Lurking variables are notorious for confusing researchers especially when the outcome of a study is being analyzed. This confusion stems from whether the relationship between variables is based on cause-and-effect or just random association. 

What is a Lurking Variable?

A lurking variable is defined as an extraneous variable that is not included in statistical analysis.

They are called lurking variables because they go undetected by lurking or hiding underneath the surface of the variables that are of interest to the researcher, thereby making the relationship between them seem stronger or weaker than it actually is. Because lurking variables are mostly unknown, control measures are often not put in place for them.

Lurking variables can either hide an existing relationship between variables or create a phantom correlation where none exists. This eventually leads to biased, or misleading results in the study. 

Read: Research Bias: Definition, Types + Examples

Effects of Lurking Variables

The effect of lurking variables arises from how they provide another interpretation for the relationship that exists between the independent and dependent variables while remaining hidden.

For example, At a time when the sales of sweaters are down, we also notice that the demand for cold water increases. 

Does this mean that increased demand for cold water is what caused low sales of sweaters? 

The answer is No. 

You can deduce from the above example that there is no correlation between cold water and sweaters, therefore the presence of a lurking variable must be considered. In this case, the lurking variable will be a change in temperature. The weather became hotter hence the decrease in the purchase of sweaters.

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Examples of Lurking Variables

Even though correlation does not equal causation, there is a well-known existing relationship between shark attacks and the sales of ice cream: both of them increase in the same period.

The above phrase is commonly used to show how two seemingly unrelated events can go together. Realizing these two events don’t have any cause and effect relationship will then lead to considering a third variable which is the lurking variable. 

To explain the correlation you will consider that either more people are buying ice cream thereby leading to more shark attacks or more shark attacks causing people to buy ice creams.

Now, the study has shown that neither of the hypotheses is correct. The lurking variable in this example is the weather. This is because as the weather becomes warmer or hotter people tend to buy more ice cream and then go for a swim at the beach.

A careful look at this example shows that the lurking variable (weather) was hidden even though it was responsible for the relationship between the ice cream and the sharks.

Read: Research Questions: Definitions, Types + [Examples]

Example 2

Studies show that there’s a correlation between traffic accidents and popcorn consumption. Is this because higher consumption of popcorn causes more traffic accidents or does traffic accidents cause people to consume more popcorn?

It is clear that neither of the scenarios is correct. Therefore, the third variable which is the lurking variable must be considered. This random variable will be the population. This means that as the population of people increases they consume more popcorn and the amount of traffic accidents also increases.

The cause is the lurking variable population. As the population increases, both the amount of popcorn consumed and the amount of traffic accidents increases.

Example 3

In research, findings show that the higher the number of volunteers that show up after a natural disaster, the greater the damage. Does this mean that more damages occur because of volunteers?

False. The likely cause is the size of the natural disaster. A larger natural disaster will imply that more volunteers will show up and this will cause an increase in the damage done by the natural disaster.

Read: Cross-Sectional Studies: Types, Pros, Cons & Uses

What Problems Can Lurking Variables Cause?

1. Lurking variables can falsely show a strong relationship between two variables and it can also hide the relationship existing between two variables.
2. They cause the correlation analysis or the regression analysis to mislead the researcher.
3. Lurking variables causes bias in the results of a study.
4. Lurking variables can invalidate research because they can be all across the study without being detected.

How to Identify a Lurking Variable

You must first identify lurking variables before you can eliminate or hold them constant 

One way you can identify them is through regression analysis. You can start by plotting the residuals, and then observe a trend whether linear or nonlinear, this will provide evidence to prove whether a particular variable is affecting the response variable.

Secondly, by Knowing the factors that could affect the relationship between the variables in the study (but which haven’t been included in the study) you can uncover potential lurking variables.

Explore: What’s a Longitudinal Study? Types, Uses & Examples

Difference between a Confounding and Lurking Variable?

In a lurking variable, two variables become confounded when their effects on a response or dependent variable cannot be distinguished from each other. However, the confounding variable is not only present in the study but is related to the other study variables.

This allows it to have an effect on the relationship between these variables. So, the difference between lurking and confounding variables lies in their inclusion in the study. If a variable was measured and included, you can determine the relationship between it and the explanatory and response variables and if the random assignment was performed.

If more than one variable remains unseparated, then it is a confounding variable while a lurking variable affects the value of the dependent variable in the study.

Conclusion

The lurking variable could substantially change your interpretation of the data if it were included. Including a lurking variable in our exploration may: help us to gain a deeper understanding of the relationship between variables, or lead us to rethink the direction of an association.

In designing experiments, time order should be considered and, when practical, randomized. Such randomization is not a panacea, however, since lurking variables can still be present.