Errors-in-variables regression as a viable approach to mediation analysis with random error-tainted measurements:...

Mediation analysis, popular in many disciplines that rely on behavioral science data analysis techniques, is often conducted using ordinary least squares (OLS) regression analysis methods. Given that one of OLS regression's weaknesses is its susceptibility to estimation bias resulting from unaccounted-for random measurement error in variables on the right-hand sides of the equation, many published mediation analyses certainly contain some and perhaps substantial bias in the direct, indirect, and total effects. In this manuscript, we offer errors-in-variables (EIV) regression as an easy-to-use alternative when a researcher has reasonable estimates of the reliability of the variables in the analysis. In three real-data examples, we show that EIV regression-based mediation analysis produces estimates that are equivalent to those obtained using an alternative, more analytically complex approach that accounts for measurement error-single-indicator latent variable structural equation modeling-yet quite different from the results generated by standard OLS regression that ignores random measurement error. In a small-scale simulation, we also establish that EIV regression successfully recovers the parameters of a mediation model involving variables adulterated by random measurement error while OLS regression generates biased estimates. To facilitate the adoption of EIV regression, we describe an implementation in the PROCESS macro for SPSS, SAS, and R that we believe eliminates most any excuse one can conjure for not accounting for random measurement error when conducting a mediation analysis.