This is a preview. Log in to get access Show Abstract Lurking variables are important explanatory variables that might well escape attention in a routine statistical analysis. In this report several examples of lurking variables are given. Important points illustrated include the following. Careful checking, plotting, and thinking are very important. Whenever possible, data and residuals should be examined with respect to time order and spatial arrangement. A variety of plots of the data and the residuals is virtually indispensable. 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. Journal Information The American Statistician strives to publish articles of general interest to the statistical profession on topics that are important for a broad group of statisticians, and ordinarily not highly technical. The journal is organized into sections: Statistical Practice, General, Teacher's Corner, Statistical Computing and Graphics, Reviews of Books and Teaching Materials, and Letters. Publisher Information Building on two centuries' experience, Taylor & Francis has grown rapidlyover the last two decades to become a leading international academic publisher.The Group publishes over 800 journals and over 1,800 new books each year, coveringa wide variety of subject areas and incorporating the journal imprints of Routledge,Carfax, Spon Press, Psychology Press, Martin Dunitz, and Taylor & Francis.Taylor & Francis is fully committed to the publication and dissemination of scholarly information of the highest quality, and today this remains the primary goal. Rights & Usage This item is part of a JSTOR Collection. A lurking variable is a variable that is not included in a statistical analysis, yet impacts the relationship between two variables within the analysis. A lurking variable can hide the true relationship between variables or it can falsely cause a relationship to appear to be present between variables. Essentially, lurking variables can cause the results of a study to be misleading. In observational studies, it’s important to be aware of the fact that lurking variables could cause unusual interpretations of data and the relationships between variables. In experimental studies, it’s important to design the experiment in such a way that (as much as possible) eliminates the risk of lurking variables. The following examples illustrate several cases in which lurking variables could be present in a study: Example 1A researcher finds that ice cream sales and shark attacks are highly positively correlated. Does this mean that increased ice cream sales is causing more shark attacks? That’s unlikely. The more likely cause is the lurking variable weather. When it is warmer outside, more people buy ice cream and more people go in the ocean.  Example 2A researcher finds that popcorn consumption and the amount of traffic accidents over the years is highly correlated. Does this mean that higher popcorn consumption is causing more traffic accidents? That’s unlikely. The more likely cause is the lurking variable population. As the population increases, both the amount of popcorn consumed and the amount of traffic accidents increases. Example 3A study finds that the more volunteers that show up after a natural disaster, the greater the damage. Does this mean that volunteers are causing more damage to occur? That’s unlikely. The more likely cause is the lurking variable size of the natural disaster. A larger natural disaster causes more volunteers to show up as well as an increase in the amount of damage done by the natural disaster. Example 4A study finds that glove sales and snowboarding accidents are highly correlated. Does this mean that gloves are causing more snowboard accidents to occur? That’s unlikely. The more likely cause is the lurking variable temperature. As temperature decreases, more people buy gloves and more people go snowboarding. How to Identify Lurking VariablesTo discover lurking variables, it helps to have domain expertise in the area under study. By knowing what potential variables could be affecting the relationship between the variables in the study that aren’t included explicitly in the study, you may be able to uncover potential lurking variables. Another way to identify potential lurking variables is through examining residual plots. If there is a trend (either linear or non-linear) in the residuals, this could mean that a lurking variable not included in the study is impacting the variables within the study in some way. How to Eliminate the Risk of Lurking VariablesIn observational studies, it can be very difficult to eliminate the risk of lurking variables. In most cases, the best you can do is simply identify, rather then prevent, potential lurking variables that may be impacting the study. In experimental studies, however, the impact of lurking variables can mostly be eliminated with good experimental design. For example, suppose we want to know whether two pills have a different impact on blood pressure. We know that lurking variables such as diet and smoking habits also impact blood pressure, so we can attempt to control for these lurking variables by using a randomized design. This means we randomly assign patients to take either the first or second pill. Since we randomly assign patients to groups, we can assume that the lurking variables will affect both groups roughly equally. This means any differences in blood pressure can be attributed to the pill, rather than the effect of a lurking variable. Is it likely that lurking variables impacted the results of this study explain?Lurking variables can falsely show a strong relationship between two variables and it can also hide the relationship existing between two variables. They cause the correlation analysis or the regression analysis to mislead the researcher. Lurking variables causes bias in the results of a study.
What does the lurking variable affect?A lurking variable is a variable that is not included as an explanatory or response variable in the analysis but can affect the interpretation of relationships between variables. A lurking variable can falsely identify a strong relationship between variables or it can hide the true relationship.
What is a lurking variable in an experiment?Lurking variable. A variable that is neither the explanatory variable nor the response variable but has a relationship (e.g. may be correlated) with the response and the explanatory variable. It is not considered in the study but could influence the relationship between the variables in the study.
Does a lurking variable affects only the response variable?A lurking variable is a variable that is not measured in the study. It is a third variable that is neither the explanatory nor the response variable, but it affects your interpretation of the relationship between the explanatory and response variable.
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