Communications for Statistical Applications and Methods

  • 제30권6호
  • /
  • Pages.631-652
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  • 2023
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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Identification of indirect effects in the two-condition within-subject mediation model and its implementation using SEM

  • Eujin Park (Division of Humanities, Arts, and Social Sciences: Creative Technology Management, International College, Yonsei University) ;
  • Changsoon Park (Department of Applied Mathematics and Statistics, State University of New York)
  • 투고 : 2023.10.14
  • 심사 : 2023.10.30
  • 발행 : 2023.11.30
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초록

In the two-condition within-subject mediation design, pairs of variables such as mediator and outcome are observed under two treatment conditions. The main objective of the design is to investigate the indirect effects of the condition difference (sum) on the outcome difference (sum) through the mediator difference (sum) for comparison of two treatment conditions. The natural condition variables mean the original variables, while the rotated condition variables mean the difference and the sum of two natural variables. The outcome difference (sum) is expressed as a linear model regressed on two natural (rotated) mediators as a parallel two-mediator design in two condition approaches: the natural condition approach uses regressors as the natural condition variables, while the rotated condition approach uses regressors as the rotated condition variables. In each condition approach, the total indirect effect on the outcome difference (sum) can be expressed as the sum of two individual indirect effects: within- and cross-condition indirect effects. The total indirect effects on the outcome difference (sum) for both condition approaches are the same. The invariance of the total indirect effect makes it possible to analyze the nature of two pairs of individual indirect effects induced from the natural conditions and the rotated conditions. The two-condition within-subject design is extended to the addition of a between-subject moderator. Probing of the conditional indirect effects given the moderator values is implemented by plotting the bootstrap confidence intervals of indirect effects against the moderator values. The expected indirect effect with respect to the moderator is derived to provide the overall effect of moderator on the indirect effect. The model coefficients are estimated by the structural equation modeling approach and their statistical significance is tested using the bias-corrected bootstrap confidence intervals. All procedures are evaluated using function lavaan() of package {lavaan} in R.

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