Bias in Cross-Sectional Analyses of Longitudinal Mediation: Partial and Complete Mediation Under an Autoregressive Model

• Published in: Multivariate behavioral research Vol. 46; no. 5; pp. 816 - 841
• Main Authors: Maxwell, Scott E., Cole, David A., Mitchell, Melissa A.
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis Group 30.09.2011; Psychology Press; Taylor & Francis Ltd
• Subjects: Bias; Case Studies; Child Health; Correlation analysis; Depression (Psychology); Estimation bias; Evaluation Research; Mediation; Mediation Theory; Mothers; Psychological Studies; Research Methodology; Structural Equation Models
• ISSN: 0027-3171; 1532-7906
• DOI: 10.1080/00273171.2011.606716

Maxwell and Cole (2007) showed that cross-sectional approaches to mediation typically generate substantially biased estimates of longitudinal parameters in the special case of complete mediation. However, their results did not apply to the more typical case of partial mediation. We extend their previous work by showing that substantial bias can also occur with partial mediation. In particular, cross-sectional analyses can imply the existence of a substantial indirect effect even when the true longitudinal indirect effect is zero. Thus, a variable that is found to be a strong mediator in a cross-sectional analysis may not be a mediator at all in a longitudinal analysis. In addition, we show that very different combinations of longitudinal parameter values can lead to essentially identical cross-sectional correlations, raising serious questions about the interpretability of cross-sectional mediation data. More generally, researchers are encouraged to consider a wide variety of possible mediation models beyond simple cross-sectional models, including but not restricted to autoregressive models of change.