for the dependent variable.
Dichotomous variable: Another name for a binary variable, with only two values.
Discrete variable: A variable with a finite number of possible values.
Endogenous variable: A variable dependent on other variables, particularly in statistical studies.
Exogenous variable: A variable determined outside of the model that impacts other variables within it.
Explanatory variable: Frequently used to describe the independent variable in an experiment.
Grouping variable: A variable employed to categorize the data set into groups.
Interval variable: A variable that illustrates meaningful differences between values.
Intervening variable: A variable that elucidates the relationship between two other measured variables.
Manifest variable: A variable that can be directly observed or measured in an experiment.
Moderating variable: A variable that can influence the relationship between independent and dependent variables.
Nominal variable: Another term for categorical variables, which include multiple categories.
Observed variable: A variable being measured during the experiment.
Ordinal variable: A type of variable with a clear order of categories.
Polychotomous variable: Variables that can take on more than two categories or values.
Ranked variable: An ordinal variable where only the order of data points is known.
Ratio variable: Similar to interval variables but with a defined zero point.
Responding variable: Similar to dependent variables, these reflect the outcome of the experiment.
Scale variable: A variable with numeric values that can be ordered meaningfully.
Study variable: Any variable used in a study that has a defined cause-and-effect relationship.
Test variable: Another term for the dependent variable that signifies the experiment’s outcome.

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Chris Kolmar

Chris Kolmar is a co-founder of Zippia and the editor-in-chief of the Zippia career advice blog. He has hired over 50 people in his career, been hired five times, and wants to help you land your next job. His research has been featured on the New York Times, Thrillist, VOX, The Atlantic, and a host of local news. More recently, he's been quoted on USA Today, BusinessInsider, and CNBC.

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