Key Takeaways
- Confounding variables can confuse the true relationship between dependent and independent variables in research.
- Researchers need to control for confounding variables to ensure their conclusions are accurate.
- True experimental research, with random assignment, is the best way to control for confounding variables.
Confounding variables are external factors (typically a third variable) in research that can interfere with the relationship between dependent and independent variables.
Real World Examples of Confounding Variables
Typical examples of confounding variables often relate to demographics and social and economic outcomes.
For instance, people who are relatively low in socioeconomic status during childhood tend to do, on average, worse financially than others do when they reach adulthood, explains Glenn Geher, PhD, professor of psychology at State University of New York at New Paltz and author of “Own Your Psychology Major!” While he said we could simply think this because poverty begets poverty, he also says there are other variables that are conflated with poverty.
People with lower economic means tend to have less access to high quality education, which is also related to fiscal success in adulthood, Geher explained. Furthermore, poverty is often associated with limited access to healthcare and, thus, with increased risk of adverse health outcomes. These factors can also play roles in fiscal success in adulthood.
“The bottom line here is that when looking to find factors that predict adult economic success, there are many variables that predict this outcome, and so many of these factors are confounded with one another,” Geher said.
The Impact of Confounding Variables on Research
Psychology researchers must be diligent in controlling for confounding variables, because if they are not, they may draw inaccurate conclusions.
For example, during a research project, Geher’s team found the number of stitches one received in childhood predicted one’s sexual activity in adulthood.
However, Geher said “to conclude that getting stitches causes promiscuous behavior would be unwarranted and odd. In fact, it is much more likely that childhood health outcomes, such as getting stitches, predicts environmental instability during childhood, which has been found to indirectly bear on adult sexual and relationship outcomes,” said Geher.
In other words, the number of stitches is confounded with environmental instability in childhood. It’s not that the number of stitches is directly correlated with sexual activity.
Another example that shows confounding variables is the idea that there is a positive correlation between ice cream sales and homicide rates. However, in fact, both these variables are confounded with time of year, said Geher. “They are both higher in summer when days are longer, days are hotter, and people are more likely to encounter others in social contexts because in the winter when it is cold people are more likely to stay home—so they are less likely to buy ice cream cones and to kill others,” he said.
Both of these are examples of how it is in the best interest of researchers to ensure that they control for confounding variables to increase the likelihood that their conclusions are truly warranted.
Universal confounding variables across research on a particular topic can also be influential. In an evaluation of confounding variables that assessed the effect of alcohol consumption on the risk of ischemic heart disease, researchers found a large variation in the confounders considered across observational studies.
While 85 of 87 studies that the researchers analyzed made a connection to alcohol and ischemic
heart disease, confounding variables that could influence ischemic heart disease included, smoking, age, and BMI, height, and/or weight. This means that these factors could have also affected heart disease, not just alcohol.
While most studies mentioned or alluded to “confounding” in their Abstract or Discussion sections, only one stated that their main findings were likely to be affected by confounding variables. The authors concluded that almost all studies ignored or eventually dismissed confounding variables in their conclusions.
Because study results and interpretations may be affected by the mix of potential confounders included within models, the researchers suggest that “efforts are necessary to standardize approaches for selecting and accounting for confounders in observational studies.”
Techniques to Identify Confounding Variables
The best way to control for confounding variables is to conduct “true experimental research,” which means researchers experimentally manipulate a variable that they think causes a certain outcome. They typically do this by randomly assigning study participants to different levels of the first variable, which is referred to as the “independent variable.”
For example, if researchers want to determine if, separate from other factors, receiving a full high-quality education, including a four-year college degree from a respected school, causes positive fiscal outcomes in adulthood, they would need to find a pool of participants, such as a group of young adults from the same broad socioeconomic group as one another. Once the group is selected, half of them would need to be randomly assigned to receive a free, high-quality education and the other half would need to be randomly assigned to not receive such an education.
“This methodology would allow you to see if there are fiscal outcomes on average for the two groups later in life and, if so, you could reasonably conclude that the cause of the differential fiscal outcomes is found in the educational differences across the two groups,” said Geher. “You can draw this conclusion because you randomly assigned the participants to these different groups—and process that naturally controls for confounding variables.”
However, with this process, different problems emerge. For instance, it would not be ethical or practical to randomly assign some participants to a “high-quality education” group and others to a “no-education” group.
“[Controlling] confounding variables via experimental manipulation is not always feasible,” Geher said.
Because of this, there are also statistical ways to try to control for confounding variables, such as “partial correlation,” which looks at a correlation between two variables (e.g., childhood SES and adulthood SES) while factoring out the effects of a potential confounding variable (e.g., educational attainment).
However, statistical control that addresses confounding by measurement can point to confounding through inappropriate control.
“This statistically oriented process is definitely not considered the gold standard compared with true experimental procedures, but often, it is the best you can do given ethical and/or practical constraints,” said Geher.
The Importance of Addressing Confounding Variables in Research
Controlling for confounding variables is critical in research primarily because it allows researchers
to make sure that they are drawing valid and accurate conclusions.
“If you don’t correct for confounding variables, you put yourself at risk for drawing conclusions regarding relationships between variables that are simply wrong (at the worst) or incomplete (at the best),” said Geher.
Controlling for confounding variables includes a basic set of skills when it comes to the social
and behavioral sciences, he added.
The Role of Confounding Variables in Valid Research
Human behavior is highly complex and any single action often has a broad array of variables that underlie it.
“Understanding the concept of confounding variables, as well as how to control for these variables, makes for better behavioral science with conclusions that are, simply, more valid that research that does not effectively take confounding variables into account,” Geher said.
Verywell Mind uses only high-quality sources, including peer-reviewed studies, to support the facts within our articles. Read our editorial process to learn more about how we fact-check and keep our content accurate, reliable, and trustworthy.
Wallach JD, Serghiou S, Chu L, et al. Evaluation of confounding in epidemiologic studies assessing alcohol consumption on the risk of ischemic heart disease. BMC Med Res Methodol. 2020;20(1):64. https://doi.org/10.1186/s12874-020-0914-6
Pourhoseingholi MA, Baghestani AR, Vahedi M. How to control confounding effects by statistical analysis. Gastroenterol Hepatol Bed Bench. 2012;5(2):79-83. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4017459/
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