Description
Meta-analytic structural equation modeling (MASEM) is a method to systematically synthesize results from primary studies, allowing the researchers to simultaneously examine multiple relations among variables by fitting a structural equation model to the pooled correlations. Incorporating dichotomous variables (e.g., having
a specific disease or not) into MASEM poses challenges. While primary studies that investigate the relation between a dichotomous and continuous variable typically report standardized mean differences (e.g., Cohen’s d), in the specialized MASEM software it is not possible to directly include standardized mean differences. In-
stead, MASEM typically uses correlation matrices as input. A proposed solution is to convert the standardized mean differences to point-biserial correlations. Here lies a complication because, in contrast to a standardized mean difference, the point-biserial correlation depends on the distribution of group membership. Through three Monte Carlo simulation studies, we investigated which conversion formula is suitable when one wants to include a dichotomous variable in MASEM. We varied the prevalence, sampling plan, and within-study sample sizes, and the distribution of participants over two groups. Our results show that which conversion
is suitable, and which is not depends on the aim of the meta-analyst. We have extended our freely available web application to fill the existing gap and to assist the meta-analyst with their conversions.
