Lurking Variables in Data Analysis: Definitions and Examples

Lurking Variables in Data Analysis: Definitions and Examples
Photo by Cameron Witney

A lurking variable is a variable that is absent from a statistical analysis but influences the connection between two variables inside the study.

A lurking variable can conceal the genuine link between variables or create a misleading relationship to emerge between variables. In essence, lurking factors might cause a study’s conclusions to be deceptive.

In observational research, it is essential to be conscious of the fact that lurking (hidden) factors may lead to erroneous data interpretations and correlations between variables. In experimental investigations, it is essential to construct the experiment so that (as much as feasible) the danger of hiding variables is eliminated.

Examples of lurking variables

The following instances highlight numerous instances in which lurking variables may exist in a study:

Example 1
A researcher discovers a significant correlation between ice cream sales and shark attacks. Does this imply that a rise in ice cream sales has led to an increase in shark attacks?

That’s improbable. The fluctuating weather is the most likely explanation. When the weather gets warmer, more people purchase ice cream and swim in the water.

Example 2
A researcher discovers a strong correlation between popcorn intake and the number of traffic accidents over the years. Does this imply that increased popcorn intake causes an increase in road accidents?

That’s improbable. The lurking variable population is the most likely reason. Both the amount of popcorn consumed and the number of traffic accidents rise as the population grows.

Example 3
A study reveals that the larger the damage following a natural catastrophe, the greater the number of volunteers that respond. Does this indicate that volunteers are inflicting greater harm?

That’s improbable. The most probable explanation is the varying scale of the impending natural disaster. A greater natural catastrophe increases both the number of volunteers and the amount of damage caused by the disaster.

Example 4
A study reveals that snowboarding accidents and glove sales are closely connected. Does this imply that gloves contribute to an increase in snowboarding accidents?

That’s improbable. The changing temperature is the most likely explanation. As the weather drops, more people purchase gloves and go skiing.

How to Locate Lurking Variables

To uncover lurking factors, domain experience in the subject of investigation is advantageous. You may be able to identify factors hiding in the shadows if you know what potential variables may be influencing the association between the variables in the research that are not included explicitly in the study.

Examining residual plots is another method for identifying possible variables in hiding. If there is a trend (either linear or non-linear) in the residuals, this might indicate that an unaccounted-for variable is influencing the variables inside the research.

How to Remove the Danger of Lurking Variables
In observational studies, it can be extremely challenging to exclude the possibility of hidden factors. In most instances, the most that can be done is to identify prospective variables that may influence the study, as opposed to preventing them.

However, with proper experimental design, the influence of lurking factors may be substantially reduced in experimental investigations.

Suppose, for instance, that we wish to determine whether two medicines have distinct effects on blood pressure. We are aware that factors such as food and smoking affect blood pressure, therefore we may attempt to control for these variables by employing a randomized design. This implies that patients are randomly assigned the first or second medication.

Since patients are assigned to groups at random, it is reasonable to expect that the hidden factors will effect both groups about equally. This indicates that any variations in blood pressure may be traced to the medication, as opposed to a hidden variable.