Personality Moderating Variables: A Warning about Statistical Artifact and a Comparison of Analytic Techniques

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Abstract
Personality moderating variables act to qualify the relationship between a personality trait measure and a relevant behavioral criterion. Two data analytic techniques that can be used to test for significant moderating effects are the “median split” (MS) approach and the “moderated multiple regression” (MMR) approach. The goals of the present research were (a) to apply the MS approach to computer‐simulated data in which the moderator and trait extremity are confounded, to determine the extent of artifact, and (b) to compare the performance (Type I and Type II error rates) of the two approaches when applied to confounded and nonconfounded data. It was found that when the MS approach was applied to confounded data in which no real moderating effect existed, this approach produced an alarming rate of apparent, but spurious, moderating effects. When the MMR approach was applied to the same data, the rate of spurious effects was reduced to that expected by chance. When both approaches were applied to simulated data which contained genuine moderating effects, the MMR approach consistently resulted in more correct detections of these effects than the MS approach. We conclude that researchers should always employ the MMR rather than the MS approach when testing for personality moderator variable effects.