That's a Lot to Process! Pitfalls of Popular Path Models

Julia M. Rohrer*, Paul Hünermund, Ruben C. Arslan, Malte Elson

*Corresponding author for this work

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Path models to test claims about mediation and moderation are a staple of psychology. But applied researchers may sometimes not understand the underlying causal inference problems and thus endorse conclusions that rest on unrealistic assumptions. In this article, we aim to provide a clear explanation for the limited conditions under which standard procedures for mediation and moderation analysis can succeed. We discuss why reversing arrows or comparing model fit indices cannot tell us which model is the right one and how tests of conditional independence can at least tell us where our model goes wrong. Causal modeling practices in psychology are far from optimal but may be kept alive by domain norms that demand every article makes some novel claim about processes and boundary conditions. We end with a vision for a different research culture in which causal inference is pursued in a much slower, more deliberate, and collaborative manner.
Original languageEnglish
Article number25152459221095827
JournalAdvances in Methods and Practices in Psychological Science
Issue number2
Number of pages14
Publication statusPublished - Apr 2022

Bibliographical note

Published online: July 28, 2022.


  • Casual modeling
  • Structural equation modeling
  • SEM
  • Mediation
  • Moderation

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