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Confounding Variables in Research | Complete Overview

Confounding Variables

Research-based on scientific methods attains information through rigorous dissection & deconstruction, observations, analysis, inference and intelligent assumptions & hypothesizations. Every perceivable aspect, attribute, & underlying process is subject to minute analysis that uncover knowledge and leads to logical inferences, accurate hypothesizations & eventually, theories.

This is how scientific research leads to the manifestation of specific types of research variables.  Confounding variables, also known as confounders, are a type of research variable. Primarily appearing in research focused on exploring causality, they are extraneous variables affecting other variables heavily.

They influence dependent and independent research variables, causing spurious & distorted associations to creep up.&nbsp Understanding and managing confounding variables are crucial to the success of any research. Find out everything you need to know about confounding variables through this article.

What is a Confounding Variable?

So, what is a confounding variable?  Assumptions and inferences are integral in scientific investigations. As we research & acquire more information, we reinforce our assumptions, make logical inferences, and gradually progress towards something concrete.

However, for that to happen, the validity of information and the system of gathering said information must be valid.  Variables in any research allow researchers to assume and re-assume. It is only then one can infer and hypothesise with increasing accuracy. However, not all variables are conducive to every scientific train of thought. Confounding variables are such variables that arise in causal inference and have a profound adverse effect on it.

As the name suggests, confounding variables aim to confound researchers as they are looking to understand underlying causal relationships. Confounders are causal concepts and produce distorted associations between variables that are not causally related.  

For example, say we have two variables, X & Y, and your research shows an association between them due to another variable Z, then Z is the confounding variable. Z influences the relationship between the two variables; thus, the link between X and Y is deemed spurious. 

Z is a confounding variable for this relationship but can be a valuable dependent and/or independent variable in other cases. 

Confounding Variables vs Extraneous Variables

Confounding variables can be classified as a form of extraneous variables. But there are subtle differences. 

What is the difference between confounding and extraneous variables? – this is a quite common query among novice researchers. Well, confounding variables are a special kind of extraneous variables. Extraneous variables are those you do not intend to study or are concerned with.

They can threaten to distort or damage the internal validity of research results. The extraneous variables that affect the causal inference process in research are confounding variables. 

Extraneous variables generate an association between variables that are NOT causally related. 

Suppose we have two variables, A and B, associated. And this association is a direct consequence of the influence of a third variable, C, on both A and B. Then, the third variable C is extraneous, and the association between A & B is spurious. 

Generally, any variable that masks the actual association between dependent & independent variables is extraneous. They may directly affect the independent variable or combine with the dependent variable/s to affect the independent one. This makes it essential to control extraneous variables for accurate research.

Note that extraneous variables are relevant to research variables & not noise. They can be situational, organismic, or sequential and become a subject of inquiry or a dependent/ independent variable during another line of inquiry. 

Confounding variables are similar to extraneous variables. The only difference is that they affect variables that are not spuriously associated but causally related. Due to the presence of causality, confounding variables affect both the dependent and causally-affected independent variables. 

For example, say M and N are associated and causally related, whereas M depends on N. Then, a confounding variable O will exaggerate the impact of N (the independent variable) on M (the dependent variable). It inflates the causal relationship between M and N & leads to a distorted causal inference.

Confounding variables influence both IV and DV, & thereby, the causal relationship between them.

Understanding the ideas behind causal inference and causality in research is important. Causality indicates a causal relationship between research variables, whereas a change in one variable causes a change in the other. A change in the independent variable (cause) causes a ca change in the dependent variable (effect). Causality is prevalent across natural and man-made phenomena, and causal inference is the scientific process of investigating causality.

It is also important to distinguish between correlation, association, and causation. Correlation implies association but not causation. On the other hand, causation implies association but not correlation. 

Now that we know the differences between confounding and extraneous variables let's look at the different types of confounding variables.

What are the Different Types of Confounding Variables?

Confounders/confounding variables are major causes of concern in specific research domains. Researchers in medicine and healthcare need to identify and control confounders carefully. Risk assessments that evaluate the nature and extent of the impact of risk factors on human health must pay close attention to the existence of confounding variables.

In other research contexts, the effect of confounding variables may not be as catastrophic as in medical research. 

There are no definitive types of confounding variables. The nature and type depend upon the study's domain, subject, and methodology. Any variable that affects the causality between the variables understudying is a type of confounding variable. If an independent variable is distinct from the independent variable under focus but is empirically inseparable, then it's a confounder. 

Finding confounders is more crucial for an experiment than figuring out their type. Identifying them is paramount as they make it impossible to differentiate their isolated impact on the dependent.

Distinguishing Confounding, Independent, and Dependent Variables

What is an Example of a Confounding Variable?

In light of analytical medical research, dependent and independent variables are the primary research variables. Dependent variables, such as the probability of disease occurrence, depend upon independent variables like lifestyle and genetic factors. 

Confounding variables are conceptually distinct but empirically inseparable from independent variables. Thus, they affect both dependent and independent variables or the relationship between the dependent and independent variables. Dependent & independent variables are variables under focus in research while confounding variables impede their study.

A confounding variable confounds the causal association between the dependent & independent variables. Here’s an example. 

  • If we wish to investigate the impact of vehicular exhaust on coronary heart disease, then confounding variables would be the differential exposures to factors like cigarette smoke, factory exhausts, or any other form of particulate-laden gas.
  • If we conduct the above experiment, exposing the treatment or experimental group to vehicular exhausts would be unwise and unethical. A natural experiment would have to be conducted, where a randomised group of participants would be studied. And in such cases, confounders such as cigarette smoke and factory exhaust can lead to aggravated observations of coronary heart disease (the dependent variable) symptoms. 

It is impossible to isolate the confounder from the independent variables of concern. 

What is the Impact of Confounding Variables On Research Results?

Confounders are impediments to causal inference or identifying genuine causal effects. Confounding variables are major causes of concern in epidemiological studies in medicine & healthcare. There are as many threats to validation as random errors and biases. 

Identifying confounding variables and confounders is critical in epidemiological investigations. Cited as confound bias in research papers, they can significantly impact research findings. Another example can illuminate the threat of confounding variables on research results. 

Suppose, in a hypothetical study, all patients who received treatment X exhibited much more severe symptoms before administration than those who received treatment Y. Subsequently, there was a significant statistical difference between X & Y, with treatment X being more effective than treatment Y. So, it must be that treatment X must be better than treatment Y!

Unless we look into any confounding variables, we would be making a highly inaccurate assumption. Given the severity of the disease, the severity of symptoms is likely to be associated with the choice of treatment and the outcome.

Let’s also take a look at examples of confounding variables in psychology. 

Confounding Variables in Psychology: An Example

You are experimenting to test the impact of situational and circumstantial factors on exam performance. Exam performance is the dependent variable in the study design. Situational & circumstantial factors such as family conditions, relationships, income levels, study environment & the like are independent factors. 

The most prominent confounders in this research will be the subjects' preparation level, access & availability of study resources, level & quality of guidance, skill & intelligence level, and extent & accuracy of pre-existing knowledge. These confounding variables would distort the outcome of the above psychological experiment severely. 

Some of the most common types of confounders prevalent in psychological experiments are 

  • Order Effects = The order of exposure of participants to experiment conditions
  • Participant Variability = The variability in the nature and features of participants, prominent enough to affect the research outcomes
  • Social Desirability Effect = The phenomenon where participants and respondents conceal or manipulate their responses to look good
  • Hawthorne Effect = Changes in respondent behaviour due to them being aware of being observed or studied
  • Demand Characteristics = Factors that can indicate a study’s aim to a respondent, affecting their behaviour and response. 
  • Evaluation Apprehension = The fear or apprehension of being negatively evaluated concerning other group members

Now that you know what confounding variables are in psychology, time to look into the crucial steps of identifying and controlling confounders. 

How can Researchers Identify & Control Confounding Variables?

Determining and controlling confounding variables might be researchers' most important and imposing task. This is because it impacts and distorts the outcome of the research & confounds any clear inference. A major aspect of nearly every research design & methodology is identifying and controlling/nullifying the impact of confounders.

A sound and intellectually-founded theoretical framework, minute analysis, and rigorous investigation can help discerning confounders. 

  • Theory and knowledge will tell us where to look for potential confounding variables. The more minute you understand a concept or phenomenon, the better you will understand how it works. What causes it to occur? What parameters and variables best define it? 
  • Alongside knowledge, systematic analysis is also crucial for identifying potential extraneous and confounding factors. Theories will point us in the right direction, while careful analysis allows us to understand which factors are confounding & why they are so. 
  • Rigorous investigation and detective work uncover the evidence for substantiating factors as confounding. This is how you prove how a confounder affects causal relationships and research outcomes. 
Together, the above three factors can help us understand whether the impact of a confounder will have a statistically significant impact on research outcomes. So, how to control confounding variables? How to account for confounding variables in any study? 

Controlling for Confounding Variables

When controlling for confounding variables, the best way is to minimise their impact in the design phase and adjust for them during analysis. In medical research, three prominent ways of adjusting and minimising the impact of confounding variables are  


Choosing respondents from a large, diverse, and random population can increase the probability of equal distribution of confounders among the lot. Both known and unknown confounding variables can be distributed equally among the respondents of treatment/exposure groups. Randomisation remains an excellent way to manage participant-relevant confounding variables.

Restriction of Enrolment

Another technique for controlling confounding variables is to limit the study to a specific group of subjects. This allows for better management of confounders as we know the level of every respondent. 

Matching Compared Groups 

Matching study participants with respect to their vulnerability to confounding factors is another effective technique. This is a popular method in cohort and case-control studies, leading to the same distribution of confounding variables. 

We review the critical importance of controlling confounding variables as we wrap up. 

Why is It Important to Control Confounding Variables?

As mentioned in the sections above, confounding variables distort our understanding of the causal relationship between dependent and independent variables. The effects of irrelevant, extraneous factors confound our observation and understanding of true relationships. The consequences of such confounding bias can be calamitous, especially in medical research.

Confounding variables mask actual associations, amplify actual causal relationships, or demonstrate false & apparent associations. This makes it difficult to demonstrate a clear causal link between dependent and independent variables. Confounders interfere & compete with exposures of interest in elaborating a study’s outcomes. 

Well, that’s about it for this write-up. 

Here’s hoping that this was a good read and helped you learn about confounding variables. Use this guide and go to authoritative literature to accurately define confounding variables. 

Connect with to tackle confounders in research or for assignments on confounding variables and/or confounding bias. 

Call, mail, or chat live with us to learn more.


Q: Are all confounding variables avoidable?

Ans: Carefully-constructed research designs, randomisations, subject restrictions, and group matchings are effective ways to avoid confounding biases. 

Q: Can confounding variables be positive?

Ans: Sure. It is a positive confounder if the variable amplifies the causal relationship between the dependent and independent variables. 

Q: What are confounding variables in psychology?

Ans: Anything that masks or distorts the relationship between IV and DV in a psychology study is a confounding variable.

Q: How to control for confounding variables in multivariate analysis?

Ans: In multivariate analyses, techniques such as logistic regression, linear regression, and analysis of covariance allow for effective control of confounding variables.

Q: What are confounding variables in a research study?

Ans: Variables that affect the dependent & independent variables and distort inferences of causality between them are confounding research variables.

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 Bella Phillips
Bella Phillips

Bella Phillips is a experienced writer with extensive expertise in crafting assignments, dissertations, and thesis. With a wealth of experience, she has become a go-to resource for students seeking assistance. Bella's skillful writing and commitment to academic excellence make her an invaluable guide in navigating the complexities of academic projects.

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