Selecting a Within- or Between-Subject Design for Mediation: Validity, Causality, and Statistical Power

Researchers with mediation hypotheses must consider which design to use: within-subject or between-subject? In this paper, I argue that three factors should influence design choice: validity, causality, and statistical power. Threats to validity include carry-over effects, participant awareness, mea...

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Veröffentlicht in:Multivariate behavioral research Jg. 58; H. 3; S. 616 - 636
1. Verfasser: Montoya, Amanda K.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Routledge 04.05.2023
Taylor & Francis Ltd
Schlagworte:
causal inference
Causality
Design factors
Empirical analysis
indirect effect
Mediation analysis
Monte Carlo simulation
power
power analysis
Statistical power
type I error
Validity
within-subject design
indirect effect
power
validity
within-subject design
causal inference
Mediation analysis
Monte Carlo simulation
type I error
power analysis
ISSN:0027-3171, 1532-7906, 1532-7906
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Zusammenfassung:Researchers with mediation hypotheses must consider which design to use: within-subject or between-subject? In this paper, I argue that three factors should influence design choice: validity, causality, and statistical power. Threats to validity include carry-over effects, participant awareness, measurement, and more. Causality is a core element of mediation, and the assumptions required for causal inference differ between the two designs. Between-subject designs require more restrictive no-confounder assumptions, but within-subject designs require the assumption of no carry-over effects. Statistical power should be higher in within-subject designs, but the degree and conditions of this advantage are unknown for mediation analysis. A Monte Carlo simulation compares designs under a broad range of sample sizes, effect sizes, and correlations among repeated measurements. The results show within-subject designs require about half the sample size of between-subject designs to detect indirect effects of the same size, but this difference can vary with population parameters. I provide an empirical example and R script for conducting power analysis for within-subject mediation analysis. Researchers interested in conducting mediation analysis should not select within-subject designs merely because of higher power, but they should also consider validity and causality in their decision, both of which can favor between-subject designs.
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ISSN:0027-3171
1532-7906
1532-7906
DOI:10.1080/00273171.2022.2077287