Cognitive psychology

Path analysis (statistics)

Process (computing)

Philosophy

Moderation

Statistics

Causal inference

Social psychology

Mediation

Operating system

Constraint (computer-aided design)

Economics

Independence (probability theory)

Computer science

Psychology

Mathematics

Causal model

Inference

Moderated mediation

Epistemology

Econometrics

Sociology

Path (computing)

Artificial intelligence

Geometry

Programming language

Machine learning

Social science

Advances in Methods and Practices in Psychological Science

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.

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