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Mediation analysis with structural equation models: Combining theory, design, and statistics
Author(s) -
Danner Daniel,
Hagemann Dirk,
Fiedler Klaus
Publication year - 2015
Publication title -
european journal of social psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.609
H-Index - 111
eISSN - 1099-0992
pISSN - 0046-2772
DOI - 10.1002/ejsp.2106
Subject(s) - structural equation modeling , mediation , spurious relationship , causal model , statistical hypothesis testing , econometrics , psychology , statistical model , computer science , statistics , mathematics , artificial intelligence , machine learning , political science , law
Statistical tests of indirect effects can hardly distinguish between genuine and spurious mediation effects. The present research demonstrates, however, that mediation analysis can be improved by combining a significance test of the indirect effect with assessing the fit of causal models. Testing only the indirect effect can be misleading, because significant results may also be obtained when the underlying causal model is different from the mediation model. We use simulated data to demonstrate that additionally assessing the fit of causal models with structural equation models can be used to exclude subsets of models that are incompatible with the observed data. The results suggest that combining structural equation modeling with appropriate research design and theoretically stringent mediation analysis can improve scientific insights. Finally, we discuss limitations of the structural equation modeling approach, and we emphasize the importance of non‐statistical methods for scientific discovery.