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Variables in Research – Definition, Types and Examples

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Variables in Research

Definition:

In Research, Variables refer to characteristics or attributes that can be measured, manipulated, or controlled. They are the factors that researchers observe or manipulate to understand the relationship between them and the outcomes of interest.

Types of Variables in Research

Types of Variables in Research are as follows:

Independent Variable

This is the variable that is manipulated by the researcher. It is also known as the predictor variable, as it is used to predict changes in the dependent variable. Examples of independent variables include age, gender, dosage, and treatment type.

Dependent Variable

This is the variable that is measured or observed to determine the effects of the independent variable. It is also known as the outcome variable, as it is the variable that is affected by the independent variable. Examples of dependent variables include blood pressure, test scores, and reaction time.

Confounding Variable

This is a variable that can affect the relationship between the independent variable and the dependent variable. It is a variable that is not being studied but could impact the results of the study. For example, in a study on the effects of a new drug on a disease, a confounding variable could be the patient’s age, as older patients may have more severe symptoms.

Mediating Variable

This is a variable that explains the relationship between the independent variable and the dependent variable. It is a variable that comes in between the independent and dependent variables and is affected by the independent variable, which then affects the dependent variable. For example, in a study on the relationship between exercise and weight loss, the mediating variable could be metabolism, as exercise can increase metabolism, which can then lead to weight loss.

Moderator Variable

This is a variable that affects the strength or direction of the relationship between the independent variable and the dependent variable. It is a variable that influences the effect of the independent variable on the dependent variable. For example, in a study on the effects of caffeine on cognitive performance, the moderator variable could be age, as older adults may be more sensitive to the effects of caffeine than younger adults.

Control Variable

This is a variable that is held constant or controlled by the researcher to ensure that it does not affect the relationship between the independent variable and the dependent variable. Control variables are important to ensure that any observed effects are due to the independent variable and not to other factors. For example, in a study on the effects of a new teaching method on student performance, the control variables could include class size, teacher experience, and student demographics.

Continuous Variable

This is a variable that can take on any value within a certain range. Continuous variables can be measured on a scale and are often used in statistical analyses. Examples of continuous variables include height, weight, and temperature.

Categorical Variable

This is a variable that can take on a limited number of values or categories. Categorical variables can be nominal or ordinal. Nominal variables have no inherent order, while ordinal variables have a natural order. Examples of categorical variables include gender, race, and educational level.

Discrete Variable

This is a variable that can only take on specific values. Discrete variables are often used in counting or frequency analyses. Examples of discrete variables include the number of siblings a person has, the number of times a person exercises in a week, and the number of students in a classroom.

Dummy Variable

This is a variable that takes on only two values, typically 0 and 1, and is used to represent categorical variables in statistical analyses. Dummy variables are often used when a categorical variable cannot be used directly in an analysis. For example, in a study on the effects of gender on income, a dummy variable could be created, with 0 representing female and 1 representing male.

Extraneous Variable

This is a variable that has no relationship with the independent or dependent variable but can affect the outcome of the study. Extraneous variables can lead to erroneous conclusions and can be controlled through random assignment or statistical techniques.

Latent Variable

This is a variable that cannot be directly observed or measured, but is inferred from other variables. Latent variables are often used in psychological or social research to represent constructs such as personality traits, attitudes, or beliefs.

Moderator-mediator Variable

This is a variable that acts both as a moderator and a mediator. It can moderate the relationship between the independent and dependent variables and also mediate the relationship between the independent and dependent variables. Moderator-mediator variables are often used in complex statistical analyses.

Variables Analysis Methods

There are different methods to analyze variables in research, including:

• Descriptive statistics: This involves analyzing and summarizing data using measures such as mean, median, mode, range, standard deviation, and frequency distribution. Descriptive statistics are useful for understanding the basic characteristics of a data set.
• Inferential statistics : This involves making inferences about a population based on sample data. Inferential statistics use techniques such as hypothesis testing, confidence intervals, and regression analysis to draw conclusions from data.
• Correlation analysis: This involves examining the relationship between two or more variables. Correlation analysis can determine the strength and direction of the relationship between variables, and can be used to make predictions about future outcomes.
• Regression analysis: This involves examining the relationship between an independent variable and a dependent variable. Regression analysis can be used to predict the value of the dependent variable based on the value of the independent variable, and can also determine the significance of the relationship between the two variables.
• Factor analysis: This involves identifying patterns and relationships among a large number of variables. Factor analysis can be used to reduce the complexity of a data set and identify underlying factors or dimensions.
• Cluster analysis: This involves grouping data into clusters based on similarities between variables. Cluster analysis can be used to identify patterns or segments within a data set, and can be useful for market segmentation or customer profiling.
• Multivariate analysis : This involves analyzing multiple variables simultaneously. Multivariate analysis can be used to understand complex relationships between variables, and can be useful in fields such as social science, finance, and marketing.

Examples of Variables

• Age : This is a continuous variable that represents the age of an individual in years.
• Gender : This is a categorical variable that represents the biological sex of an individual and can take on values such as male and female.
• Education level: This is a categorical variable that represents the level of education completed by an individual and can take on values such as high school, college, and graduate school.
• Income : This is a continuous variable that represents the amount of money earned by an individual in a year.
• Weight : This is a continuous variable that represents the weight of an individual in kilograms or pounds.
• Ethnicity : This is a categorical variable that represents the ethnic background of an individual and can take on values such as Hispanic, African American, and Asian.
• Time spent on social media : This is a continuous variable that represents the amount of time an individual spends on social media in minutes or hours per day.
• Marital status: This is a categorical variable that represents the marital status of an individual and can take on values such as married, divorced, and single.
• Blood pressure : This is a continuous variable that represents the force of blood against the walls of arteries in millimeters of mercury.
• Job satisfaction : This is a continuous variable that represents an individual’s level of satisfaction with their job and can be measured using a Likert scale.

Applications of Variables

Variables are used in many different applications across various fields. Here are some examples:

• Scientific research: Variables are used in scientific research to understand the relationships between different factors and to make predictions about future outcomes. For example, scientists may study the effects of different variables on plant growth or the impact of environmental factors on animal behavior.
• Business and marketing: Variables are used in business and marketing to understand customer behavior and to make decisions about product development and marketing strategies. For example, businesses may study variables such as consumer preferences, spending habits, and market trends to identify opportunities for growth.
• Healthcare : Variables are used in healthcare to monitor patient health and to make treatment decisions. For example, doctors may use variables such as blood pressure, heart rate, and cholesterol levels to diagnose and treat cardiovascular disease.
• Education : Variables are used in education to measure student performance and to evaluate the effectiveness of teaching strategies. For example, teachers may use variables such as test scores, attendance, and class participation to assess student learning.
• Social sciences : Variables are used in social sciences to study human behavior and to understand the factors that influence social interactions. For example, sociologists may study variables such as income, education level, and family structure to examine patterns of social inequality.

Purpose of Variables

Variables serve several purposes in research, including:

• To provide a way of measuring and quantifying concepts: Variables help researchers measure and quantify abstract concepts such as attitudes, behaviors, and perceptions. By assigning numerical values to these concepts, researchers can analyze and compare data to draw meaningful conclusions.
• To help explain relationships between different factors: Variables help researchers identify and explain relationships between different factors. By analyzing how changes in one variable affect another variable, researchers can gain insight into the complex interplay between different factors.
• To make predictions about future outcomes : Variables help researchers make predictions about future outcomes based on past observations. By analyzing patterns and relationships between different variables, researchers can make informed predictions about how different factors may affect future outcomes.
• To test hypotheses: Variables help researchers test hypotheses and theories. By collecting and analyzing data on different variables, researchers can test whether their predictions are accurate and whether their hypotheses are supported by the evidence.

Characteristics of Variables

Characteristics of Variables are as follows:

• Measurement : Variables can be measured using different scales, such as nominal, ordinal, interval, or ratio scales. The scale used to measure a variable can affect the type of statistical analysis that can be applied.
• Range : Variables have a range of values that they can take on. The range can be finite, such as the number of students in a class, or infinite, such as the range of possible values for a continuous variable like temperature.
• Variability : Variables can have different levels of variability, which refers to the degree to which the values of the variable differ from each other. Highly variable variables have a wide range of values, while low variability variables have values that are more similar to each other.
• Validity and reliability : Variables should be both valid and reliable to ensure accurate and consistent measurement. Validity refers to the extent to which a variable measures what it is intended to measure, while reliability refers to the consistency of the measurement over time.
• Directionality: Some variables have directionality, meaning that the relationship between the variables is not symmetrical. For example, in a study of the relationship between smoking and lung cancer, smoking is the independent variable and lung cancer is the dependent variable.

Advantages of Variables

Here are some of the advantages of using variables in research:

• Control : Variables allow researchers to control the effects of external factors that could influence the outcome of the study. By manipulating and controlling variables, researchers can isolate the effects of specific factors and measure their impact on the outcome.
• Replicability : Variables make it possible for other researchers to replicate the study and test its findings. By defining and measuring variables consistently, other researchers can conduct similar studies to validate the original findings.
• Accuracy : Variables make it possible to measure phenomena accurately and objectively. By defining and measuring variables precisely, researchers can reduce bias and increase the accuracy of their findings.
• Generalizability : Variables allow researchers to generalize their findings to larger populations. By selecting variables that are representative of the population, researchers can draw conclusions that are applicable to a broader range of individuals.
• Clarity : Variables help researchers to communicate their findings more clearly and effectively. By defining and categorizing variables, researchers can organize and present their findings in a way that is easily understandable to others.

Disadvantages of Variables

Here are some of the main disadvantages of using variables in research:

• Simplification : Variables may oversimplify the complexity of real-world phenomena. By breaking down a phenomenon into variables, researchers may lose important information and context, which can affect the accuracy and generalizability of their findings.
• Measurement error : Variables rely on accurate and precise measurement, and measurement error can affect the reliability and validity of research findings. The use of subjective or poorly defined variables can also introduce measurement error into the study.
• Confounding variables : Confounding variables are factors that are not measured but that affect the relationship between the variables of interest. If confounding variables are not accounted for, they can distort or obscure the relationship between the variables of interest.
• Limited scope: Variables are defined by the researcher, and the scope of the study is therefore limited by the researcher’s choice of variables. This can lead to a narrow focus that overlooks important aspects of the phenomenon being studied.
• Ethical concerns: The selection and measurement of variables may raise ethical concerns, especially in studies involving human subjects. For example, using variables that are related to sensitive topics, such as race or sexuality, may raise concerns about privacy and discrimination.

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Types of Variables – A Comprehensive Guide

Published by Carmen Troy at August 14th, 2021 , Revised On October 26, 2023

A variable is any qualitative or quantitative characteristic that can change and have more than one value, such as age, height, weight, gender, etc.

Before conducting research, it’s essential to know what needs to be measured or analysed and choose a suitable statistical test to present your study’s findings. 

In most cases, you can do it by identifying the key issues/variables related to your research’s main topic.

Example:  If you want to test whether the hybridisation of plants harms the health of people. You can use the key variables like agricultural techniques, type of soil, environmental factors, types of pesticides used, the process of hybridisation, type of yield obtained after hybridisation, type of yield without hybridisation, etc.

Variables are broadly categorised into:

• Independent variables
• Dependent variable
• Control variable

Independent Vs. Dependent Vs. Control Variable

The research includes finding ways:

• To change the independent variables.
• To prevent the controlled variables from changing.
• To measure the dependent variables.

Note:  The term dependent and independent is not applicable in  correlational research  as this is not a  controlled experiment.  A researcher doesn’t have control over the variables. The association and between two or more variables are measured. If one variable affects another one, then it’s called the predictor variable and outcome variable.

Example:  Correlation between investment (predictor variable) and profit (outcome variable)

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Types of Variables Based on the Types of Data

A data is referred to as the information and statistics gathered for analysis of a research topic. Data is broadly divided into two categories, such as:

Quantitative/Numerical data  is associated with the aspects of measurement, quantity, and extent. 

Categorial data  is associated with groupings.

A qualitative variable consists of qualitative data, and a quantitative variable consists of a quantitative variable.

Quantitative Variable

The quantitative variable is associated with measurement, quantity, and extent, like how many . It follows the statistical, mathematical, and computational techniques in numerical data such as percentages and statistics. The research is conducted on a large group of population.

Example:  Find out the weight of students of the fifth standard studying in government schools.

The quantitative variable can be further categorised into continuous and discrete.

Categorial Variable

The categorical variable includes measurements that vary in categories such as names but not in terms of rank or degree. It means one level of a categorical variable cannot be considered better or greater than another level. 

Example: Gender, brands, colors, zip codes

The categorical variable is further categorised into three types:

Note:  Sometimes, an ordinal variable also acts as a quantitative variable. Ordinal data has an order, but the intervals between scale points may be uneven.

Example: Numbers on a rating scale represent the reviews’ rank or range from below average to above average. However, it also represents a quantitative variable showing how many stars and how much rating is given.

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Other Types of Variables

It’s important to understand the difference between dependent and independent variables and know whether they are quantitative or categorical to choose the appropriate statistical test.

There are many other types of variables to help you differentiate and understand them.

Also, read a comprehensive guide written about inductive and deductive reasoning .

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Frequently Asked Questions

What are the 10 types of variables in research.

The 10 types of variables in research are:

• Independent
• Confounding
• Categorical
• Extraneous.

What is an independent variable?

An independent variable, often termed the predictor or explanatory variable, is the variable manipulated or categorized in an experiment to observe its effect on another variable, called the dependent variable. It’s the presumed cause in a cause-and-effect relationship, determining if changes in it produce changes in the observed outcome.

What is a variable?

In research, a variable is any attribute, quantity, or characteristic that can be measured or counted. It can take on various values, making it “variable.” Variables can be classified as independent (manipulated), dependent (observed outcome), or control (kept constant). They form the foundation for hypotheses, observations, and data analysis in studies.

What is a dependent variable?

A dependent variable is the outcome or response being studied in an experiment or investigation. It’s what researchers measure to determine the effect of changes in the independent variable. In a cause-and-effect relationship, the dependent variable is presumed to be influenced or caused by the independent variable.

What is a variable in programming?

In programming, a variable is a symbolic name for a storage location that holds data or values. It allows data storage and retrieval for computational operations. Variables have types, like integer or string, determining the nature of data they can hold. They’re fundamental in manipulating and processing information in software.

What is a control variable?

A control variable in research is a factor that’s kept constant to ensure that it doesn’t influence the outcome. By controlling these variables, researchers can isolate the effects of the independent variable on the dependent variable, ensuring that other factors don’t skew the results or introduce bias into the experiment.

What is a controlled variable in science?

In science, a controlled variable is a factor that remains constant throughout an experiment. It ensures that any observed changes in the dependent variable are solely due to the independent variable, not other factors. By keeping controlled variables consistent, researchers can maintain experiment validity and accurately assess cause-and-effect relationships.

How many independent variables should an investigation have?

Ideally, an investigation should have one independent variable to clearly establish cause-and-effect relationships. Manipulating multiple independent variables simultaneously can complicate data interpretation.

However, in advanced research, experiments with multiple independent variables (factorial designs) are used, but they require careful planning to understand interactions between variables.

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This post provides the key disadvantages of secondary research so you know the limitations of secondary research before making a decision.

A confounding variable can potentially affect both the suspected cause and the suspected effect. Here is all you need to know about accounting for confounding variables in research.

Descriptive research is carried out to describe current issues, programs, and provides information about the issue through surveys and various fact-finding methods.

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Variables in Research | Types, Definiton & Examples

Introduction

What is a variable, what are the 5 types of variables in research, other variables in research.

Variables are fundamental components of research that allow for the measurement and analysis of data. They can be defined as characteristics or properties that can take on different values. In research design , understanding the types of variables and their roles is crucial for developing hypotheses , designing methods , and interpreting results .

This article outlines the the types of variables in research, including their definitions and examples, to provide a clear understanding of their use and significance in research studies. By categorizing variables into distinct groups based on their roles in research, their types of data, and their relationships with other variables, researchers can more effectively structure their studies and achieve more accurate conclusions.

A variable represents any characteristic, number, or quantity that can be measured or quantified. The term encompasses anything that can vary or change, ranging from simple concepts like age and height to more complex ones like satisfaction levels or economic status. Variables are essential in research as they are the foundational elements that researchers manipulate, measure, or control to gain insights into relationships, causes, and effects within their studies. They enable the framing of research questions, the formulation of hypotheses, and the interpretation of results.

Variables can be categorized based on their role in the study (such as independent and dependent variables ), the type of data they represent (quantitative or categorical), and their relationship to other variables (like confounding or control variables). Understanding what constitutes a variable and the various variable types available is a critical step in designing robust and meaningful research.

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Variables are crucial components in research, serving as the foundation for data collection , analysis , and interpretation . They are attributes or characteristics that can vary among subjects or over time, and understanding their types is essential for any study. Variables can be broadly classified into five main types, each with its distinct characteristics and roles within research.

This classification helps researchers in designing their studies, choosing appropriate measurement techniques, and analyzing their results accurately. The five types of variables include independent variables, dependent variables, categorical variables, continuous variables, and confounding variables. These categories not only facilitate a clearer understanding of the data but also guide the formulation of hypotheses and research methodologies.

Independent variables

Independent variables are foundational to the structure of research, serving as the factors or conditions that researchers manipulate or vary to observe their effects on dependent variables. These variables are considered "independent" because their variation does not depend on other variables within the study. Instead, they are the cause or stimulus that directly influences the outcomes being measured. For example, in an experiment to assess the effectiveness of a new teaching method on student performance, the teaching method applied (traditional vs. innovative) would be the independent variable.

The selection of an independent variable is a critical step in research design, as it directly correlates with the study's objective to determine causality or association. Researchers must clearly define and control these variables to ensure that observed changes in the dependent variable can be attributed to variations in the independent variable, thereby affirming the reliability of the results. In experimental research, the independent variable is what differentiates the control group from the experimental group, thereby setting the stage for meaningful comparison and analysis.

Dependent variables

Dependent variables are the outcomes or effects that researchers aim to explore and understand in their studies. These variables are called "dependent" because their values depend on the changes or variations of the independent variables.

Essentially, they are the responses or results that are measured to assess the impact of the independent variable's manipulation. For instance, in a study investigating the effect of exercise on weight loss, the amount of weight lost would be considered the dependent variable, as it depends on the exercise regimen (the independent variable).

The identification and measurement of the dependent variable are crucial for testing the hypothesis and drawing conclusions from the research. It allows researchers to quantify the effect of the independent variable , providing evidence for causal relationships or associations. In experimental settings, the dependent variable is what is being tested and measured across different groups or conditions, enabling researchers to assess the efficacy or impact of the independent variable's variation.

To ensure accuracy and reliability, the dependent variable must be defined clearly and measured consistently across all participants or observations. This consistency helps in reducing measurement errors and increases the validity of the research findings. By carefully analyzing the dependent variables, researchers can derive meaningful insights from their studies, contributing to the broader knowledge in their field.

Categorical variables

Categorical variables, also known as qualitative variables, represent types or categories that are used to group observations. These variables divide data into distinct groups or categories that lack a numerical value but hold significant meaning in research. Examples of categorical variables include gender (male, female, other), type of vehicle (car, truck, motorcycle), or marital status (single, married, divorced). These categories help researchers organize data into groups for comparison and analysis.

Categorical variables can be further classified into two subtypes: nominal and ordinal. Nominal variables are categories without any inherent order or ranking among them, such as blood type or ethnicity. Ordinal variables, on the other hand, imply a sort of ranking or order among the categories, like levels of satisfaction (high, medium, low) or education level (high school, bachelor's, master's, doctorate).

Understanding and identifying categorical variables is crucial in research as it influences the choice of statistical analysis methods. Since these variables represent categories without numerical significance, researchers employ specific statistical tests designed for a nominal or ordinal variable to draw meaningful conclusions. Properly classifying and analyzing categorical variables allow for the exploration of relationships between different groups within the study, shedding light on patterns and trends that might not be evident with numerical data alone.

Continuous variables

Continuous variables are quantitative variables that can take an infinite number of values within a given range. These variables are measured along a continuum and can represent very precise measurements. Examples of continuous variables include height, weight, temperature, and time. Because they can assume any value within a range, continuous variables allow for detailed analysis and a high degree of accuracy in research findings.

The ability to measure continuous variables at very fine scales makes them invaluable for many types of research, particularly in the natural and social sciences. For instance, in a study examining the effect of temperature on plant growth, temperature would be considered a continuous variable since it can vary across a wide spectrum and be measured to several decimal places.

When dealing with continuous variables, researchers often use methods incorporating a particular statistical test to accommodate a wide range of data points and the potential for infinite divisibility. This includes various forms of regression analysis, correlation, and other techniques suited for modeling and analyzing nuanced relationships between variables. The precision of continuous variables enhances the researcher's ability to detect patterns, trends, and causal relationships within the data, contributing to more robust and detailed conclusions.

Confounding variables

Confounding variables are those that can cause a false association between the independent and dependent variables, potentially leading to incorrect conclusions about the relationship being studied. These are extraneous variables that were not considered in the study design but can influence both the supposed cause and effect, creating a misleading correlation.

Identifying and controlling for a confounding variable is crucial in research to ensure the validity of the findings. This can be achieved through various methods, including randomization, stratification, and statistical control. Randomization helps to evenly distribute confounding variables across study groups, reducing their potential impact. Stratification involves analyzing the data within strata or layers that share common characteristics of the confounder. Statistical control allows researchers to adjust for the effects of confounders in the analysis phase.

Properly addressing confounding variables strengthens the credibility of research outcomes by clarifying the direct relationship between the dependent and independent variables, thus providing more accurate and reliable results.

Beyond the primary categories of variables commonly discussed in research methodology , there exists a diverse range of other variables that play significant roles in the design and analysis of studies. Below is an overview of some of these variables, highlighting their definitions and roles within research studies:

• Discrete variables : A discrete variable is a quantitative variable that represents quantitative data , such as the number of children in a family or the number of cars in a parking lot. Discrete variables can only take on specific values.
• Categorical variables : A categorical variable categorizes subjects or items into groups that do not have a natural numerical order. Categorical data includes nominal variables, like country of origin, and ordinal variables, such as education level.
• Predictor variables : Often used in statistical models, a predictor variable is used to forecast or predict the outcomes of other variables, not necessarily with a causal implication.
• Outcome variables : These variables represent the results or outcomes that researchers aim to explain or predict through their studies. An outcome variable is central to understanding the effects of predictor variables.
• Latent variables : Not directly observable, latent variables are inferred from other, directly measured variables. Examples include psychological constructs like intelligence or socioeconomic status.
• Composite variables : Created by combining multiple variables, composite variables can measure a concept more reliably or simplify the analysis. An example would be a composite happiness index derived from several survey questions .
• Preceding variables : These variables come before other variables in time or sequence, potentially influencing subsequent outcomes. A preceding variable is crucial in longitudinal studies to determine causality or sequences of events.

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Definitions

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.

Identifying Dependent and Independent Variables

Don't 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, it's 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 people's 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 you're 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;

Structure and Writing Style

The process of examining a research problem in the social and behavioral sciences is often framed around methods of analysis that compare, contrast, correlate, average, or integrate relationships between or among variables . Techniques include associations, sampling, random selection, and blind selection. Designation of the dependent and independent variable involves unpacking the research problem in a way that identifies a general cause and effect and classifying these variables as either independent or dependent.

The variables should be outlined in the introduction of your paper and explained in more detail in the methods section . There are no rules about the structure and style for writing about independent or dependent variables but, as with any academic writing, clarity and being succinct is most important.

After you have described the research problem and its significance in relation to prior research, explain why you have chosen to examine the problem using a method of analysis that investigates the relationships between or among independent and dependent variables . State what it is about the research problem that lends itself to this type of analysis. For example, if you are investigating the relationship between corporate environmental sustainability efforts [the independent variable] and dependent variables associated with measuring employee satisfaction at work using a survey instrument, you would first identify each variable and then provide background information about the variables. What is meant by "environmental sustainability"? Are you looking at a particular company [e.g., General Motors] or are you investigating an industry [e.g., the meat packing industry]? Why is employee satisfaction in the workplace important? How does a company make their employees aware of sustainability efforts and why would a company even care that its employees know about these efforts?

Identify each variable for the reader and define each . In the introduction, this information can be presented in a paragraph or two when you describe how you are going to study the research problem. In the methods section, you build on the literature review of prior studies about the research problem to describe in detail background about each variable, breaking each down for measurement and analysis. For example, what activities do you examine that reflect a company's commitment to environmental sustainability? Levels of employee satisfaction can be measured by a survey that asks about things like volunteerism or a desire to stay at the company for a long time.

The structure and writing style of describing the variables and their application to analyzing the research problem should be stated and unpacked in such a way that the reader obtains a clear understanding of the relationships between the variables and why they are important. This is also important so that the study can be replicated in the future using the same variables but applied in a different way.

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; “Case Example for Independent and Dependent Variables.” ORI Curriculum Examples. U.S. Department of Health and Human Services, Office of Research Integrity; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349; “Independent Variables and Dependent Variables.” Karl L. Wuensch, Department of Psychology, East Carolina University [posted email exchange]; “Variables.” Elements of Research. Dr. Camille Nebeker, San Diego State University.

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Independent and Dependent Variables

This guide discusses how to identify independent and dependent variables effectively and incorporate their description within the body of a research paper.

A variable can be anything you might aim to measure in your study, whether in the form of numerical data or reflecting complex phenomena such as feelings or reactions. Dependent variables change due to the other factors measured, especially if a study employs an experimental or semi-experimental design. Independent variables are stable: they are both presumed causes and conditions in the environment or milieu being manipulated.

Identifying Independent and Dependent Variables

Even though the definitions of the terms independent and dependent variables may appear to be clear, in the process of analyzing data resulting from actual research, identifying the variables properly might be challenging. Here is a simple rule that you can apply at all times: the independent variable is what a researcher changes, whereas the dependent variable is affected by these changes. To illustrate the difference, a number of examples are provided below.

The purpose of Study 1 is to measure the impact of different plant fertilizers on how many fruits apple trees bear.

Plant fertilizers (chosen by researchers)

Fruits that the trees bear (affected by choice of fertilizers)

The purpose of Study 2 is to find an association between living in close vicinity to hydraulic fracturing sites and respiratory diseases.

Proximity to hydraulic fracturing sites (a presumed cause and a condition of the environment)

The percentage/ likelihood of suffering from respiratory diseases

Confusion is possible in identifying independent and dependent variables in the social sciences. When considering psychological phenomena and human behavior, it can be difficult to distinguish between cause and effect. For example, the purpose of Study 3 is to establish how tactics for coping with stress are linked to the level of stress-resilience in college students. Even though it is feasible to speculate that these variables are interdependent, the following factors should be taken into account in order to clearly define which variable is dependent and which is interdependent.

• The dependent variable is usually the objective of the research. In the study under examination, the levels of stress resilience are being investigated.
• The independent variable precedes the dependent variable. The chosen stress-related coping techniques help to build resilience; thus, they occur earlier.

Writing Style and Structure

Usually, the variables are first described in the introduction of a research paper and then in the method section. No strict guidelines for approaching the subject exist; however, academic writing demands that the researcher make clear and concise statements. It is only reasonable not to leave readers guessing which of the variables is dependent and which is independent. The description should reflect the literature review, where both types of variables are identified in the context of the previous research. For instance, in the case of Study 3, a researcher would have to provide an explanation as to the meaning of stress resilience and coping tactics.

In properly organizing a research paper, it is essential to outline and operationalize the appropriate independent and dependent variables. Moreover, the paper should differentiate clearly between independent and dependent variables. Finding the dependent variable is typically the objective of a study, whereas independent variables reflect influencing factors that can be manipulated. Distinguishing between the two types of variables in social sciences may be somewhat challenging as it can be easy to confuse cause with effect. Academic format calls for the author to mention the variables in the introduction and then provide a detailed description in the method section.

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27 Types of Variables in Research and Statistics

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In research and statistics, a variable is a characteristic or attribute that can take on different values or categories. It represents data points or information that can be measured, observed, or manipulated within a study.

Statistical and experimental analysis aims to explore the relationships between variables. For example, researchers may hypothesize a connection between a particular variable and an outcome, like the association between physical activity levels (an independent variable) and heart health (a dependent variable).

Variables play a crucial role in data analysis . Data sets collected through research typically consist of multiple variables, and the analysis is driven by how these variables are related, how they influence each other, and what patterns emerge from these relationships.

Therefore, as a researcher, your understanding of variables and their manipulation forms the crux of your study.

To help with your understanding, I’ve presented 27 of the most common types of variables below.

Types of Variables

1. quantitative (numerical) variables.

Definition: Quantitative variables, also known as numerical variables, are quantifiable in nature and represented in numbers, allowing the data collected to be measured on a scale or range (Moodie & Johnson, 2021). These variables generally yield data that can be organized, ranked, measured, and subjected to mathematical operations.

Explanation: The values of quantitative variables can either be counted (referred to as discrete variables) or measured (continuous variables). Quantifying data in numerical form allows for a range of statistical analysis techniques to be applied, from calculating averages to finding correlations.

Quantitative Variable Example : Consider a marketing survey where you ask respondents to rate their satisfaction with your product on a scale of 1 to 10. The satisfaction score here represents a quantitative variable. The data can be quantified and used to calculate average satisfaction scores, identify the scope for product improvement, or compare satisfaction levels across different demographic groups.

2. Continuous Variables

Definition: Continuous variables are a subtype of quantitative variables that can have an infinite number of measurements within a specified range. They provide detailed insights based on precise measurements and are often representative on a continuous scale (Christmann & Badgett, 2009).

Explanation: The variable is “continuous” because there are an infinite number of possible values within the chosen range. For instance, variables like height, weight, or time are measured continuously.

Continuous Variable Example : The best real-world example of a continuous variable is time. For instance, the time it takes for a customer service representative to resolve a customer issue can range anywhere from few seconds to several hours, and can accurately be measured down to the second, providing an almost finite set of possible values.

3. Discrete Variables

Definition: Discrete variables are a form of quantitative variable that can only assume a finite number of values. They are typically count-based (Frankfort-Nachmias & Leon-Guerrero, 2006).

Explanation: Discrete variables are commonly used in situations where the “count” or “quantity” is distinctly separate. For instance, the number of children in a family is a common example – you can’t have 2.5 kids.

Discrete Variable Example : The number of times a customer contacts customer service within a month. This is a discrete variable because it can only take a whole number of values – you can’t call customer service 2.5 times.

4. Qualitative (Categorical) Variables

Definition: Qualitative, or categorical variables, are non-numerical data points that categorize or group data entities based on shared features or qualities (Moodie & Johnson, 2021).

Explanation: They are often used in research to classify particular traits, characteristics, or properties of subjects that are not easily quantifiable, such as colors, textures, tastes, or smells.

Qualitative Variable Example : Consider a survey that asks respondents to identify their favorite color from a list of choices. The color preference would be a qualitative variable as it categorizes data into different categories corresponding to different colors.

5. Nominal Variables

Definition: Nominal variables, a subtype of qualitative variables, represent categories without any inherent order or ranking (Norman & Streiner, 2008).

Explanation: Nominal variables are often used to label or categorize particular sets of items or individuals, with no intention of giving numerical value or order. For example, race, gender, or religion.

Nominal Variable Example : For instance, the type of car someone owns (sedan, SUV, truck, etc.) is a nominal variable. Each category is unique and one is not inherently higher, better, or larger than the others.

6. Ordinal Variables

Definition: Ordinal variables are a subtype of categorical (qualitative) variables with a key feature of having a clear, distinct, and meaningful order or ranking to the categories (De Vaus, 2001).

Explanation: Ordinal variables represent categories that can be logically arranged in a specific order or sequence but the difference between categories is unknown or doesn’t matter, such as satisfaction rating scale (unsatisfied, neutral, satisfied).

Ordinal Variable Example : A classic example is asking survey respondents how strongly they agree or disagree with a statement (strongly disagree, disagree, neither agree nor disagree, agree, strongly agree). The answers form an ordinal scale; they can be ranked, but the intervals between responses are not necessarily equal.

7. Dichotomous (Binary) Variables

Definition: Dichotomous or binary variables are a type of categorical variable that consist of only two opposing categories like true/false, yes/no, success/failure, and so on (Adams & McGuire, 2022).

Explanation: Dichotomous variables refer to situations where there can only be two, and just two, possible outcomes – there is no middle ground.

Dichotomous Variable Example : Whether a customer completed a transaction (Yes or No) is a binary variable. Either they completed the purchase (yes) or they did not (no).

8. Ratio Variables

Definition: Ratio variables are the highest level of quantitative variables that contain a zero point or absolute zero, which represents a complete absence of the quantity (Norman & Streiner, 2008).

Explanation: Besides being able to categorize and order units, ratio variables also allow for the relative degree of difference between them to be calculated. For example, income, height, weight, and temperature (in Kelvin) are ratio variables.

Ratio Variable Example : An individual’s annual income is a ratio variable. You can say someone earning $50,000 earns twice as much as someone making $25,000. The zero point in this case would be an income of $0, which indicates that no income is being earned.

9. Interval Variables

Definition: Interval variables are quantitative variables that have equal, predictable differences between values, but they do not have a true zero point (Norman & Streiner, 2008).

Explanation: Interval variables are similar to ratio variables; both provide a clear ordering of categories and have equal intervals between successive values. The primary difference is the absence of an absolute zero.

Interval Variable Example : The classic example of an interval variable is the temperature in Fahrenheit or Celsius. The difference between 20 degrees and 30 degrees is the same as the difference between 70 degrees and 80 degrees, but there isn’t a true zero because the scale doesn’t start from absolute nonexistence of the quantity being measured.

Related: Quantitative Reasoning Examples

10. Dependent Variables

Definition: The dependent variable is the outcome or effect that the researcher wants to study. Its value depends on or is influenced by one or more other variables known as independent variables.

Explanation: In a research study, the dependent variable is the phenomenon or behavior that may be affected by manipulations in the independent variable. It’s what you measure to see if your predictions about the effects of the independent variable are correct.

Dependent Variable Example: Suppose you want to study the impact of exercise frequency on weight loss. In this case, the dependent variable is weight loss, which changes based on how often the subject exercises (the independent variable).

11. Independent Variables

Definition: The independent variable, or the predictor variable, is what the researcher manipulates to test its effect on the dependent variable.

Explanation: The independent variable is presumed to have some effect on the dependent variable in a study. It can often be thought of as the cause in a cause-and-effect relationship.

Independent Variable Example: In a study looking at how different dosages of a medication affect the severity of symptoms, the medication dosage is an independent variable. Researchers will adjust the dosage to see what effect it has on the symptoms (the dependent variable).

See Also: Independent and Dependent Variable Examples

12. Confounding Variables

Definition: Confounding variables—also known as confounders—are variables that might distort, confuse or interfere with the relationship between an independent variable and a dependent variable, leading to a false correlation (Boniface, 2019).

Explanation: Confounders are typically related in some way to both the independent and dependent variables. Because of this, they can create or hide relationships, leading researchers to make inaccurate conclusions about causality.

Confounding Variable Example : If you’re studying the relationship between physical activity and heart health, diet could potentially act as a confounding variable. People who are physically active often also eat healthier diets, which could independently improve heart health [National Heart, Lung, and Blood Institute].

13. Control Variables

Definition: Control variables are variables in a research study that the researcher keeps constant to prevent them from interfering with the relationship between the independent and dependent variables (Sproull, 2002).

Explanation: Control variables allow researchers to isolate the effects of the independent variable on the dependent variable, ensuring that any changes observed are solely due to the manipulation of the independent variable and not an external factor.

Control Variable Example : In a study evaluating the impact of a tutoring program on student performance, some control variables could include the teacher’s experience, the type of test used to measure performance, and the student’s previous grades.

14. Latent Variables

Definition: Latent variables—also referred to as hidden or unobserved variables—are variables that are not directly observed or measured but are inferred from other variables that are observed (measured directly).

Explanation: Latent variables can represent abstract concepts like intelligence, socioeconomic status, or even happiness. They are often used in psychological and sociological research, where certain concepts can’t be measured directly.

Latent Variable Example: In a study on job satisfaction, factors like job stress, financial reward, work-life balance, or relationship with colleagues can be measured directly. However, “job satisfaction” itself is a latent variable as it is inferred from these observed variables.

15. Derived Variables

Definition: Derived variables are variables that are created or developed based on existing variables in a dataset. They involve applying certain calculations or manipulations to one or more variables to create a new one.

Explanation: Derived variables can be created by either transforming a single variable (like taking the square root) or combining multiple variables (computing the ratio of two variables).

Derived Variable Example: In a dataset containing a person’s height and weight, a derived variable could be the Body Mass Index (BMI). The BMI is calculated by dividing weight (in kilograms) by the square of height (in meters).

16. Time-series Variables

Definition: Time-series variables are a set of data points ordered or indexed in time order. They provide a sequence of data points, each associated with a specific instance in time.

Explanation: Time-series variables are often used in statistical models to study trends, analyze patterns over time, make forecasts, and understand underlying causes and characteristics of the trend.

Time-series Variable Example : The quarterly GDP (Gross Domestic Product) data over a period of several years would be an example of a time series variable. Economists use such data to examine economic trends over time.

17. Cross-sectional Variables

Definition: Cross-sectional variables are data collected from many subjects at the same point in time or without regard to differences in time.

Explanation: This type of data provides a “snapshot” of the variables at a specific time. They’re often used in research to compare different population groups at a single point in time.

Cross-sectional Variable Example: A basic example of a set of cross-sectional data could be a national survey that asks respondents about their current employment status. The data captured represents a single point in time and does not track changes in employment over time.

18. Predictor Variables

Definition: A predictor variable—also known as independent or explanatory variable—is a variable that is being manipulated in an experiment or study to see how it influences the dependent or response variable.

Explanation: In a cause-and-effect relationship, the predictor variable is the cause. Its modification allows the researcher to study its effect on the response variable.

Predictor Variable Example : In a study evaluating the impact of studying hours on exam score, the number of studying hours is a predictor variable. Researchers alter the study duration to see its impact on the exam results (response variable).

19. Response Variables

Definition: A response variable—also known as the dependent or outcome variable—is what the researcher observes for any changes in an experiment or study. Its value depends on the predictor or independent variable.

Explanation: The response variable is the “effect” in a cause-and-effect scenario. Any changes occurring to this variable due to the predictor variable are observed and recorded.

Response Variable Example: Continuing from the previous example, the exam score is the response variable. It changes based on the manipulation of the predictor variable, i.e., the number of studying hours.

20. Exogenous Variables

Definition: Exogenous variables are variables that are not affected by other variables in the system but can affect other variables within the same system.

Explanation: In a model, an exogenous variable is considered to be an input, it’s determined outside the model, and its value is simply imposed on the system.

Exogenous Variable Example: In an economic model, the government’s taxation rate may be considered an exogenous variable. The rate is set externally (not determined within the economic model) but impacts variables within the model, such as business profitability.

21. Endogenous Variables

Definition: In contrast, endogenous variables are variables whose value is determined by the functional relationships within the system in an economic or statistical model. They depend on the values of other variables in the model.

Explanation: These are the “output” variables of a system, determined through cause-and-effect relationships within the system.

Endogenous Variable Example: To continue the previous example, business profitability in an economic model may be considered an endogenous variable. It is influenced by several other variables within the model, including the exogenous taxation rate set by the government.

22. Causal Variables

Definition: Causal variables are variables which can directly cause an effect on the outcome or dependent variable. Their value or level determines the value or level of other variables.

Explanation: In a cause-and-effect relationship, a causal variable is the cause. The understanding of causal relationships is the basis of scientific enquiry, allowing researchers to manipulate variables to see the effect.

Causal Variable Example: In a study examining the effect of fertilizer on plant growth, the type or amount of fertilizer used is the causal variable. Changing its type or amount should directly affect the outcome—plant growth.

23. Moderator Variables

Definition: Moderator variables are variables that can affect the strength or direction of the association between the predictor (independent) and response (dependent) variable. They specify when or under what conditions a relationship holds.

Explanation: The role of a moderator is to illustrate “how” or “when” an independent variable’s effect on a dependent variable changes.

Moderator Variable Example: If you are studying the effect of a training program on job performance, a potential moderator variable could be the employee’s education level. The influence of the training program on job performance could depend on the employee’s initial level of education.

24. Mediator Variables

Definition: Mediator variables are variables that account for, or explain, the relationship between an independent variable and a dependent variable, providing an understanding of “why” or “how” an effect occurs.

Explanation: Often, the relationship between an independent and a dependent variable isn’t direct—it’s through a third, intervening, variable known as a mediator variable.

Mediator Variable Example: In a study looking at the relationship between socioeconomic status and academic performance, a mediator variable might be the access to educational resources. Socioeconomic status may influence access to educational resources, which in turn affects academic performance. The relationship between socioeconomic status and academic performance isn’t direct but through access to resources.

25. Extraneous Variables

Definition: Extraneous variables are variables that are not of primary interest to a researcher but might influence the outcome of a study. They can add “noise” to the research data if not controlled.

Explanation: An extraneous variable is anything else that has the potential to influence our dependent variable or confound our results if not kept in check, other than our independent variable.

Extraneous Variable Example : Consider an experiment to test whether temperature influences the rate of a chemical reaction. Potential extraneous variables could include the light level, humidity, or impurities in the chemicals used—each could affect the reaction rate and, thus, should be controlled to ensure valid results.

26. Dummy Variables

Definition: Dummy variables, often used in regression analysis, are artificial variables created to represent an attribute with two or more distinct categories or levels.

Explanation: They are used to turn a qualitative variable into a quantitative one to facilitate mathematical processing. Typically, dummy variables are binary – taking a value of either 0 or 1.

Dummy Variable Example: Consider a dataset that includes a variable “Gender” with categories “male” and “female”. A corresponding dummy variable “IsMale” could be introduced, where males get classified as 1 and females as 0.

27. Composite Variables

Definition: Composite variables are new variables created by combining or grouping two or more variables.

Explanation: Depending upon their complexity, composite variables can help assess concepts that are explicit (e.g., “total score”) or relatively abstract (e.g., “life quality index”).

Composite Variable Example: A “Healthy Living Index” might be created as a composite of multiple variables such as eating habits, physical activity level, sleep quality, and stress level. Each of these variables contributes to the overall “Healthy Living Index”.

Knowing your variables will make you a better researcher. Some you need to keep an eye out for: confounding variables , for instance, always need to be in the backs of our minds. Others you need to think about during study design, matching the research design to the research objectives.

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Allen, M. (2017). The SAGE Encyclopedia of Communication Research Methods (Vol. 1). New York: SAGE Publications.

Babbie, E., Halley, F., & Zaino, J. (2007).  Adventures in Social Research: Data Analysis Using SPSS 14.0 and 15.0 for Windows  (6th ed.). New York: SAGE Publications.

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

Christmann, E. P., & Badgett, J. L. (2009). Interpreting Assessment Data: Statistical Techniques You Can Use. New York: NSTA Press.

Coolidge, F. L. (2012). Statistics: A Gentle Introduction (3rd ed.). SAGE Publications.

Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . New York: SAGE Publications.

De Vaus, D. A. (2001). Research Design in Social Research . New York: SAGE Publications.

Katz, M. (2006) . Study Design and Statistical Analysis: A Practical Guide for Clinicians . Cambridge: Cambridge University Press.

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

Moodie, P. F., & Johnson, D. E. (2021). Applied Regression and ANOVA Using SAS. CRC Press.

Norman, G. R., & Streiner, D. L. (2008). Biostatistics: The Bare Essentials . New York: B.C. Decker.

Privitera, G. J. (2022). Research Methods for the Behavioral Sciences . New Jersey: SAGE Publications.

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Types of Variables in Psychology Research

Examples of Independent and Dependent Variables

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

 James Lacy, MLS, is a fact-checker and researcher.

Dependent and Independent Variables

• Intervening Variables
• Extraneous Variables
• Controlled Variables
• Confounding Variables
• Operationalizing Variables

Frequently Asked Questions

Variables in psychology are things that can be changed or altered, such as a characteristic or value. Variables are generally used in psychology experiments to determine if changes to one thing result in changes to another.

Variables in psychology play a critical role in the research process. By systematically changing some variables in an experiment and measuring what happens as a result, researchers are able to learn more about cause-and-effect relationships.

The two main types of variables in psychology are the independent variable and the dependent variable. Both variables are important in the process of collecting data about psychological phenomena.

This article discusses different types of variables that are used in psychology research. It also covers how to operationalize these variables when conducting experiments.

Students often report problems with identifying the independent and dependent variables in an experiment. While this task can become more difficult as the complexity of an experiment increases, in a psychology experiment:

• The independent variable is the variable that is manipulated by the experimenter. An example of an independent variable in psychology: In an experiment on the impact of sleep deprivation on test performance, sleep deprivation would be the independent variable. The experimenters would have some of the study participants be sleep-deprived while others would be fully rested.
• The dependent variable is the variable that is measured by the experimenter. In the previous example, the scores on the test performance measure would be the dependent variable.

So how do you differentiate between the independent and dependent variables? Start by asking yourself what the experimenter is manipulating. The things that change, either naturally or through direct manipulation from the experimenter, are generally the independent variables. What is being measured? The dependent variable is the one that the experimenter is measuring.

Intervening Variables in Psychology

Intervening variables, also sometimes called intermediate or mediator variables, are factors that play a role in the relationship between two other variables. In the previous example, sleep problems in university students are often influenced by factors such as stress. As a result, stress might be an intervening variable that plays a role in how much sleep people get, which may then influence how well they perform on exams.

Extraneous Variables in Psychology

Independent and dependent variables are not the only variables present in many experiments. In some cases, extraneous variables may also play a role. This type of variable is one that may have an impact on the relationship between the independent and dependent variables.

For example, in our previous example of an experiment on the effects of sleep deprivation on test performance, other factors such as age, gender, and academic background may have an impact on the results. In such cases, the experimenter will note the values of these extraneous variables so any impact can be controlled for.

There are two basic types of extraneous variables:

• Participant variables : These extraneous variables are related to the individual characteristics of each study participant that may impact how they respond. These factors can include background differences, mood, anxiety, intelligence, awareness, and other characteristics that are unique to each person.
• Situational variables : These extraneous variables are related to things in the environment that may impact how each participant responds. For example, if a participant is taking a test in a chilly room, the temperature would be considered an extraneous variable. Some participants may not be affected by the cold, but others might be distracted or annoyed by the temperature of the room.

Other extraneous variables include the following:

• Demand characteristics : Clues in the environment that suggest how a participant should behave
• Experimenter effects : When a researcher unintentionally suggests clues for how a participant should behave

Controlled Variables in Psychology

In many cases, extraneous variables are controlled for by the experimenter. A controlled variable is one that is held constant throughout an experiment.

In the case of participant variables, the experiment might select participants that are the same in background and temperament to ensure that these factors don't interfere with the results. Holding these variables constant is important for an experiment because it allows researchers to be sure that all other variables remain the same across all conditions.  

Using controlled variables means that when changes occur, the researchers can be sure that these changes are due to the manipulation of the independent variable and not caused by changes in other variables.

It is important to also note that a controlled variable is not the same thing as a control group . The control group in a study is the group of participants who do not receive the treatment or change in the independent variable.

All other variables between the control group and experimental group are held constant (i.e., they are controlled). The dependent variable being measured is then compared between the control group and experimental group to see what changes occurred because of the treatment.

Confounding Variables in Psychology

If a variable cannot be controlled for, it becomes what is known as a confounding variabl e. This type of variable can have an impact on the dependent variable, which can make it difficult to determine if the results are due to the influence of the independent variable, the confounding variable, or an interaction of the two.

Operationalizing Variables in Psychology

An operational definition describes how the variables are measured and defined in the study. Before conducting a psychology experiment , it is essential to create firm operational definitions for both the independent variable and dependent variables.

For example, in our imaginary experiment on the effects of sleep deprivation on test performance, we would need to create very specific operational definitions for our two variables. If our hypothesis is "Students who are sleep deprived will score significantly lower on a test," then we would have a few different concepts to define:

• Students : First, what do we mean by "students?" In our example, let’s define students as participants enrolled in an introductory university-level psychology course.
• Sleep deprivation : Next, we need to operationally define the "sleep deprivation" variable. In our example, let’s say that sleep deprivation refers to those participants who have had less than five hours of sleep the night before the test.
• Test variable : Finally, we need to create an operational definition for the test variable. For this example, the test variable will be defined as a student’s score on a chapter exam in the introductory psychology course.

Once all the variables are operationalized, we're ready to conduct the experiment.

Variables play an important part in psychology research. Manipulating an independent variable and measuring the dependent variable allows researchers to determine if there is a cause-and-effect relationship between them.

A Word From Verywell

Understanding the different types of variables used in psychology research is important if you want to conduct your own psychology experiments. It is also helpful for people who want to better understand what the results of psychology research really mean and become more informed consumers of psychology information .

Independent and dependent variables are used in experimental research. Unlike some other types of research (such as correlational studies ), experiments allow researchers to evaluate cause-and-effect relationships between two variables.

Researchers can use statistical analyses to determine the strength of a relationship between two variables in an experiment. Two of the most common ways to do this are to calculate a p-value or a correlation. The p-value indicates if the results are statistically significant while the correlation can indicate the strength of the relationship.

In an experiment on how sugar affects short-term memory, sugar intake would be the independent variable and scores on a short-term memory task would be the independent variable.

In an experiment looking at how caffeine intake affects test anxiety, the amount of caffeine consumed before a test would be the independent variable and scores on a test anxiety assessment would be the dependent variable.

Just as with other types of research, the independent variable in a cognitive psychology study would be the variable that the researchers manipulate. The specific independent variable would vary depending on the specific study, but it might be focused on some aspect of thinking, memory, attention, language, or decision-making.

American Psychological Association. Operational definition . APA Dictionary of Psychology.

American Psychological Association. Mediator . APA Dictionary of Psychology.

Altun I, Cınar N, Dede C. The contributing factors to poor sleep experiences in according to the university students: A cross-sectional study .  J Res Med Sci . 2012;17(6):557-561. PMID:23626634

Skelly AC, Dettori JR, Brodt ED. Assessing bias: The importance of considering confounding .  Evid Based Spine Care J . 2012;3(1):9-12. doi:10.1055/s-0031-1298595

• Evans, AN & Rooney, BJ. Methods in Psychological Research. Thousand Oaks, CA: SAGE Publications; 2014.
• Kantowitz, BH, Roediger, HL, & Elmes, DG. Experimental Psychology. Stamfort, CT: Cengage Learning; 2015.

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Types of variables and commonly used statistical designs.

Jacob Shreffler ; Martin R. Huecker .

Affiliations

Last Update: March 6, 2023 .

• Definition/Introduction

Suitable statistical design represents a critical factor in permitting inferences from any research or scientific study. [1]  Numerous statistical designs are implementable due to the advancement of software available for extensive data analysis. [1]  Healthcare providers must possess some statistical knowledge to interpret new studies and provide up-to-date patient care. We present an overview of the types of variables and commonly used designs to facilitate this understanding. [2]

• Issues of Concern

Individuals who attempt to conduct research and choose an inappropriate design could select a faulty test and make flawed conclusions. This decision could lead to work being rejected for publication or (worse) lead to erroneous clinical decision-making, resulting in unsafe practice. [1]  By understanding the types of variables and choosing tests that are appropriate to the data, individuals can draw appropriate conclusions and promote their work for an application. [3]

To determine which statistical design is appropriate for the data and research plan, one must first examine the scales of each measurement. [4]  Multiple types of variables determine the appropriate design.

Ordinal data (also sometimes referred to as discrete) provide ranks and thus levels of degree between the measurement. [5]  Likert items can serve as ordinal variables, but the Likert scale, the result of adding all the times, can be treated as a continuous variable. [6]  For example, on a 20-item scale with each item ranging from 1 to 5, the item itself can be an ordinal variable, whereas if you add up all items, it could result in a range from 20 to 100. A general guideline for determining if a variable is ordinal vs. continuous: if the variable has more than ten options, it can be treated as a continuous variable. [7]  The following examples are ordinal variables:

• Likert items
• Cancer stages
• Residency Year

Nominal, Categorical, Dichotomous, Binary

Other types of variables have interchangeable terms. Nominal and categorical variables describe samples in groups based on counts that fall within each category, have no quantitative relationships, and cannot be ranked. [8]  Examples of these variables include:

• Service (i.e., emergency, internal medicine, psychiatry, etc.)
• Mode of Arrival (ambulance, helicopter, car)

A dichotomous or a binary variable is in the same family as nominal/categorical, but this type has only two options. Binary logistic regression, which will be discussed below, has two options for the outcome of interest/analysis. Often used as (yes/no), examples of dichotomous or binary variables would be:

• Alive (yes vs. no)
• Insurance (yes vs. no)
• Readmitted (yes vs. no)

With this overview of the types of variables provided, we will present commonly used statistical designs for different scales of measurement. Importantly, before deciding on a statistical test, individuals should perform exploratory data analysis to ensure there are no issues with the data and consider type I, type II errors, and power analysis. Furthermore, investigators should ensure appropriate statistical assumptions. [9] [10]  For example, parametric tests, including some discussed below (t-tests, analysis of variance (ANOVA), correlation, and regression), require the data to have a normal distribution and that the variances within each group are similar. [6] [11]  After eliminating any issues based on exploratory data analysis and reducing the likelihood of committing type I and type II errors, a statistical test can be chosen. Below is a brief introduction to each of the commonly used statistical designs with examples of each type. An example of one research focus, with each type of statistical design discussed, can be found in Table 1 to provide more examples of commonly used statistical designs. 

Commonly Used Statistical Designs

Independent Samples T-test

An independent samples t-test allows a comparison of two groups of subjects on one (continuous) variable. Examples in biomedical research include comparing results of treatment vs. control group and comparing differences based on gender (male vs. female).

Example: Does adherence to the ketogenic diet (yes/no; two groups) have a differential effect on total sleep time (minutes; continuous)?

Paired T-test

A paired t-test analyzes one sample population, measuring the same variable on two different occasions; this is often useful for intervention and educational research.

Example :  Does participating in a research curriculum (one group with intervention) improve resident performance on a test to measure research competence (continuous)?

One-Way Analysis of Variance (ANOVA)

Analysis of variance (ANOVA), as an extension of the t-test, determines differences amongst more than two groups, or independent variables based on a dependent variable. [11]  ANOVA is preferable to conducting multiple t-tests as it reduces the likelihood of committing a type I error.

Example: Are there differences in length of stay in the hospital (continuous) based on the mode of arrival (car, ambulance, helicopter, three groups)?

Repeated Measures ANOVA

Another procedure commonly used if the data for individuals are recurrent (repeatedly measured) is a repeated-measures ANOVA. [1]  In these studies, multiple measurements of the dependent variable are collected from the study participants. [11]  A within-subjects repeated measures ANOVA determines effects based on the treatment variable alone, whereas mixed ANOVAs allow both between-group effects and within-subjects to be considered.

Within-Subjects Example: How does ketamine effect mean arterial pressure (continuous variable) over time (repeated measurement)?

Mixed Example: Does mean arterial pressure (continuous) differ between males and females (two groups; mixed) on ketamine throughout a surgical procedure (over time; repeated measurement)?  

Nonparametric Tests

Nonparametric tests, such as the Mann-Whitney U test (two groups; nonparametric t-test), Kruskal Wallis test (multiple groups; nonparametric ANOVA), Spearman’s rho (nonparametric correlation coefficient) can be used when data are ordinal or lack normality. [3] [5]  Not requiring normality means that these tests allow skewed data to be analyzed; they require the meeting of fewer assumptions. [11]

Example: Is there a relationship between insurance status (two groups) and cancer stage (ordinal)?  

A Chi-square test determines the effect of relationships between categorical variables, which determines frequencies and proportions into which these variables fall. [11]  Similar to other tests discussed, variants and extensions of the chi-square test (e.g., Fisher’s exact test, McNemar’s test) may be suitable depending on the variables. [8]

Example: Is there a relationship between individuals with methamphetamine in their system (yes vs. no; dichotomous) and gender (male or female; dichotomous)?

Correlation

Correlations (used interchangeably with ‘associations’) signal patterns in data between variables. [1]  A positive association occurs if values in one variable increase as values in another also increase. A negative association occurs if variables in one decrease while others increase. A correlation coefficient, expressed as r,  describes the strength of the relationship: a value of 0 means no relationship, and the relationship strengthens as r approaches 1 (positive relationship) or -1 (negative association). [5]

Example: Is there a relationship between age (continuous) and satisfaction with life survey scores (continuous)?

Linear Regression

Regression allows researchers to determine the degrees of relationships between a dependent variable and independent variables and results in an equation for prediction. [11]  A large number of variables are usable in regression methods.

Example: Which admission to the hospital metrics (multiple continuous) best predict the total length of stay (minutes; continuous)?

Binary Logistic Regression

This type of regression, which aims to predict an outcome, is appropriate when the dependent variable or outcome of interest is binary or dichotomous (yes/no; cured/not cured). [12]

Example: Which panel results (multiple of continuous, ordinal, categorical, dichotomous) best predict whether or not an individual will have a positive blood culture (dichotomous/binary)?

The table provides more examples of commonly used statistical designs by providing an example of one research focus and discussing each type of statistical design (see Table. Types of Variables and Statistical Designs).

• Clinical Significance

Though numerous other statistical designs and extensions of methods covered in this article exist, the above information provides a starting point for healthcare providers to become acquainted with variables and commonly used designs. Researchers should study types of variables before determining statistical tests to obtain relevant measures and valid study results. [6]  There is a recommendation to consult a statistician to ensure appropriate usage of the statistical design based on the variables and that the assumptions are upheld. [1]  With the variety of statistical software available, investigators must a priori understand the type of statistical tests when designing a study. [13]  All providers must interpret and scrutinize journal publications to make evidence-based clinical decisions, and this becomes enhanced by a limited but sound understanding of variables and commonly used study designs. [14]

• Nursing, Allied Health, and Interprofessional Team Interventions

All interprofessional healthcare team members need to be familiar with study design and the variables used in studies to accurately evaluate new data and studies as they are published and apply the latest data to patient care and drive optimal outcomes.

• Review Questions
• Access free multiple choice questions on this topic.
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Types of Variables and Statistical Designs.  Contributed by M Huecker, MD, and J Shreffler, PhD

Disclosure: Jacob Shreffler declares no relevant financial relationships with ineligible companies.

Disclosure: Martin Huecker declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Shreffler J, Huecker MR. Types of Variables and Commonly Used Statistical Designs. [Updated 2023 Mar 6]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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Educational Research Basics by Del Siegle

Each person/thing we collect data on is called an OBSERVATION (in our work these are usually people/subjects. Currently, the term participant rather than subject is used when describing the people from whom we collect data).

OBSERVATIONS (participants) possess a variety of CHARACTERISTICS .

If a CHARACTERISTIC of an OBSERVATION (participant) is the same for every member of the group (doesn’t vary) it is called a CONSTANT .

If a CHARACTERISTIC of an OBSERVATION (participant) differs for group members it is called a VARIABLE . In research we don’t get excited about CONSTANTS (since everyone is the same on that characteristic); we’re more interested in VARIABLES. Variables can be classified as QUANTITATIVE or QUALITATIVE (also known as CATEGORICAL).

QUANTITATIVE variables are ones that exist along a continuum that runs from low to high. Ordinal, interval, and ratio variables are quantitative.  QUANTITATIVE variables are sometimes called CONTINUOUS VARIABLES because they have a variety (continuum) of characteristics. Height in inches and scores on a test would be examples of quantitative variables.

QUALITATIVE variables do not express differences in amount, only differences. They are sometimes referred to as CATEGORICAL variables because they classify by categories. Nominal variables such as gender, religion, or eye color are CATEGORICAL variables. Generally speaking, categorical variables

A special case of a CATEGORICAL variable is a DICHOTOMOUS VARIABLE. DICHOTOMOUS variables have only two CHARACTERISTICS (male or female). When naming QUALITATIVE variables, it is important to name the category rather than the levels (i.e., gender is the variable name, not male and female).

Variables have different purposes or roles…

Independent (Experimental, Manipulated, Treatment, Grouping) Variable- That factor which is measured, manipulated, or selected by the experimenter to determine its relationship to an observed phenomenon. “In a research study, independent variables are antecedent conditions that are presumed to affect a dependent variable. They are either manipulated by the researcher or are observed by the researcher so that their values can be related to that of the dependent variable. For example, in a research study on the relationship between mosquitoes and mosquito bites, the number of mosquitoes per acre of ground would be an independent variable” (Jaeger, 1990, p. 373)

While the independent variable is often manipulated by the researcher, it can also be a classification where subjects are assigned to groups. In a study where one variable causes the other, the independent variable is the cause. In a study where groups are being compared, the independent variable is the group classification.

Dependent (Outcome) Variable- That factor which is observed and measured to determine the effect of the independent variable, i.e., that factor that appears, disappears, or varies as the experimenter introduces, removes, or varies the independent variable. “In a research study, the independent variable defines a principal focus of research interest. It is the consequent variable that is presumably affected by one or more independent variables that are either manipulated by the researcher or observed by the researcher and regarded as antecedent conditions that determine the value of the dependent variable. For example, in a study of the relationship between mosquitoes and mosquito bites, the number of mosquito bites per hour would be the dependent variable” (Jaeger, 1990, p. 370). The dependent variable is the participant’s response.

The dependent variable is the outcome. In an experiment, it may be what was caused or what changed as a result of the study. In a comparison of groups, it is what they differ on.

Moderator Variable- That factor which is measured, manipulated, or selected by the experimenter to discover whether it modifies the relationship of the independent variable to an observed phenomenon. It is a special type of independent variable.

The independent variable’s relationship with the dependent variable may change under different conditions. That condition is the moderator variable. In a study of two methods of teaching reading, one of the methods of teaching reading may work better with boys than girls. Method of teaching reading is the independent variable and reading achievement is the dependent variable. Gender is the moderator variable because it moderates or changes the relationship between the independent variable (teaching method) and the dependent variable (reading achievement).

Suppose we do a study of reading achievement where we compare whole language with phonics, and we also include students’ social economic status (SES) as a variable. The students are randomly assigned to either whole language instruction or phonics instruction. There are students of high and low SES in each group.

Let’s assume that we found that whole language instruction worked better than phonics instruction with the high SES students, but phonics instruction worked better than whole language instruction with the low SES students. Later you will learn in statistics that this is an interaction effect. In this study, language instruction was the independent variable (with two levels: phonics and whole language). SES was the moderator variable (with two levels: high and low). Reading achievement was the dependent variable (measured on a continuous scale so there aren’t levels).

With a moderator variable, we find the type of instruction did make a difference, but it worked differently for the two groups on the moderator variable. We select this moderator variable because we think it is a variable that will moderate the effect of the independent on the dependent. We make this decision before we start the study.

If the moderator had not been in the study above, we would have said that there was no difference in reading achievement between the two types of reading instruction. This would have happened because the average of the high and low scores of each SES group within a reading instruction group would cancel each other an produce what appears to be average reading achievement in each instruction group (i.e., Phonics: Low—6 and High—2; Whole Language:   Low—2 and High—6; Phonics has an average of 4 and Whole Language has an average of 4. If we just look at the averages (without regard to the moderator), it appears that the instruction types produced similar results).

Extraneous Variable- Those factors which cannot be controlled. Extraneous variables are independent variables that have not been controlled. They may or may not influence the results. One way to control an extraneous variable which might influence the results is to make it a constant (keep everyone in the study alike on that characteristic). If SES were thought to influence achievement, then restricting the study to one SES level would eliminate SES as an extraneous variable.

Here are some examples similar to your homework:

Null Hypothesis: Students who receive pizza coupons as a reward do not read more books than students who do not receive pizza coupon rewards. Independent Variable: Reward Status Dependent Variable: Number of Books Read

High achieving students do not perform better than low achieving student when writing stories regardless of whether they use paper and pencil or a word processor. Independent Variable: Instrument Used for Writing Moderator Variable: Ability Level of the Students Dependent Variable:  Quality of Stories Written When we are comparing two groups, the groups are the independent variable. When we are testing whether something influences something else, the influence (cause) is the independent variable. The independent variable is also the one we manipulate. For example, consider the hypothesis “Teachers given higher pay will have more positive attitudes toward children than teachers given lower pay.” One approach is to ask ourselves “Are there two or more groups being compared?” The answer is “Yes.” “What are the groups?” Teachers who are given higher pay and teachers who are given lower pay. Therefore, the independent variable is teacher pay (it has two levels– high pay and low pay). The dependent variable (what the groups differ on) is attitude towards school.

We could also approach this another way. “Is something causing something else?” The answer is “Yes.” “What is causing what?” Teacher pay is causing attitude towards school. Therefore, teacher pay is the independent variable (cause) and attitude towards school is the dependent variable (outcome).

Research Questions and Hypotheses

The research question drives the study. It should specifically state what is being investigated. Statisticians often convert their research questions to null and alternative hypotheses. The null hypothesis states that no relationship (correlation study) or difference (experimental study) exists. Converting research questions to hypotheses is a simple task. Take the questions and make it a positive statement that says a relationship exists (correlation studies) or a difference exists (experiment study) between the groups and we have the alternative hypothesis. Write a statement  that a relationship does not exist or a difference does not exist and we have the null hypothesis.

Format for sample research questions and accompanying hypotheses:

Research Question for Relationships: Is there a relationship between height and weight? Null Hypothesis:  There is no relationship between height and weight. Alternative Hypothesis:   There is a relationship between height and weight.

When a researcher states a nondirectional hypothesis in a study that compares the performance of two groups, she doesn’t state which group she believes will perform better. If the word “more” or “less” appears in the hypothesis, there is a good chance that we are reading a directional hypothesis. A directional hypothesis is one where the researcher states which group she believes will perform better.  Most researchers use nondirectional hypotheses.

We usually write the alternative hypothesis (what we believe might happen) before we write the null hypothesis (saying it won’t happen).

Directional Research Question for Differences: Do boys like reading more than girls? Null Hypothesis:   Boys do not like reading more than girls. Alternative Hypothesis:   Boys do like reading more than girls.

Nondirectional Research Question for Differences: Is there a difference between boys’ and girls’ attitude towards reading? –or– Do boys’ and girls’ attitude towards reading differ? Null Hypothesis:   There is no difference between boys’ and girls’ attitude towards reading.  –or–  Boys’ and girls’ attitude towards reading do not differ. Alternative Hypothesis:   There is a difference between boys’ and girls’ attitude towards reading.  –or–  Boys’ and girls’ attitude towards reading differ.

Del Siegle, Ph.D. Neag School of Education – University of Connecticut [email protected] www.delsiegle.com

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• Independent vs Dependent Variables | Definition & Examples

Independent vs Dependent Variables | Definition & Examples

Published on 4 May 2022 by Pritha Bhandari . Revised on 17 October 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.

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.

Table of contents

What is an independent variable, types of independent variables, what is a dependent variable, identifying independent vs dependent variables, independent and dependent variables in research, visualising independent and dependent variables, frequently asked questions about independent and dependent variables.

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.

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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.

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.

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.

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.

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.

Recognising 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?

Recognising 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 are generally used in experimental and quasi-experimental research.

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

For experimental data, you analyse your results by generating descriptive statistics and visualising 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 analyse your data and answer your research questions.

In quantitative research , it’s good practice to use charts or graphs to visualise 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 visualisation you use depends on the variable types in your research questions:

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

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.

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.

• Right-hand-side variables (they appear on the right-hand side of a regression equation)

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:

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

You want to find out how blood sugar levels are affected by drinking diet cola and regular cola, so you conduct an experiment .

• The type of cola – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of cola.

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.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both.

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Variables in Quantitative Research: A Beginner's Guide (COUN)

Quantitative variables.

Because quantitative methodology requires measurement, the concepts being investigated need to be defined in a way that can be measured. Organizational change, reading comprehension, emergency response, or depression are concepts, but they cannot be measured as such. Frequency of organizational change, reading comprehension scores, emergency response time, or types of depression can be measured. They are variables (concepts that can vary).

• Independent variables (IV).
• Dependent variables (DV).
• Sample variables.
• Extraneous variables.

Independent Variables (IV)

Independent variables (IV) are those that are suspected of being the cause in a causal relationship. If you are asking a cause and effect question, your IV will be the variable (or variables) that you suspect causes the effect.

There are two main sorts of IV—active independent variables and attribute independent variables:

• Active IV are interventions or conditions that are being applied to the participants. A special tutorial for the third graders, a new therapy for clients, or a new training program being tested on employees would be active IVs.
• Attribute IV are intrinsic characteristics of the participants that are suspected of causing a result. For example, if you are examining whether gender which is intrinsic to the participants results in higher or lower scores on some skill, gender is an attribute IV.
• Both types of IV can have what are called levels. For example:
• In the example above, the active IV special tutorial , receiving the tutorial is one level, and tutorial withheld (control) is a second level.
• In the same example, being a third grader would be an attribute IV. It could be defined as only one level—being in third grade or you might wish to define it with more than one level, such as first half of third grade and second half of third grade. Indeed, that attribute IV could take many more, for example, if you wished to look at each month of third grade.

Independent variables are frequently called different things depending on the nature of the research question. In predictive questions, where a variable is thought to predict another but it is not yet appropriate to ask whether it causes the other, the IV is usually called a predictor or criterion variable rather than an independent variable.

Dependent Variables (DV)

• Dependent variables are variables that depend on or are influenced by the independent variables.
• They are outcomes or results of the influence of the independent variable.
• Dependent variables answer the question, "What do I observe happening when I apply the intervention?"
• The dependent variable receives the intervention.

In questions where full clausation is not assumed, such as a predictive question or a question about differences between groups but no manipulation of an IV, the dependent variables are usually called outcome variable s, and the independent variables are usually called the predictor or criterion variables.

Sample Variables

In some studies, some characteristic of the participants must be measured for some reason, but that characteristic is not the IV or the DV. In this case, these are called sample variables. For example, suppose you are investigating whether amount of sleep affects level of concentration in depressed people. In order to obtain a sample of depressed people, a standard test of depression will be given. So the presence or absence of depression will be a sample variable. That score is not used as an IV or a DV, but simply to get the appropriate people into the sample.

When there is no measure of a characteristic of the participants, the characteristic is called a sample characteristic . When the characteristic must be measured, it is called a sample variable .

Extraneous Variables

Extraneous variables are not of interest to the study, but may influence the dependent variable. For this reason, most quantitative studies attempt to control extraneous variables. The literature should inform you what extraneous variables to account for. For example, in the study of third graders' reading scores, variables such as noise levels in the testing room, the size or lighting or temperature of the room, and whether the children had had a good breakfast might be extraneous variables.

There is a special class of extraneous variables called confounding variables. These are variables that can cause the effect we are looking for if they are not controlled for, resulting in a false finding that the IV is effective when it is not. In a study of changes in skill levels in a group of caseworkers after a training program, if the follow-up measure is taken relatively late after the training, the simple effect of practicing the skills might explain improved scores, and the training might be mistakenly thought to be successful when it was not.

There are many details about variables not covered in this handout. Please consult any text on research methods for a more comprehensive review.

Doc. reference: phd_t2_coun_u02s2_h02_quantvar.html

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What Is the Meaning of Variables in Research?

Science Projects to Learn if an Ice Cube Melts Faster in Air or Water

In scientific research, scientists, technicians and researchers utilize a variety of methods and variables when conducting their experiments. In simple terms, a variable represents a measurable attribute that changes or varies across the experiment whether comparing results between multiple groups, multiple people or even when using a single person in an experiment conducted over time. In all, there are six common variable types.

TL;DR (Too Long; Didn't Read)

Variables represents the measurable traits that can change over the course of a scientific experiment. In all there are six basic variable types: dependent, independent, intervening, moderator, controlled and extraneous variables.

Independent and Dependent Variables

In general, experiments purposefully change one variable, which is the independent variable. But a variable that changes in direct response to the independent variable is the dependent variable. Say there’s an experiment to test whether changing the position of an ice cube affects its ability to melt. The change in an ice cube's position represents the independent variable. The result of whether the ice cube melts or not is the dependent variable.

Intervening and Moderator Variables

Intervening variables link the independent and dependent variables, but as abstract processes, they are not directly observable during the experiment. For example, if studying the use of a specific teaching technique for its effectiveness, the technique represents the independent variable, while the completion of the technique's objectives by the study participants represents the dependent variable, while the actual processes used internally by the students to learn the subject matter represents the intervening variables.

By modifying the effect of the intervening variables -- the unseen processes -- moderator variables influence the relationship between the independent and dependent variables. Researchers measure moderator variables and take them into consideration during the experiment.

Constant or Controllable Variable

Sometimes certain characteristics of the objects under scrutiny are deliberately left unchanged. These are known as constant or controlled variables. In the ice cube experiment, one constant or controllable variable could be the size and shape of the cube. By keeping the ice cubes' sizes and shapes the same, it's easier to measure the differences between the cubes as they melt after shifting their positions, as they all started out as the same size.

Extraneous Variables

A well-designed experiment eliminates as many unmeasured extraneous variables as possible. This makes it easier to observe the relationship between the independent and dependent variables. These extraneous variables, also known as unforeseen factors, can affect the interpretation of experimental results. Lurking variables, as a subset of extraneous variables represent the unforeseen factors in the experiment.

Another type of lurking variable includes the confounding variable, which can render the results of the experiment useless or invalid. Sometimes a confounding variable could be a variable not previously considered. Not being aware of the confounding variable’s influence skews the experimental results. For example, say the surface chosen to conduct the ice-cube experiment was on a salted road, but the experimenters did not realize the salt was there and sprinkled unevenly, causing some ice cubes to melt faster. Because the salt affected the experiment's results, it's both a lurking variable and a confounding variable.

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• Arizona State University: Developing Hypothesis and Research Questions
• University of Massachusetts, Amherst: How to Do Case Research Study
• University of California, Berkeley: Lurking Variable

About the Author

Mariecor Agravante earned a Bachelor of Science in biology from Gonzaga University and has completed graduate work in Organizational Leadership. She's been published on USA Today, Medium, Red Tricycle, and other online media venues.

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