Moderation analysis in two-instance repeated measures designs: Probing methods and multiple moderator models

• Published in: Behavior research methods Vol. 51; no. 1; pp. 61 - 82
• Main Author: Montoya, Amanda Kay
• Format: Journal Article
• Language: English
• Published: New York Springer US 15.02.2019; Springer Nature B.V
• Subjects: Behavioral Science and Psychology; Brief Report; Cognitive Psychology; Computer applications; Data Interpretation, Statistical; Humans; Linear Models; Multilevel Analysis; Psychology; Psychology - methods; Research Design; Within-subjects design; Probing; Repeated measures; Linear regression; Moderation; Interaction; Johnson-Neyman
• ISSN: 1554-3528; 1554-351X; 1554-3528
• DOI: 10.3758/s13428-018-1088-6

Moderation hypotheses appear in every area of psychological science, but the methods for testing and probing moderation in two-instance repeated measures designs are incomplete. This article begins with a short overview of testing and probing interactions in between-participant designs. Next I review the methods outlined in Judd, McClelland, and Smith ( Psychological Methods 1; 366–378, 1996 ) and Judd, Kenny, and McClelland ( Psychological Methods 6; 115–134, 2001 ) for estimating and conducting inference on an interaction between a repeated measures factor and a single between-participant moderator using linear regression. I extend these methods in two ways: First, the article shows how to probe interactions in a two-instance repeated measures design using both the pick-a-point approach and the Johnson–Neyman procedure. Second, I extend the models described by Judd et al. ( 1996 ) to multiple-moderator models, including additive and multiplicative moderation. Worked examples with a published dataset are included, to demonstrate the methods described throughout the article. Additionally, I demonstrate how to use Mplus and MEMORE ( Me diation and Mo deration for Re peated Measures; available at http://akmontoya.com ), an easy-to-use tool available for SPSS and SAS, to estimate and probe interactions when the focal predictor is a within-participant factor, reducing the computational burden for researchers. I describe some alternative methods of analysis, including structural equation models and multilevel models. The conclusion touches on some extensions of the methods described in the article and potentially fruitful areas of further research.