BACKGROUND: Although there is a broad consensus on the use of statistical procedures for mediation analysis in psychological research, the interpretation of the effect of mediation is highly controversial because of the potential violation of the assumptions required in application, most of which are ignored in practice. METHOD: This paper summarises two currently independent procedures for mediation analysis, the classical/SEM and causal inference/CI approaches, together with the statistical assumptions required to estimate unbiased mediation effects, in particular the existence of omitted variables or confounders. A simulation study was run to test whether violating the assumptions changes the estimation of mediating effects. RESULTS: The simulation study showed a significant overestimation of mediation effects with latent confounders. CONCLUSIONS: We recommend expanding the classical with the causal inference approach, which generalises the results of the first approach to mediation using a common estimation method and incorporates new tools to evaluate the statistical assumptions. To achieve this goal, we compare the distinguishing features of recently developed software programs in R, SAS, SPSS, STATA and Mplus.
BACKGROUND: Although there is a broad consensus on the use of statistical procedures for mediation analysis in psychological research, the interpretation of the effect of mediation is highly controversial because of the potential violation of the assumptions required in application, most of which are ignored in practice. METHOD: This paper summarises two currently independent procedures for mediation analysis, the classical/SEM and causal inference/CI approaches, together with the statistical assumptions required to estimate unbiased mediation effects, in particular the existence of omitted variables or confounders. A simulation study was run to test whether violating the assumptions changes the estimation of mediating effects. RESULTS: The simulation study showed a significant overestimation of mediation effects with latent confounders. CONCLUSIONS: We recommend expanding the classical with the causal inference approach, which generalises the results of the first approach to mediation using a common estimation method and incorporates new tools to evaluate the statistical assumptions. To achieve this goal, we compare the distinguishing features of recently developed software programs in R, SAS, SPSS, STATA and Mplus.
Authors: Carolina Ferreira-Atuesta; Nico Döhler; Barbara Erdélyi-Canavese; Ansgar Felbecker; Philip Siebel; Natalie Scherrer; Giulio Bicciato; Juliane Schweizer; Lucia Sinka; Lukas L Imbach; Mira Katan; Laura Abraira; Estevo Santamarina; José Álvarez-Sabín; Michael Winklehner; Tim J von Oertzen; Judith N Wagner; Gian Luigi Gigli; Anna Serafini; Francesco Janes; Giovanni Merlino; Mariarosaria Valente; Giorgia Gregoraci; Julian Conrad; Stefan Evers; Piergiorgio Lochner; Frauke Roell; Francesco Brigo; Carla Bentes; Ana Rita Peralta; Teresa Pinho E Melo; Mark R Keezer; John S Duncan; Josemir W Sander; Barbara Tettenborn; Matthias J Koepp; Marian Galovic Journal: Ann Neurol Date: 2021-09-30 Impact factor: 11.274