ERIC Number: EJ1169011
Record Type: Journal
Publication Date: 2018-Mar
Abstractor: As Provided
Accommodating Binary and Count Variables in Mediation: A Case for Conditional Indirect Effects
Geldhof, G. John; Anthony, Katherine P.; Selig, James P.; Mendez-Luck, Carolyn A.
International Journal of Behavioral Development, v42 n2 p300-308 Mar 2018
The existence of several accessible sources has led to a proliferation of mediation models in the applied research literature. Most of these sources assume endogenous variables (e.g., M, and Y) have normally distributed residuals, precluding models of binary and/or count data. Although a growing body of literature has expanded mediation models to include more diverse data types, the nonlinearity of these models presents a substantial hurdle to their implementation and interpretation. The present study extends the existing literature (e.g., Hayes & Preacher, 2010; Stolzenberg, 1980) to propose conditional indirect effects as a useful tool for understanding mediation models that include paths estimated using the Generalized Linear Model (e.g., logistic regression, Poisson regression). We briefly review the relevant literature, culminating in a discussion of conditional indirect effects and their importance when examining nonlinear associations. We present a simple extension of the equations presented by Hayes and Preacher (2010) and provide an applied example of the technique.
Descriptors: Regression (Statistics), Statistical Analysis, Evaluation Methods, Correlation, Statistical Inference, Mexican Americans, Adults, Females, Likert Scales, Health, Behavior
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Publication Type: Journal Articles; Reports - Research
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