Title: Dealing with Artificially Dichotomized Variables in Meta-Analytic
Structural Equation Modeling

Authors: Hannelies de Jonge, Suzanne Jak, Kees Jan Kan

Affiliation: University of Amsterdam

Abstract:
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 (ß
MX = .16, ß MX = .23, ß 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
point-biserial 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.