how to find independent and dependent variables in research articles

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. Independent variable : plant fertilizers (chosen by researchers) Dependent variable : 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. Independent variable: proximity to hydraulic fracturing sites (a presumed cause and a condition of the environment) Dependent variable: 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.

Unfortunately, your browser is too old to work on this site.

For full functionality of this site it is necessary to enable JavaScript.

Research Variables 101

Independent variables, dependent variables, control variables and more

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

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

Overview: Variables In Research

What (exactly) is a variable.

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

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

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

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

The “Big 3” Variables

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

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

What is an independent variable?

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

For example:

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

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

Need a helping hand?

What is a dependent variable?

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

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

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

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

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

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

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

What is a control variable?

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

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

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

• Temperature
• Time of day
• Noise or distractions

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

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

Other types of variables

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

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

Let’s jump into it…

What is a moderating variable?

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

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

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

What is a mediating variable?

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

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

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

What is a confounding variable?

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

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

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

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

What is a latent variable?

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

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

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

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

Let’s recap

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

To recap, we’ve explored:

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

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

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

You Might Also Like:

Very informative, concise and helpful. Thank you

Helping information.Thanks

practical and well-demonstrated

Very helpful and insightful

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Independent and Dependent Variables
• Purpose of Guide
• Design Flaws to Avoid
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

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.

• << Previous: Design Flaws to Avoid
• Next: Glossary of Research Terms >>
• Last Updated: May 30, 2024 9:38 AM
• URL: https://libguides.usc.edu/writingguide

• Science Notes Posts
• Contact Science Notes
• Todd Helmenstine Biography
• Anne Helmenstine Biography
• Free Printable Periodic Tables (PDF and PNG)
• Periodic Table Wallpapers
• Interactive Periodic Table
• Periodic Table Posters
• How to Grow Crystals
• Chemistry Projects
• Fire and Flames Projects
• Holiday Science
• Chemistry Problems With Answers
• Physics Problems
• Unit Conversion Example Problems
• Chemistry Worksheets
• Biology Worksheets
• Periodic Table Worksheets
• Physical Science Worksheets
• Science Lab Worksheets
• My Amazon Books

Independent and Dependent Variables Examples

The independent and dependent variables are key to any scientific experiment, but how do you tell them apart? Here are the definitions of independent and dependent variables, examples of each type, and tips for telling them apart and graphing them.

Independent Variable

The independent variable is the factor the researcher changes or controls in an experiment. It is called independent because it does not depend on any other variable. The independent variable may be called the “controlled variable” because it is the one that is changed or controlled. This is different from the “ control variable ,” which is variable that is held constant so it won’t influence the outcome of the experiment.

Dependent Variable

The dependent variable is the factor that changes in response to the independent variable. It is the variable that you measure in an experiment. The dependent variable may be called the “responding variable.”

Examples of Independent and Dependent Variables

Here are several examples of independent and dependent variables in experiments:

• In a study to determine whether how long a student sleeps affects test scores, the independent variable is the length of time spent sleeping while the dependent variable is the test score.
• You want to know which brand of fertilizer is best for your plants. The brand of fertilizer is the independent variable. The health of the plants (height, amount and size of flowers and fruit, color) is the dependent variable.
• You want to compare brands of paper towels, to see which holds the most liquid. The independent variable is the brand of paper towel. The dependent variable is the volume of liquid absorbed by the paper towel.
• You suspect the amount of television a person watches is related to their age. Age is the independent variable. How many minutes or hours of television a person watches is the dependent variable.
• You think rising sea temperatures might affect the amount of algae in the water. The water temperature is the independent variable. The mass of algae is the dependent variable.
• In an experiment to determine how far people can see into the infrared part of the spectrum, the wavelength of light is the independent variable and whether the light is observed is the dependent variable.
• If you want to know whether caffeine affects your appetite, the presence/absence or amount of caffeine is the independent variable. Appetite is the dependent variable.
• You want to know which brand of microwave popcorn pops the best. The brand of popcorn is the independent variable. The number of popped kernels is the dependent variable. Of course, you could also measure the number of unpopped kernels instead.
• You want to determine whether a chemical is essential for rat nutrition, so you design an experiment. The presence/absence of the chemical is the independent variable. The health of the rat (whether it lives and reproduces) is the dependent variable. A follow-up experiment might determine how much of the chemical is needed. Here, the amount of chemical is the independent variable and the rat health is the dependent variable.

How to Tell the Independent and Dependent Variable Apart

If you’re having trouble identifying the independent and dependent variable, here are a few ways to tell them apart. First, remember the dependent variable depends on the independent variable. It helps to write out the variables as an if-then or cause-and-effect sentence that shows the independent variable causes an effect on the dependent variable. If you mix up the variables, the sentence won’t make sense. Example : The amount of eat (independent variable) affects how much you weigh (dependent variable).

This makes sense, but if you write the sentence the other way, you can tell it’s incorrect: Example : How much you weigh affects how much you eat. (Well, it could make sense, but you can see it’s an entirely different experiment.) If-then statements also work: Example : If you change the color of light (independent variable), then it affects plant growth (dependent variable). Switching the variables makes no sense: Example : If plant growth rate changes, then it affects the color of light. Sometimes you don’t control either variable, like when you gather data to see if there is a relationship between two factors. This can make identifying the variables a bit trickier, but establishing a logical cause and effect relationship helps: Example : If you increase age (independent variable), then average salary increases (dependent variable). If you switch them, the statement doesn’t make sense: Example : If you increase salary, then age increases.

How to Graph Independent and Dependent Variables

Plot or graph independent and dependent variables using the standard method. The independent variable is the x-axis, while the dependent variable is the y-axis. Remember the acronym DRY MIX to keep the variables straight: D = Dependent variable R = Responding variable/ Y = Graph on the y-axis or vertical axis M = Manipulated variable I = Independent variable X = Graph on the x-axis or horizontal axis

• Babbie, Earl R. (2009). The Practice of Social Research (12th ed.) Wadsworth Publishing. ISBN 0-495-59841-0.
• di Francia, G. Toraldo (1981). The Investigation of the Physical World . Cambridge University Press. ISBN 978-0-521-29925-1.
• Gauch, Hugh G. Jr. (2003). Scientific Method in Practice . Cambridge University Press. ISBN 978-0-521-01708-4.
• Popper, Karl R. (2003). Conjectures and Refutations: The Growth of Scientific Knowledge . Routledge. ISBN 0-415-28594-1.

Related Posts

Dependent vs. Independent Variables in Research

Introduction

Independent and dependent variables in research, can qualitative data have independent and dependent variables.

Experiments rely on capturing the relationship between independent and dependent variables to understand causal patterns. Researchers can observe what happens when they change a condition in their experiment or if there is any effect at all.

It's important to understand the difference between the independent variable and dependent variable. We'll look at the notion of independent and dependent variables in this article. If you are conducting experimental research, defining the variables in your study is essential for realizing rigorous research .

In experimental research, a variable refers to the phenomenon, person, or thing that is being measured and observed by the researcher. A researcher conducts a study to see how one variable affects another and make assertions about the relationship between different variables.

A typical research question in an experimental study addresses a hypothesized relationship between the independent variable manipulated by the researcher and the dependent variable that is the outcome of interest presumably influenced by the researcher's manipulation.

Take a simple experiment on plants as an example. Suppose you have a control group of plants on one side of a garden and an experimental group of plants on the other side. All things such as sunlight, water, and fertilizer being equal, both plants should be expected to grow at the same rate.

Now imagine that the plants in the experimental group are given a new plant fertilizer under the assumption that they will grow faster. Then you will need to measure the difference in growth between the two groups in your study.

In this case, the independent variable is the type of fertilizer used on your plants while the dependent variable is the rate of growth among your plants. If there is a significant difference in growth between the two groups, then your study provides support to suggest that the fertilizer causes higher rates of plant growth.

What is the key difference between independent and dependent variables?

The independent variable is the element in your study that you intentionally change, which is why it can also be referred to as the manipulated variable.

You manipulate this variable to see how it might affect the other variables you observe, all other factors being equal. This means that you can observe the cause and effect relationships between one independent variable and one or multiple dependent variables.

Independent variables are directly manipulated by the researcher, while dependent variables are not. They are "dependent" because they are affected by the independent variable in the experiment. Researchers can thus study how manipulating the independent variable leads to changes in the main outcome of interest being measured as the dependent variable.

Note that while you can have multiple dependent variables, it is challenging to establish research rigor for multiple independent variables. If you are making so many changes in an experiment, how do you know which change is responsible for the outcome produced by the study? Studying more than one independent variable would require running an experiment for each independent variable to isolate its effects on the dependent variable.

This being said, it is certainly possible to employ a study design that involves multiple independent and dependent variables, as is the case with what is called a factorial experiment. For example, a psychological study examining the effects of sleep and stress levels on work productivity and social interaction would have two independent variables and two dependent variables, respectively.

Such a study would be complex and require careful planning to establish the necessary research rigor , however. If possible, consider narrowing your research to the examination of one independent variable to make it more manageable and easier to understand.

Independent variable examples

Let's consider an experiment in the social studies. Suppose you want to determine the effectiveness of a new textbook compared to current textbooks in a particular school.

The new textbook is supposed to be better, but how can you prove it? Besides all the selling points that the textbook publisher makes, how do you know if the new textbook is any good? A rigorous study examining the effects of the textbook on classroom outcomes is in order.

The textbook given to students makes up the independent variable in your experimental study. The shift from the existing textbooks to the new one represents the manipulation of the independent variable in this study.

Dependent variable examples

In any experiment, the dependent variable is observed to measure how it is affected by changes to the independent variable. Outcomes such as test scores and other performance metrics can make up the data for the dependent variable.

Now that we are changing the textbook in the experiment above, we should examine if there are any effects.

To do this, we will need two classrooms of students. As best as possible, the two sets of students should be of similar proficiency (or at least of similar backgrounds) and placed within similar conditions for teaching and learning (e.g., physical space, lesson planning).

The control group in our study will be one set of students using the existing textbook. By examining their performance, we can establish a baseline. The performance of the experimental group, which is the set of students using the new textbook, can then be compared with the baseline performance.

As a result, the change in the test scores make up the data for our dependent variable. We cannot directly affect how well students perform on the test, but we can conclude from our experiment whether the use of the new textbook might impact students' performance.

Turn data into valuable insights with ATLAS.ti

Rely on our powerful data analysis interface for your research, starting with a free trial.

How do you know if a variable is independent or dependent?

We can typically think of an independent variable as something a researcher can directly change. In the above example, we can change the textbook used by the teacher in class. If we're talking about plants, we can change the fertilizer.

Conversely, the dependent variable is something that we do not directly influence or manipulate. Strictly speaking, we cannot directly manipulate a student's performance on a test or the rate of growth of a plant, not without other factors such as new teaching methods or new fertilizer, respectively.

Understanding the distinction between a dependent variable and an independent variable is key to experimental research. Ultimately, the distinction can be reduced to which element in a study has been directly influenced by the researcher.

Other variables

Given the potential complexities encountered in research, there is essential terminology for other variables in any experimental study. You might employ this terminology or encounter them while reading other research.

A control variable is any factor that the researcher tries to keep constant as the independent variable changes. In the plant experiment described earlier in this article, the sunlight and water are each a controlled variable while the type of fertilizer used is the manipulated variable across control and experimental groups.

To ensure research rigor, the researcher needs to keep these control variables constant to dispel any concerns that differences in growth rate were being driven by sunlight or water, as opposed to the fertilizer being used.

Extraneous variables refer to any unwanted influence on the dependent variable that may confound the analysis of the study. For example, if bugs or animals ate the plants in your fertilizer study, this was greatly impact the rates of plant growth. This is why it would be important to control the environment and protect it from such threats.

Finally, independent variables can go by different names such as subject variables or predictor variables. Dependent variables can also be referred to as the responding variable or outcome variable. Whatever the language, they all serve the same role of influencing the dependent variable in an experiment.

The use of the word " variables " is typically associated with quantitative and confirmatory research. Naturalistic qualitative research typically does not employ experimental designs or establish causality. Qualitative research often draws on observations , interviews , focus groups , and other forms of data collection that are allow researchers to study the naturally occurring "messiness" of the social world, rather than controlling all variables to isolate a cause-and-effect relationship.

In limited circumstances, the idea of experimental variables can apply to participant observations in ethnography , where the researcher should be mindful of their influence on the environment they are observing.

However, the experimental paradigm is best left to quantitative studies and confirmatory research questions. Qualitative researchers in the social sciences are oftentimes more interested in observing and describing socially-constructed phenomena rather than testing hypotheses .

Nonetheless, the notion of independent and dependent variables does hold important lessons for qualitative researchers. Even if they don't employ variables in their study design, qualitative researchers often observe how one thing affects another. A theoretical or conceptual framework can then suggest potential cause-and-effect relationships in their study.

With ATLAS.ti, insightful data analysis is at your fingertips

Download a free trial of ATLAS.ti to see how you can make the most of your data.

Independent and Dependent Variables

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

In research, a variable is any characteristic, number, or quantity that can be measured or counted in experimental investigations . One is called the dependent variable, and the other is the independent variable.

In research, the independent variable is manipulated to observe its effect, while the dependent variable is the measured outcome. Essentially, the independent variable is the presumed cause, and the dependent variable is the observed effect.

Variables provide the foundation for examining relationships, drawing conclusions, and making predictions in research studies.

Independent Variable

In psychology, the independent variable is the variable the experimenter manipulates or changes and is assumed to directly affect the dependent variable.

It’s considered the cause or factor that drives change, allowing psychologists to observe how it influences behavior, emotions, or other dependent variables in an experimental setting. Essentially, it’s the presumed cause in cause-and-effect relationships being studied.

For example, allocating participants to drug or placebo conditions (independent variable) to measure any changes in the intensity of their anxiety (dependent variable).

In a well-designed experimental study , the independent variable is the only important difference between the experimental (e.g., treatment) and control (e.g., placebo) groups.

By changing the independent variable and holding other factors constant, psychologists aim to determine if it causes a change in another variable, called the dependent variable.

For example, in a study investigating the effects of sleep on memory, the amount of sleep (e.g., 4 hours, 8 hours, 12 hours) would be the independent variable, as the researcher might manipulate or categorize it to see its impact on memory recall, which would be the dependent variable.

Dependent Variable

In psychology, the dependent variable is the variable being tested and measured in an experiment and is “dependent” on the independent variable.

In psychology, a dependent variable represents the outcome or results and can change based on the manipulations of the independent variable. Essentially, it’s the presumed effect in a cause-and-effect relationship being studied.

An example of a dependent variable is depression symptoms, which depend on the independent variable (type of therapy).

In an experiment, the researcher looks for the possible effect on the dependent variable that might be caused by changing the independent variable.

For instance, in a study examining the effects of a new study technique on exam performance, the technique would be the independent variable (as it is being introduced or manipulated), while the exam scores would be the dependent variable (as they represent the outcome of interest that’s being measured).

Examples in Research Studies

For example, we might change the type of information (e.g., organized or random) given to participants to see how this might affect the amount of information remembered.

In this example, the type of information is the independent variable (because it changes), and the amount of information remembered is the dependent variable (because this is being measured).

For the following hypotheses, name the IV and the DV.

1. Lack of sleep significantly affects learning in 10-year-old boys.

IV……………………………………………………

DV…………………………………………………..

2. Social class has a significant effect on IQ scores.

DV……………………………………………….…

3. Stressful experiences significantly increase the likelihood of headaches.

4. Time of day has a significant effect on alertness.

Operationalizing Variables

To ensure cause and effect are established, it is important that we identify exactly how the independent and dependent variables will be measured; this is known as operationalizing the variables.

Operational variables (or operationalizing definitions) refer to how you will define and measure a specific variable as it is used in your study. This enables another psychologist to replicate your research and is essential in establishing reliability (achieving consistency in the results).

For example, if we are concerned with the effect of media violence on aggression, then we need to be very clear about what we mean by the different terms. In this case, we must state what we mean by the terms “media violence” and “aggression” as we will study them.

Therefore, you could state that “media violence” is operationally defined (in your experiment) as ‘exposure to a 15-minute film showing scenes of physical assault’; “aggression” is operationally defined as ‘levels of electrical shocks administered to a second ‘participant’ in another room.

In another example, the hypothesis “Young participants will have significantly better memories than older participants” is not operationalized. How do we define “young,” “old,” or “memory”? “Participants aged between 16 – 30 will recall significantly more nouns from a list of twenty than participants aged between 55 – 70” is operationalized.

The key point here is that we have clarified what we mean by the terms as they were studied and measured in our experiment.

If we didn’t do this, it would be very difficult (if not impossible) to compare the findings of different studies to the same behavior.

Operationalization has the advantage of generally providing a clear and objective definition of even complex variables. It also makes it easier for other researchers to replicate a study and check for reliability .

For the following hypotheses, name the IV and the DV and operationalize both variables.

1. Women are more attracted to men without earrings than men with earrings.

I.V._____________________________________________________________

D.V. ____________________________________________________________

Operational definitions:

I.V. ____________________________________________________________

2. People learn more when they study in a quiet versus noisy place.

I.V. _________________________________________________________

D.V. ___________________________________________________________

3. People who exercise regularly sleep better at night.

Can there be more than one independent or dependent variable in a study?

Yes, it is possible to have more than one independent or dependent variable in a study.

In some studies, researchers may want to explore how multiple factors affect the outcome, so they include more than one independent variable.

Similarly, they may measure multiple things to see how they are influenced, resulting in multiple dependent variables. This allows for a more comprehensive understanding of the topic being studied.

What are some ethical considerations related to independent and dependent variables?

Ethical considerations related to independent and dependent variables involve treating participants fairly and protecting their rights.

Researchers must ensure that participants provide informed consent and that their privacy and confidentiality are respected. Additionally, it is important to avoid manipulating independent variables in ways that could cause harm or discomfort to participants.

Researchers should also consider the potential impact of their study on vulnerable populations and ensure that their methods are unbiased and free from discrimination.

Ethical guidelines help ensure that research is conducted responsibly and with respect for the well-being of the participants involved.

Can qualitative data have independent and dependent variables?

Yes, both quantitative and qualitative data can have independent and dependent variables.

In quantitative research, independent variables are usually measured numerically and manipulated to understand their impact on the dependent variable. In qualitative research, independent variables can be qualitative in nature, such as individual experiences, cultural factors, or social contexts, influencing the phenomenon of interest.

The dependent variable, in both cases, is what is being observed or studied to see how it changes in response to the independent variable.

So, regardless of the type of data, researchers analyze the relationship between independent and dependent variables to gain insights into their research questions.

Can the same variable be independent in one study and dependent in another?

Yes, the same variable can be independent in one study and dependent in another.

The classification of a variable as independent or dependent depends on how it is used within a specific study. In one study, a variable might be manipulated or controlled to see its effect on another variable, making it independent.

However, in a different study, that same variable might be the one being measured or observed to understand its relationship with another variable, making it dependent.

The role of a variable as independent or dependent can vary depending on the research question and study design.

Related Articles

Research Methodology

Qualitative Data Coding

What Is a Focus Group?

Cross-Cultural Research Methodology In Psychology

What Is Internal Validity In Research?

Research Methodology , Statistics

What Is Face Validity In Research? Importance & How To Measure

Criterion Validity: Definition & Examples

• PRO Courses Guides New Tech Help Pro Expert Videos About wikiHow Pro Upgrade Sign In
• EDIT Edit this Article
• EXPLORE Tech Help Pro About Us Random Article Quizzes Request a New Article Community Dashboard This Or That Game Popular Categories Arts and Entertainment Artwork Books Movies Computers and Electronics Computers Phone Skills Technology Hacks Health Men's Health Mental Health Women's Health Relationships Dating Love Relationship Issues Hobbies and Crafts Crafts Drawing Games Education & Communication Communication Skills Personal Development Studying Personal Care and Style Fashion Hair Care Personal Hygiene Youth Personal Care School Stuff Dating All Categories Arts and Entertainment Finance and Business Home and Garden Relationship Quizzes Cars & Other Vehicles Food and Entertaining Personal Care and Style Sports and Fitness Computers and Electronics Health Pets and Animals Travel Education & Communication Hobbies and Crafts Philosophy and Religion Work World Family Life Holidays and Traditions Relationships Youth
• Browse Articles
• Learn Something New
• Quizzes Hot
• This Or That Game
• Train Your Brain
• Explore More
• Support wikiHow
• About wikiHow
• Log in / Sign up
• Education and Communications
• Mathematics

How to Identify Dependent and Independent Variables

Last Updated: September 19, 2023 Fact Checked

This article was co-authored by Michael Simpson, PhD . Dr. Michael Simpson (Mike) is a Registered Professional Biologist in British Columbia, Canada. He has over 20 years of experience in ecology research and professional practice in Britain and North America, with an emphasis on plants and biological diversity. Mike also specializes in science communication and providing education and technical support for ecology projects. Mike received a BSc with honors in Ecology and an MA in Society, Science, and Nature from The University of Lancaster in England as well as a Ph.D. from the University of Alberta. He has worked in British, North American, and South American ecosystems, and with First Nations communities, non-profits, government, academia, and industry. There are 10 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 134,125 times.

Whether you’re conducting an experiment or learning algebra, understanding the relationship between independent and dependent variables is a valuable skill. Learning the difference between them can be tricky at first, but you’ll get the hang of it in no time.

Understanding Independent and Dependent Variables

• For example, if a researcher wants to see how well different doses of a medication work, the dose is the independent variable.
• Suppose you want to see if studying more improves your test scores. The amount of time you spend studying is the independent variable.

• Say a researcher is testing an allergy medication. Allergy relief after taking the dose is the dependent variable, or the outcome caused by taking the medicine.

Tip: When you encounter variables, plug them into this sentence: “ Independent variable causes Dependent Variable , but it isn't possible that Dependent Variable could cause Independent Variable .

For example: “A 5 mg dose of medication causes allergy relief, but it isn’t possible that allergy relief could cause a 5 mg dose of medication.”

Identifying Variables in Equations

• The $3 per chore is a constant. Your parents set that in stone, and that number isn't going to change. On the other hand, the number of chores you do and the total amount of money you earn aren't constant. They're variables that you want to measure.
• To set up an equation, use letters to represent the chores you do and the money you'll earn. Let t represent the total amount of money you earn and n stand for the number of chores you do.

• Notice that the amount of money you'll earn depends on the number of chores to do. Since it depends on other variables, it's the dependent variable.

Graphing Independent and Dependent Variables

• Say you sell apples and want to see how advertising affects your sales. The amount of money you spent in a month on advertising is the independent variable, or the factor that causes the effect you’re trying to understand. The number of apples you sold that month is the dependent variable.

• Suppose you’re trying to see if advertising more increases the number of apples you sold. Divide the x-axis into units to measure your monthly advertising budget.
• If you’ve spent between $0 and $500 a month in the last year on advertising, draw 10 dashes along the x-axis. Label the left end of the line “$0.” Then label each dash with a dollar amount in $50 increments ($50, $100, $150, and so on) until you’ve reached the last dash, or “$500.”

• Suppose your monthly apple sales have ranged between 60 and 250 over the last year. Draw 10 dashes across the y-axis, label the first “50,” and label the rest of the dashes in increments of 25 (50, 75, 100, and so on), until you’ve written 275 next to the last dash.

• For instance, if you spent $350 on advertising last month, find the dash labeled “350” on the x-axis. If last month’s apple sales totaled 225, find the dash labeled “225” on the y-axis. Draw a dot at the point at the graph coordinate ($350, 225), then continue graphing points for the rest of your monthly numbers.

• For example, say you’ve graphed your advertising expenses and monthly apple sales, and the dots are arranged in an upward sloped line. This means that your monthly sales were higher when you spent more on advertising.

Expert Q&A

You Might Also Like

• ↑ Michael Simpson, PhD. Registered Professional Biologist. Expert Interview. 25 June 2021.
• ↑ https://researchbasics.education.uconn.edu/variables/
• ↑ https://libguides.usc.edu/writingguide/variables
• ↑ https://nces.ed.gov/nceskids/help/user_guide/graph/variables.asp
• ↑ https://www.khanacademy.org/math/algebra/introduction-to-algebra/alg1-dependent-independent/e/dependent-and-independent-variables
• ↑ https://www.mathsisfun.com/algebra/equations-solving.html
• ↑ https://www.khanacademy.org/math/pre-algebra/pre-algebra-equations-expressions/pre-algebra-dependent-independent/a/dependent-and-independent-variables-review
• ↑ https://www2.nau.edu/lrm22/lessons/graph_tips/graph_tips.html
• ↑ https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-equations-and-inequalities/cc-6th-dependent-independent/v/dependent-and-independent-variables-exercise-example-2
• ↑ https://nces.ed.gov/nceskids/help/user_guide/graph/line.asp

About This Article

• Send fan mail to authors

Did this article help you?

Featured Articles

Trending Articles

Watch Articles

• Terms of Use
• Privacy Policy
• Do Not Sell or Share My Info
• Not Selling Info

Get all the best how-tos!

Sign up for wikiHow's weekly email newsletter

How to identify independent and dependent research variables

Introduction

Research variables

Variables are key components of every research study. Understanding their roles is important when you use a research methodology. What are Independent Variables (IV)? These are variables that are changed/manipulated so that their impact on the dependent variables can be monitored. What are Dependent Variables (DV)? These are variables that rely on something else(the independent variables) to occur/change before they can have a result. Dependent Variables are usually the variables the researcher is interested in. Differences between Independent Variables and Dependent Variables 1. Independent Variables are the manipulators or causes or influencers WHILE Dependent Variables are the results or effects or outcome.   2. Independent variables are "independent of" prior causes that act on it WHILE Dependent Variables "depend on" the cause.

Relationship between Hypothesis and Variables A hypothesis is a prediction of what the study will find or the answer to a research question. A hypothesis is an empirical statement that can be verified based upon observation or experiment or experience A hypothesis is testable to be true or false through the research study findings. Variables are found in the hypothesis or research question. In a hypothesis, you can see how variables operate in a research study. How to identify independent and dependent research variables To identify Independent research variables, look for items in your research question or hypothesis that manipulates, causes or influences something or a reaction. To identify Dependent research variables, look for items in your research question or hypothesis that sees the result, effect or outcome of changing the independent variable. Some Examples Example 1 - Research Topic: Decision making and its impact on an organization "Decision making" influences the organization, therefore, this is the Independent Variable "impact in an organization" the organization is being impacted on, therefore, this is the dependent variable Example 2 - Hypothesis/Research Question: What effects do multiple taxations have on small scale businesses "multiple taxations" causes an action, therefore, this is the Independent Variable "small scale businesses" receives the effects of multiple taxations, therefore, this is the Dependent Variable Example 3 - Hypothesis/Research Question: What influence do democratic leadership style and motivation have on employee performance of these businesses "democratic leadership style" and "motivation" both cause an effect, therefore, these are the Independent Variables "employee performance" is being impacted, therefore, this is the Dependent Variable The basic rule is to look for what causes reactions and what receives the effects. With lots of practice, you would begin to spot with ease the Independent and Dependent variables in a research question/Hypothesis.

23  comments:

Anonymous Mar. 16, 2021

very interesting

Reply   

Anonymous Jul. 3, 2021

this was helpful. thank you

Anonymous Nov. 16, 2021

This is the simplest and best explanation of variables and hypothesis. Thank you

Anonymous Sep. 3, 2022

Thank you for this explanation... easy to understand and explained concisely.

Anonymous Oct. 7, 2022

Yhis was really helpful thank you.

Anonymous Oct. 13, 2022

Thank you for this super simple explanation on variables

Anonymous Oct. 24, 2022

Truely, it is simple and useful.

Anonymous Nov. 2, 2022

It was really helpful. You broke it down in simple terms. Thanks

Really great. You made the explanations easy to digest. It was really helpful. Thank you. ~Xandy Umoh

Anonymous Nov. 9, 2022

This was really helpful. Thank you

Anonymous Nov. 28, 2022

Simple and understandable explanation

Anonymous Nov. 29, 2022

Very helpful.. thank you very much!

Anonymous Dec. 15, 2022

Thank you very much This was helpful

Anonymous Jan. 19, 2023

it is interesting and helpful

Anonymous Jan. 30, 2023

very helpful. so grateful and ell appreciated

Anonymous Feb. 8, 2023

Thank you It was helpful

Anonymous Mar. 25, 2023

Thank you so much for making this simple to understand!! Also to anyone reading this comment, please do not be like me and wait to do a research assignment until the last minute. Your future self will

idk why it cut off, but that last part meant to say: Your future self will thank you! :D

Anonymous Mar. 27, 2023

I am still not clear. so for underperforming faculty, who would be the variables? Assessing how community college deans manage underperforming faculty. Who would be the variables - independent and dep

Anonymous Aug. 25, 2023

So clear i love this example giving, am writtin my exam today ...

Anonymous Nov. 21, 2023

Very understandable and explicit. Thanks

Anonymous Dec. 1, 2023

Very helpful thank you so much

Anonymous Mar. 17, 2024

Very Helpful, with the aid of the examples i'i've got a point. Thank you

Anonymous Apr. 2, 2024

Blog categories.

• » Android Projects (3)
• » Computers (19)
• » Digital Skills (0)
• » Educational News (7)
• » Job Seeking Tips (5)
• » Phones & Tablets (5)
• » Project Software Manuals (125)
• » Research Projects (13)
• » Scholarships (1)
• » Web Design (7)
• » Web Development (2)
• » WordPress Tutorials (23)

• West Coast University Libraries
• Research Guides
• Find Materials
• West Coast University
• Research Help

WCU FAQs: Research Help

• APA and Citations
• General Education Classes
• Library Login and Information
• Program-Specific Classes
• Technology Help
• WCU General Information
• Writing and Tutoring Help
• 26 A-Z Database List
• 3 Academic Search Complete
• 1 Acland's Video Atlas of Human Anatomy
• 1 Alexander Street Academic Video
• 1 Authority
• 1 Business Administration
• 2 Business Source Complete
• 1 Credible or Reliable
• 31 Databases
• 2 Dentistry Oral Sciences
• 1 Evaluating Resources
• 3 Evidence based
• 13 Full Text
• 1 Hein Online or HeinOnline
• 1 Integrative Review
• 3 Interlibrary Loan or ILL
• 21 Journal Articles
• 13 Library Catalog
• 1 Library ID
• 1 Library Information
• 1 Link or URL
• 1 Literature Review
• 1 Mergent Intellect
• 2 MESH or mesh
• 2 NURS 350 or N350
• 2 Nursing Reference Center Plus
• 2 Off-campus
• 8 Opposing Viewpoints
• 5 Original Research
• 8 Peer reviewed
• 4 Primary Source
• 14 Proquest
• 3 Qualitative
• 3 Quantitative
• 10 Reference
• 1 Relevance
• 28 Research
• 13 Research Guides
• 1 Research Paper
• 4 Search or Searching
• 1 Secondary Source
• 4 Statistics
• 2 Subject Headings or Subject Terms
• 1 Up To Date

Research Help Question

Research help answer, related topics.

• Journal Articles
• Peer reviewed
• Primary Source
• Original Research
• Opposing Viewpoints

Have a Question?

• Share on Facebook

Was this helpful? Yes 37 No 49

Independent, dependent, and other variables in healthcare and chaplaincy research

Affiliation.

• 1 a Center for Psychosocial Research , Massapequa , New York , USA.
• PMID: 25255148
• DOI: 10.1080/08854726.2014.959374

This article begins by defining the term variable and the terms independent variable and dependent variable, providing examples of each. It then proceeds to describe and discuss synonyms for the terms independent variable and dependent variable, including treatment, intervention, predictor, and risk factor, and synonyms for dependent variable, such as response variables and outcomes. The article explains that the terms extraneous, nuisance, and confounding variables refer to any variable that can interfere with the ability to establish relationships between independent variables and dependent variables, and it describes ways to control for such confounds. It further explains that even though intervening, mediating, and moderating variables explicitly alter the relationship between independent variables and dependent variables, they help to explain the causal relationship between them. In addition, the article links terminology about variables with the concept of levels of measurement in research.

Keywords: confounds; dependent variable; independent variable; levels of measurement; mediation; moderation; risk factors.

• Biomedical Research*
• Chaplaincy Service, Hospital*
• Terminology as Topic*

Board characteristics and cybersecurity disclosure: evidence from the UK

• Published: 04 June 2024

Cite this article

• Ahmad Yuosef Alodat   ORCID: orcid.org/0000-0001-9407-0226 1 ,
• Yunhong Hao 1 ,
• Haitham Nobanee 2 , 3 , 4 ,
• Hazem Ali 5 ,
• Marwan Mansour   ORCID: orcid.org/0000-0001-7166-0063 6 &
• Hamzeh Al Amosh   ORCID: orcid.org/0000-0002-6938-348X 7  

24 Accesses

Explore all metrics

The purpose of this study is to explore the influence of board of directors characteristics on the cybersecurity disclosure (CSD) of firms listed on the London Stock Exchange. The current study used an empirical approach to data collection and analysis. The independent variable is the boards of directors’ characteristics; the dependent variables are the CSD. The study analysed 2250 observation of the UK. listed firms for the period of 2011–2020. The results of the current study show a significant and positive relationship between the extent of CSD and the board size, board independence and board meeting; in terms of board gender diversity yielded an insignificant and positive relationship with the extent of CSD. The findings indicate that firms with more independent and larger board, and high meeting frequency promote cybersecurity transparency and reduce related information asymmetric with stakeholders. The analyses have implications for policymakers, top management, corporate executives and practitioners. Firms are encouraged to restructure their board to enhance its effectiveness to better support and monitor CSD. This is the first study in the UK that examined the determinants of CSD. This adds value to the literature on CSD, in addition to contributing to an understanding of the relationship between board characteristics and CSD.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Adel, C., Hussain, M. M., Mohamed, E. K., & Basuony, M. A. (2019). Is corporate governance relevant to the quality of corporate social responsibility disclosure in large European companies? International Journal of Accounting and Information Management, 32 (6), 301–332.

Article   Google Scholar  

Agrafiotis, I., Nurse, J.R., Goldsmith, M., Creese, S. and Upton, D., (2018). A taxonomy of cyber-harms: Defining the impacts of cyber-attacks and understanding how they propagate.  Journal of Cybersecurity ,  4 (1), p.tyy006.

Aladwey, L., Elgharbawy, A. and Ganna, M.A., (2021). Attributes of corporate boards and assurance of corporate social responsibility reporting: evidence from the U.K.  Corporate Governance: The International Journal of Business in Society , 22 (4), 748–780.

Aladwey, L., Elgharbawy, A. and Ganna, M.A., (2021). Attributes of corporate boards and assurance of corporate social responsibility reporting: evidence from the U.K.  Corporate Governance: The International Journal of Business in Society .

Alodat, A. Y., Al Amosh, H., Khatib, S. F., & Mansour, M. (2023). Audit committee chair effectiveness and firm performance: The mediating role of sustainability disclosure. Cogent Business and Management, 10 (1), 2181156.

Alodat, A. Y., Nobanee, H., Salleh, Z., & Hashim, H. A. (2023). The impact of longer audit committee chair tenure and board tenure on the level of sustainability disclosure: The moderating role of firm size. Business Strategy & Development, 6 (4), 885–896.

Alodat, A. Y., Salleh, Z., & Hashim, H. A. (2022). Corporate governance and sustainability disclosure: Evidence from Jordan. Corporate Governance: The International Journal of Business in Society, 23 (3), 587–606.

Alodat, A. Y., Salleh, Z., Hashim, H. A., & Sulong, F. (2022). Corporate governance and firm performance: Empirical evidence from Jordan. Journal of Financial Reporting and Accounting, 20 (5), 866–896.

Alodat, A.Y., Salleh, Z., Hashim, H.A. and Sulong, F., (2022c). Investigating the mediating role of sustainability disclosure in the relationship between corporate governance and firm performance in Jordan.  Management of Environmental Quality: An International Journal , (ahead-of-print).

Alodat, A. Y., Salleh, Z., Hashim, H. A., & Sulong, F. (2024). Sustainability disclosure and firms’ performance in a voluntary environment. Measuring Business Excellence, 28 (1), 105–121.

Amir, E., Levi, S., & Livne, T. (2018). Do firms underreport information on cyber-attacks? Evidence from capital markets. Review of Accounting Studies, 23 , 1177–1206.

Amran, A., Lee, S. P., & Devi, S. S. (2014). The influence of governance structure and strategic corporate social responsibility toward sustainability reporting quality. Business Strategy and the Environment, 23 (4), 217–235.

Ben Barka, H., & Legendre, F. (2017). Effect of the board of directors and the audit committee on firm performance: A panel data analysis. Journal of Management & Governance, 21 (3), 737–755.

Burgess, Z., & Tharenou, P. (2002). Women board directors: Characteristics of the few. Journal of business ethics, 37 (1), 39–49.

Cheng, E. C., & Courtenay, S. M. (2006). Board composition, regulatory regime and voluntary disclosure. The international journal of accounting, 41 (3), 262–289.

Ciftci, I., Tatoglu, E., Wood, G., Demirbag, M., & Zaim, S. (2019). Corporate governance and firm performance in emerging markets: Evidence from Turkey. International Business Review, 28 (1), 90–103.

Ciglic, K., & Hering, J. (2021). A multi-stakeholder foundation for peace in cyberspace. Journal of Cyber Policy, 6 (3), 360–374.

Cortez, E. K., and Dekker, M. (2022). A Corporate Governance Approach to Cybersecurity Risk Disclosure.  European Journal of Risk Regulation , 1–23.‏

Cyber Security Regulation and Incentives Review, (2016). https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/579442/Cyber_Security_Regulation_and_Incentives_Review.pdf

Donnelly, R., & Mulcahy, M. (2008). Board structure, ownership, and voluntary disclosure in Ireland. Corporate Governance: An International Review, 16 (5), 416–429.

Foglietta, C., Masucci, D., Palazzo, C., Santini, R., Panzieri, S., Rosa, L., Cruz, T., & Lev, L. (2018). From detecting cyber-attacks to mitigating risk within a hybrid environment. IEEE Systems Journal, 13 (1), 424–435.

Freeman, R.E., (2010).  Strategic management: A stakeholder approach . Cambridge university press.

Galletta, S., Mazzù, S., Naciti, V., & Vermiglio, C. (2022). Gender diversity and sustainability performance in the banking industry. Corporate Social Responsibility and Environmental Management, 29 (1), 161–174.

Gao, L., Calderon, T. G., & Tang, F. (2020). Public companies’ cybersecurity risk disclosures. International Journal of Accounting Information Systems, 38 , 100468.

Gordon, L. A., & Loeb, M. P. (2006). Managing cybersecurity resources: A cost-benefit analysis (Vol. 1). McGraw-Hill.

Google Scholar  

Gordon, L. A., Loeb, M. P., Sohail, T., Tseng, C. Y., & Zhou, L. (2008). Cybersecurity, capital allocations and management control systems. European Accounting Review, 17 (2), 215–241.

Haapamäki, E., & Sihvonen, J. (2019). Cybersecurity in accounting research. Managerial Auditing Journal, 34 (7), 808–834. https://doi.org/10.1108/MAJ-09-2018-2004

Haapamäki, E. and Sihvonen, J., (2022). Cybersecurity in accounting research. In  Artificial Intelligence in Accounting  (pp. 182–214). Routledge.

Hair, J. F., Jr., Babin, B. J., & Anderson, R. E. (2010). A global p-erspect-ivie . Kennesaw State University.

Harun, M. S., Hussainey, K., Kharuddin, K. A. M., & Al Farooque, O. (2020). CSR disclosure, corporate governance and firm value: A study on GCC Islamic banks. International Journal of Accounting & Information Management, 28 (4), 607–638.

Hillman, A. J., & Dalziel, T. (2003). Boards of directors and firm performance: Integrating agency and resource dependence perspectives. Academy of Management review, 28 (3), 383–396.

Hillman, A. J., Cannella, A. A., Jr., & Harris, I. C. (2002). Women and racial minorities in the boardroom: How do directors differ? Journal of management, 28 (6), 747–763.

Hlel, K., Kahloul, I., & Bouzgarrou, H. (2020). IFRS adoption, corporate governance and management earnings forecasts. Journal of Financial Reporting and Accounting, 18 (2), 325–342.

Hung, H. (1998). A typology of the theories of the roles of governing boards. Corporate Governance: An International Review, 6 (2), 101–111.

Jensen, M.C. and Meckling, W.H., (2019). Theory of the firm: Managerial behavior, agency costs and ownership structure. In  Corporate Governance  (pp. 77–132). Gower.

Jin, J., (2015). Cybersecurity disclosure effectiveness on public companies.

Kesner, I. F., & Johnson, R. B. (1990). An investigation of the relationship between board composition and stockholder suits. Strategic Management Journal, 11 (4), 327–336.

Khatib, S. F. A., Abdullah, D. F., Elamer, A., & Hazaea, S. A. (2022). The development of corporate governance literature in Malaysia: A systematic literature review and research agenda. Corporate Governance, 22 (5), 1026–1053.

Kılıç, M. and Kuzey, C., (2016). The effect of board gender diversity on firm performance: evidence from Turkey.  Gender in management: An international journal .

Kumar, K., Kumari, R., Nandy, M., Sarim, M. and Kumar, R., (2022). Do ownership structures and governance attributes matter for corporate sustainability reporting? An examination in the Indian context.  Management of Environmental Quality: An International Journal , (ahead-of-print).

Kweh, Q. L., Ting, I. W. K., Hanh, L. T. M., & Zhang, C. (2019). Intellectual capital, governmental presence, and firm performance of publicly listed companies in Malaysia. International Journal of Learning and Intellectual Capital, 16 (2), 193–211.

Lakhal, F. (2005). Voluntary earnings disclosures and corporate governance: Evidence from France. Review of Accounting and Finance, 4 (3), 64–85.

Laksmana, I. (2008). Corporate board governance and voluntary disclosure of executive compensation practices. Contemporary accounting research, 25 (4), 1147–1182.

Li, H., No, W. G., & Wang, T. (2018). SEC’s cybersecurity disclosure guidance and disclosed cybersecurity risk factors. International Journal of Accounting Information Systems, 30 , 40–55.

Liao, L., Lin, T. P., & Zhang, Y. (2018). Corporate board and corporate social responsibility assurance: Evidence from China. Journal of Business Ethics, 150 (1), 211–225.

Liao, L., Luo, L., & Tang, Q. (2015). Gender diversity, board independence, environmental committee and greenhouse gas disclosure. The British accounting review, 47 (4), 409–424.

Lorca, C., Sánchez-Ballesta, J. P., & García-Meca, E. (2011). Board effectiveness and cost of debt. Journal of business ethics, 100 (4), 613–631.

Majumder, M. T. H., Akter, A., & Li, X. (2017). Corporate governance and corporate social disclosures: A meta-analytical review. International Journal of Accounting & Information Management, 25 (4), 434–458.

Mansour, M., Aishah Hashim, H., Salleh, Z., Al-ahdal, W. M., Almaqtari, F. A., & Abdulsalam Qamhan, M. (2022). Governance practices and corporate performance: Assessing the competence of principal-based guidelines. Cogent Business & Management, 9 (1), 2105570. https://doi.org/10.1080/23311975.2022.2105570

Mansour, M., Al Amosh, H., Alodat, A. Y., Khatib, S. F., & Saleh, M. W. (2022). The Relationship between Corporate Governance Quality and Firm Performance: The Moderating Role of Capital Structure. Sustainability, 14 (17), 10525. https://doi.org/10.3390/su141710525

Mansour, M., Hashim, H. A., & Salleh, Z. (2020). Datasets for corporate governance index of Jordanian non-financial sector firms. Data in brief, 30 , 105603. https://doi.org/10.1016/j.dib.2020.105603

Mazumder, M.M.M. and Hossain, D.M. (2022). Voluntary cybersecurity disclosure in the banking industry of Bangladesh: does board composition matter? Journal of Accounting in Emerging Economies , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JAEE-07-2021-0237 .

Michelon, G., & Parbonetti, A. (2012). The effect of corporate governance on sustainability disclosure. Journal of management and governance, 16 (3), 477–509.

Mueller, M. (2017). Is cybersecurity eating internet governance? Causes and consequences of alternative framings. Digital Policy, Regulation and Governance, 19 (6), 415–428. https://doi.org/10.1108/DPRG-05-2017-0025

Mundial, F.E., (2019). The global risks report 2019.  Ginebra: Foro Económico Mundial .

Nobanee, H., Alodat, A., Bajodah, R., Al-Ali, M., & Al Darmaki, A. (2023). Bibliometric analysis of cybercrime and cybersecurity risks literature. Journal of Financial Crime, 30 (6), 1736–1754.

Nobanee, H., Alodat, A.Y., Dilshad, M.N., El Sayah, A., Alas’ ad, S.N., Al Shalabi, B.O., Alsadi, S.F., Al Marri, N.M. and Fiza, F.K., 2023b. Mapping cyber insurance: a taxonomical study using bibliometric visualization and systematic analysis.  Global Knowledge, Memory and Communication . https://doi.org/10.1108/GKMC-03-2023-0082

Pfeffer, J. and Salancik, G.R., 2003.  The external control of organisations: A resource dependence perspective . Stanford University Press.

Radu, C., & Smaili, N. (2022). Board gender diversity and corporate response to cyber risk: Evidence from cybersecurity related disclosure. Journal of business ethics, 177 (2), 351–374.

Ramírez, M., Rodríguez Ariza, L., & Gómez Miranda, M. E. (2022). The disclosures of information on cybersecurity in listed companies in latin america—Proposal for a cybersecurity disclosure index. Sustainability, 14 (3), 1390.

Rao, K. K., Tilt, C. A., & Lester, L. H. (2012). Corporate governance and environmental reporting: An Australian study. Corporate Governance: The International Journal of Business in Society, 12 (2), 143–163.

Rosati, P., Gogolin, F., & Lynn, T. (2022). Cyber-security incidents and audit quality. European Accounting Review, 31 (3), 701–728.

Saggar, R., & Singh, B. (2017). Corporate governance and risk reporting: Indian evidence. Managerial Auditing Journal, 32 (4/5), 378–405.

Saha, R., & Kabra, K. C. (2021). Corporate governance and voluntary disclosure: Evidence from India. Journal of Financial Reporting and Accounting, 20 (1), 127–160.

Saleh, M. W. A., Zaid, M. A. A., Shurafa, R., Maigoshi, Z. S., Mansour, M., & Zaid, A. (2021). Does board gender enhance Palestinian firm performance? The moderating role of corporate social responsibility. Corporate Governance, 21 (4), 685–701. https://doi.org/10.1108/CG-08-2020-0325

Salleh, z., seno, r., alodat, a.y.m. and hashim, h.a., (2022). does the audit committee effectiveness influence the reporting practice of ghg emissions in malaysia?.  journal of sustainability science and management ,  17 (1), pp.204–220.

Securities and Exchange Commission (SEC) (2018). Commission statement and guidance on public company cybersecurity disclosures. Available at: www.sec.gov/rules/interp/2018/33-10459.pdf (accessed 13 November 2018).

Shackelford, S.J. and Bohm, Z., 2016. Securing North American critical infrastructure: A comparative case study in cybersecurity regulation.  Can.-USLJ ,  40 , p. 61.

Smaili, N., Radu, C. and Khalili, A., (2022). Board effectiveness and cybersecurity disclosure.  Journal of Management and Governance , pp. 1–23.

Ullah, I., & Zeb, A. (2023). FinTech and firm’s cash holdings: Evidence from China. Digital Policy, Regulation and Governance, 25 (5), 522–541.

Velte, P., Stawinoga, M., & Lueg, R. (2020). Carbon performance and disclosure: A systematic review of governance-related determinants and financial consequences. Journal of Cleaner Production, 254 , 120063.

Virtanen, A. (2012). Women on the boards of listed companies: Evidence from Finland. Journal of Management and Governance, 16 (4), 571–593.

Williams, R. J. (2003). Women on corporate boards of directors and their influence on corporate philanthropy. Journal of Business Ethics, 42 (1), 1–10.

Download references

Acknowledgements

The authors acknowledge the support from the Program of the National Social Science Foundation of China (19AGL015).

Projects of National Social Science Foundation of China (19AGL015).

Author information

Authors and affiliations.

School of Business Administration, Zhejiang Gongshang University, Hangzhou, 310018, China

Ahmad Yuosef Alodat & Yunhong Hao

College of Business, Abu Dhabi University, Abu Dhabi, UAE

Haitham Nobanee

Oxford Centre for Islamic Studies, University of Oxford, Oxford, UK

Faculty of Humanities and Social Sciences, The University of Liverpool, Liverpool, UK

School of Economics and Managememt, Yiwu Industrial and Commercial College, Jinhua, China

Faculty of Business, Economics and Social Development, Amman Arab University, Amman, Jordan

Marwan Mansour

College of Accounting Sciences, University of South Africa, Pretoria, South Africa

Hamzeh Al Amosh

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Ahmad Yuosef Alodat .

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Alodat, A.Y., Hao, Y., Nobanee, H. et al. Board characteristics and cybersecurity disclosure: evidence from the UK. Electron Commer Res (2024). https://doi.org/10.1007/s10660-024-09867-w

Download citation

Accepted : 13 May 2024

Published : 04 June 2024

DOI : https://doi.org/10.1007/s10660-024-09867-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Corporate governance
• Cybersecurity disclosure
• Board characteristics
• Find a journal
• Publish with us
• Track your research

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Indian J Psychol Med
• v.43(3); 2021 May

A Student’s Guide to the Classification and Operationalization of Variables in the Conceptualization and Design of a Clinical Study: Part 2

Chittaranjan andrade.

Dept. of Clinical Psychopharmacology and Neurotoxicology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India.

Students without prior research experience may not know how to conceptualize and design a study. This is the second of a two-part article that explains how an understanding of the classification and operationalization of variables is the key to the process. Variables need to be operationalized; that is, defined in a way that permits their accurate measurement. They may be operationalized as categorical or continuous variables. Categorical variables are expressed as category frequencies in the sample as a whole, while continuous variables are expressed as absolute numbers for each subject in the sample. Continuous variables should not be converted into categorical variables; there are many reasons for this, the most important being that precision and statistical power are lost. However, in certain circumstances, such as when variables cannot be accurately measured, when there is an administrative or public health need, or when the data are not normally distributed, it may be justifiable to do so. Confounding variables are those that increase (or decrease) the apparent effect of an independent variable on the dependent variable, thereby producing spurious (or suppressing true) relationships. These and other concepts are explained with the help of clinically relevant examples.

Introduction

The first article in this two-part series explained what independent and dependent variables are, how an understanding of these is important in framing hypotheses, and what operationalization of a variable entails. 1 This article is the second part; it discusses categorical and continuous variables and explains the importance of identifying and studying confounding variables.

Categorical and Continuous Variables

Categorical variables are also known as discrete or qualitative variables. These are variables that are operationalized as categories. The value of the variable is expressed as a category count (also known as category frequency) for the sample, or as a cell count (or cell frequency) when the data are presented in a table. Sex is an example of a categorical variable; it is operationalized into male and female categories and is expressed as the number (count, frequency) of males and the number (count, frequency) of females in the sample. Religion (categories: Hindu, Muslim, Christian, Other), place of residence (categories: rural, semi-urban, urban), occupation (categories: unskilled, semi-skilled, skilled), family history of mental illness (categories: present, absent), HIV test result (categories: positive, negative), two-year survival (categories: alive, dead), and response to a question (categories: yes, no) are other examples of categorical variables.

Continuous variables are also known as quantitative variables. These are variables that are operationalized as a number for each unit in the sample. Age is an example of a continuous variable; it is represented by a number for each subject in the sample. Other examples of continuous variables include height, number of previous depressive episodes, total score on a depression rating scale, red blood cell count, and duration of survival after treatment for cancer. Whereas the value of a categorical variable is expressed as category frequencies in the sample, the value of a continuous variable is expressed as mean, median, mode, range, standard deviation, and/or interquartile range for the sample.

Expressing Continuous Variables as Categorical Variables

Continuous variables can be converted into categorical variables. Thus, instead of expressing age as a value for each subject (and as mean and standard deviation for the sample), we can describe age as a category size, as shown in Table 1 . There are many reasons why this is not good statistical practice 2 , 3 :

• We lose precision. Imagine that we classify the variable “Examination result” into pass and fail categories with fail defined as a score of <40 marks and pass as a score of >40 marks. With such a classification, we have no idea whether those who passed barely managed to pass or passed with flying colors. This is exactly what happens when, in a drug trial, we classify patients as responders and nonresponders; we have no idea whether those who responded improved partially and so continued to exhibit residual symptoms, or recovered completely. And, if a male subject in our study does not tell us his exact age but hedges, saying “I’m 20 to 29 years old,” and we are unhappy with his statement, why would we record age as presented in Table 1 ? Loss of precision impairs our ability to see and understand finer details in the data.
• We may classify subjects in a way that defies common sense. With reference to Table 1 , subjects who are 20 and 29 years old, who are nine years apart in age, are classified in the same group (20 to 29 years) whereas subjects who are 29 and 30 years old, who are just one year apart, are classified in different groups (20 to 29 years and 30 to 39 years).
• The boundaries of the categories are arbitrary. There is no mathematical reason why we should prefer categories that increase in units of ten (e.g., 20 to 29, 30 to 39) as opposed, say, to categories that increase in units of eight (e.g., 20 to 27, 28 to 35).
• Statistical significance is harder to achieve in tests applied to categorical data. So, if continuous data are converted into categorical data, the analyses may be contaminated by type 2 (false negative) errors.

Presentation of Age as a Categorical Variable

Note: Data presented are cell count (percentage in the treatment group).

The above notwithstanding, there are certain situations in which continuous variables may justifiably be converted into categorical variables 2 , 3 :

• There is an administrative or public health need. As an example of an administrative need, age may be categorized into pediatric, adult, and geriatric groups for hospital services. As an example of a public health need, blood pressure and low-density lipoprotein cholesterol values may be split into different categories, using different cut-off values, for category-specific treatment guidelines. Similarly, classifying patients into responders and nonresponders satisfies a public health need; it helps people understand how many patients can be expected to improve to at least the cut-off point (e.g., 50% improvement) that was used to define treatment response.
• The variable cannot be accurately measured. This happens, for example, when rural or illiterate patients are unable to state their exact age but are able to say that they are in their twenties, thirties, or forties.
• A variable shows a nonlinear association with the dependent variable. As an example, in the well-known Yerkes–Dodson curve, low and high stress are both associated with poorer performance or achievement, whereas moderate stress is associated with higher performance or achievement. There may, likewise, be nonlinear associations between alcohol intake and ischemic heart disease events.
• The data are skewed, that is, there are some subjects (outliers) with extreme values. In such situations, the mean is not an appropriate measure of central tendency; rather, the median is appropriate. The data, then, are either ranked and studied using nonparametric tests or categorized and further studied. As an example, data on physical exercise variables are usually skewed: most people do little exercise, some people exercise moderately, and a few people exercise vigorously. For statistical analysis, such data may be categorized into tertiles, quartiles, or quintiles.
• The data are presented in a histogram. When histograms are necessary to explain the data, the only way to do so is to present continuous data in class intervals (or categories) along the X-axis, with frequency count displayed on the Y-axis.
• When risks need to be calculated. In logistic regression analyses, continuous data may be converted into categories (e.g., quintiles) so that odds ratios (e.g., for highest vs. lowest quintiles) can be calculated. Such a strategy can be used, for example, to examine the influence of baseline low-density lipoprotein cholesterol level on the five-year risk of an ischemic heart disease event. As a simpler example, in a randomized, placebo-controlled trial, the relative risk of response or remission can only be determined if a cut-off is applied to continuous data (obtained using a rating scale) to classify patients into response or remission categories.

Confounding Variables

A discussion on variables is incomplete without a section on confounding variables. Consider the following example. We study data on mortality associated with helmet use in a thousand two-wheeler traffic accident cases ( Table 2 ). Here, wearing a helmet is the (categorical) independent variable, and occurrence of death is the (categorical) dependent variable. It would indeed seem, from the statistically significant finding in Table 2 , that wearing a helmet protects the rider from serious head injuries and death. Can we conclude that the data prove a cause and effect relationship and hence that wearing a helmet should be made compulsory for two-wheeler riders? To the layperson’s eye, it would seem so.

Mortality Associated With Traffic Accidents Involving Two-Wheeler Riders

Note: Chi-square = 90.91; df = 1; P < 0.001.

This was an observational study, not a randomized controlled trial. So, we must consider another possibility. What if personality factors are responsible for reckless riding (resulting in more serious and potentially fatal accidents) as well as for a disregard for safety measures such as helmet use? If such is the case, then recklessness, rather than not wearing a helmet, would partly or wholly explain the mortality risk. So, personality may be a confounding variable that influences the association between wearing a helmet and the risk of death in a traffic accident. Expressed otherwise, people who are careful by nature may ride carefully and be less likely to suffer an accident. People who are careful are also more likely to obey laws and wear helmets. So, carefulness, as a personality trait, is what saves lives; wearing a helmet is merely a marker for carefulness and hence a lower risk of accidents.

Similarly, we may find that overcrowding of wards is associated with a higher risk of postoperative infection. It may not be the crowding of beds in the wards that increases the risk; rather, when wards are crowded, the number of visitors proportionately increases, and the risk of germs being brought into the ward also proportionately increases. Thus, visitor density, and not bed density, may explain the relationship between overcrowding of wards and postoperative infection. Bed density is just a marker of (increased) risk of infection.

In an example cited in the first article in this series, 1 age is the confounding variable that explains the association between the number of teeth and body weight in preschool children. In an example relevant to perinatal psychiatry, the increased risk of autism spectrum disorder (ASD) associated with antidepressant (AD) use during pregnancy may not be because of AD exposure; it may be because of genetic factors or behavioral changes associated with depression. The use of AD to treat depression during pregnancy is therefore merely a marker for the increased risk of ASD. Thus, the genetic factors and behavioral changes are confounding variables that partly or wholly explain the association between AD exposure and the risk of ASD. 4 , 5

Readers may note that confounding variables may also mask relationships between independent and dependent variables. 6 For example, a study may find that stress has no significant effect on performance. However, had motivation been examined as a confounding variable, the study might have found that stress increased performance in persons with high motivation and decreased performance in persons with low motivation, resulting in a net absence of effect in the sample as a whole. A more extensive discussion on confounding variables is available elsewhere. 6 – 9

From this discussion, it should be clear that once the independent variable has been defined, confounding variables comprise all the other variables that can either increase or decrease the value of the dependent variable. In good research, therefore, all variables which influence the dependent variable should be measured and studied, and not just the independent variable(s) of interest. It would be disastrous to complete a study and then discover that an important confound had not been studied.

Concluding Notes

It is important to identify and study all important dependent and independent variables related to the study’s subject. This requires careful thought at the time of preparation of the research protocol, itself. As an example, in a study on sociodemographic and clinical predictors of AD response in patients with major depressive disorder, after data collection is complete, it is too late to remember that adherence to AD treatment should also have been studied.

Studying a large number of variables improves the understanding of the subject of the study as well as allows the examination of the influence of confounding variables. Thus, if a researcher wishes to examine the effects of diet on ischemic heart disease, it is not sufficient to collect information only about dietary habits and the occurrence of myocardial infarction in a large cohort of subjects. A far better design would be to include:

• The following independent variables: age, sex, dietary habits, exercise patterns, smoking, alcohol intake, family history of ischemic heart disease, medical history of diabetes and hypertension, and so on.
• The following dependent variables: occurrence(s) of angina during follow-up, occurrence(s) of myocardial infarction during follow-up, need for angioplasty or other surgical intervention, and occurrence of cardiovascular death.

It is important to study the same variable using different instruments. This is because not all instruments are equal in sensitivity, specificity, reliability, validity, and other characteristics. Furthermore, different instruments may measure different aspects of the same variable, or different concepts of the same variable when the variable is abstract (e.g., personality, depression, and psychosis). Thus, as explained in the first part of this article, 1 when studying the influence of medication on depression, it is a good idea to use several different methods for the assessment of the disorder, and not just one method, and several methods for the assessment of the same dependent variable, and not just one.

Declaration of Conflicting Interests: The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author received no financial support for the research, authorship, and/or publication of this article.