Confounding Variable: Easy Guide + Examples

Confounding variables are the 3rd alternative in a study searching for eventual cause-and-effect interrelations. Such factors are related to supposed reason and have an influence on a dependent variable. Failing to account for confounders may distort the values and, thus, lead to inaccurate study results. A hypothesis may suggest there is a correlation between independent and dependent factors, when in fact, there isn't.

Read on our article to learn more about a confounding variable, how to determine it and methods of control. You will find good examples for better understanding plus the best dissertation writing services to maximize your results. Let’s dive deep into details.  

Confounding variable is an extra factor that influences both independent and dependent variables. Confounders are the types of extraneous variables that affect a cause-and-effect relationship and may change an outcome of an experiment.

A factor involved in a study needs to meet the following criteria to be a confounder:  

For example, a hypothesis that exercising is helpful for weight loss may not be 100% accurate, unless you account for the third factor – healthy diet. Chances are that those people who follow an active lifestyle also eat healthy food. In this case, the latter is a confounder: it’s associated with an independent factor and has an evident impact on a dependent variable.

Because confounding variables in research can distort a causal relationship, it is important that you measure them. Accounting for such factors ensures the validity of research results. These factors might be easily confused with an independent determinant because they also produce an effect. In turn, your whole statistical experiment might become invalid. For this reason, you should be able to identify any external factors involved in your study and keep in mind their impact on the outcome.

Let’s look through several examples of confounding variables so that you get a better idea. As was mentioned earlier, failing to consider confounding may lead you to false findings. Even if you identify a causal relationship right, you may misestimate the influence of an independent factor on a dependent one.

For example, if you are studying whether drinking alcohol causes heart diseases, you should also consider another extra factor – tobacco smoking. In this case, those people who consume alcohol tend to smoke more than those who don’t, so tobacco smoking is a confounding variable. Nothing has been mentioned about the sex and age. A research design like this fails to account for other factors and, as such, may cause bias.  

Tip: While trying to detect a confounder, don’t confuse it with some other types. These are factors other than an independent variable that may cause a result.

There are several control methods that help students reduce the impact of confounding variables. Researchers might exercise these measures to minimise undesired effect:

Each of these methods has their own advantages and drawbacks. Let’s discuss these techniques more in detail.

One good way to account for potential confounders is to randomize the values of your independent factor. If there are 2 groups in your study – a treatment group and a control one – you can randomly select participants for each experimental group. Random assignment to groups works especially well if you have a decent sample size. Eventually, you will have the same average value for both groups no matter what factor you have involved.

Randomization is probably the best control method since it allows you to minimize the influence even of those factors that you haven’t considered. But as with any other method, it also has disadvantages.  

Restriction is another helpful method of controlling for confounding bias. This technique boils down to ensuring similarity in potential confounders. For instance, an experimenter may choose participants of the same age. This will remove any variation in values between 2 experimental groups. In this respect, such experiment will restrict possible influence of age on the outcome.

We have more than one experimental group example to follow. You will get a better understanding of how to divide one team into different groups.  

Matching is a technique that allows to eliminate confounding variables by comparing the members with the same values. In this method, you should create matching pairs of subjects who are similar in terms of extra factors, but have different independent factor values. Once matching is done, one participant of such pair goes into a comparison group, and the other one is placed into a treatment group.

For example, you may select participants of the same age to study the effect of caffeine on heart disease and place them into different groups. One of members in such pair must consume caffeine, while the other one shouldn’t. Such study design will help you make an accurate comparison.  

All of these techniques work only during an experiment and before data collection. But if an experimental design is complete or impossible to carry out, you should adjust for potential confounders.  

Unlike other types of bias, confounders can be adjusted after your study is complete. There are several analytical methods that help adjusting for the effect of extra factors:

Both methods allow to reduce the effect after a study is complete. However, they are only applicable if data is already collected. Information concerning influencing factors should be collected during research. If anything seems too difficult at this point, our help in writing a research paper will do you lots of good.

Stratification allows adjusting for common confounding variables by sorting participants into distinct categories according to various factors involved. This method is practiced in class and case-control experiments. An experimenter should create several subgroups in which values are the same or don't vary significantly (e.g. socioeconomic status, country of origin, or age). In this case, the values will be assessed separately in each stratum.

Multivariate analysis is one of the most widely used methods of confounding control. It’s the only way to adjust for multivariate confounding variables at once. Multivariate analysis has to do with the factors of interest (e.g. risk factor or exposure). Values of these factors are measured when they are held constant.

Multivariate statistical analysis has proven to be an effective tool among those techniques that provide quantitative risk assessment systems. This technique is especially useful if there is a great impact on the results.  

A confounding variable is an additional factor that may misrepresent the results of your study. Such factors may cause bias. For this reason, you should account for all extra factors involved in your observational study. Unlike other variables, experimenters are able not only to control such factors, but also adjust for them using stratification or multivariate analysis. Get ready to test which method works best for you.