Within-subject mediation analysis for experimental data in cognitive psychology and neuroscience

• Published in: Behavior research methods Vol. 50; no. 5; pp. 2125 - 2143
• Main Authors: Vuorre, Matti, Bolger, Niall
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2018; Springer Nature B.V
• Subjects: Bayes Theorem; Bayesian analysis; Bayesian Statistics; Behavioral Science and Psychology; Between-subjects design; Causal Models; Causality; Cognitive ability; Cognitive Psychology; Data Interpretation, Statistical; Data processing; Experiments; Hierarchical Linear Modeling; Humans; Hypothesis Testing; Mathematical models; Mediation; Models, Statistical; Nervous system; Neurosciences; Neurosciences - methods; Psychology; Psychology - methods; Software; Statistical Analysis; Within-subjects design; Repeated measures; Multilevel analysis; Bayesian statistics; Mediation; Causal mechanism
• ISSN: 1554-3528; 1554-3528
• DOI: 10.3758/s13428-017-0980-9

Statistical mediation allows researchers to investigate potential causal effects of experimental manipulations through intervening variables. It is a powerful tool for assessing the presence and strength of postulated causal mechanisms. Although mediation is used in certain areas of psychology, it is rarely applied in cognitive psychology and neuroscience. One reason for the scarcity of applications is that these areas of psychology commonly employ within-subjects designs, and mediation models for within-subjects data are considerably more complicated than for between-subjects data. Here, we draw attention to the importance and ubiquity of mediational hypotheses in within-subjects designs, and we present a general and flexible software package for conducting Bayesian within-subjects mediation analyses in the R programming environment. We use experimental data from cognitive psychology to illustrate the benefits of within-subject mediation for theory testing and comparison.