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Lurking Variable

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A lurking variable in statistics is a “hidden” variable that we have failed to consider in our model but which has an effect on the response variable. The main reason for the presence of lurking variables is that the researcher fails to identify all the variables that cause changes to occur in the response variable.

Some examples of situations where lurking variables may arise are:

  1. Suppose a researcher develops a linear regression model for the height of a person where the diet, sex, lifestyle, etc. are taken to be the explanatory variables. In such a case genetical factors may be the lurking variable which the researcher has failed to identify.
  2. In a linear model for modelling the blood pressure of patients, a researcher takes the explanatory variables to be age, diet and heart rate. Here the sex of the patient may turn out to be the lurking variable.
  3. In a linear model for the growth rate of crops the type of fertilizer and quality of soil are taken to be the explanatory variables. In such a case the weather may turn out to be the hidden variable.
Lurking Variable Example
Lurking Variable Example

Lurking variable and confounding variable:

The main difference between the lurking variable and confounding variable is that whereas the lurking variable has an effect on the response variable only, the confounding variable may affect both the dependent as well as the independent variable in our model.

Of course, the main similarity between these two variables is that both of them affect the end result of our model even though they are not included in it as explanatory variables.

How to identify lurking variables?

Lurking variables can be identified by the researcher by analyzing the error in our model. If the errors appear to be distributed randomly, obeying the normal distribution then it indicates the absence of a lurking variable. On the other hand, a non-random error pattern indicates the presence of lurking variables.

The researcher may also use his knowledge of the causation involved in the situation. As seen in the first example above, it is plausible that genetic factors may have an effect on the height of a person.

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