Back to Search
Start Over
Accommodating Binary and Count Variables in Mediation: A Case for Conditional Indirect Effects
- Source :
-
International Journal of Behavioral Development . Mar 2018 42(2):300-308. - Publication Year :
- 2018
-
Abstract
- 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.
Details
- Language :
- English
- ISSN :
- 0165-0254
- Volume :
- 42
- Issue :
- 2
- Database :
- ERIC
- Journal :
- International Journal of Behavioral Development
- Publication Type :
- Academic Journal
- Accession number :
- EJ1169011
- Document Type :
- Journal Articles<br />Reports - Research
- Full Text :
- https://doi.org/10.1177/0165025417727876