Bias in Cross-Sectional Analyses of Longitudinal Mediation: Partial and Complete Mediation Under an Autoregressive Model
2011/09/30 by Scott E. Maxwell, David A. Cole, Melissa A. Mitchell · 1,280 citations
Mathematics · Psychology · #Advanced Causal Inference Techniques #Artificial intelligence #Autoregressive model #Computer science #Cross-sectional study #Econometrics #Interpretability #Mathematics #Mediation #Psychology #Sociology #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics #Structural equation modeling #Variety (cybernetics)
paper · pdf · doi:10.1080/00273171.2011.606716
published in Multivariate Behavioral Research 46(5), 816-841 (Taylor & Francis)
openalex publication_date 2011/09/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Abstract
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.
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