Dealing With Artificially Dichotomized Variables in Meta-Analytic Structural Equation Modeling

Meta-analytic structural equation modeling (MASEM) is a relatively new method in which effect sizes of different independent studies between multiple variables are typically first pooled into a matrix and next analyzed using structural equation modeling. While its popularity is increasing, there are issues still to be resolved, such as how to deal with primary studies in which variables have been artificially dichotomized. To be able to advise researchers who apply MASEM and need to deal with this issue, we performed two simulation studies using random-effects two stage structural equation modeling. We simulated data according to a full and partial mediation model and systematically varied the size of one (standardized) path coefficient (beta(MX) = .16, beta(MX) = .23, beta(MX) =.33), the percentage of dichotomization (25%, 75%, 100%), and the cut-off point of dichotomization (.5,.1). We analyzed the simulated datasets in two different ways, namely, by using (1) the pointbiserial and (2) the biserial correlation as effect size between the artificially dichotomized predictor and continuous variables. The results of these simulation studies indicate that the biserial correlation is the most appropriate effect size to use, as it provides unbiased estimates of the path coefficients in the population.