Sample Size Requirements for Simple and Complex Mediation Models

Mikyung Sim, Su Young Kim, Youngsuk Suh

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Mediation models have been widely used in many disciplines to better understand the underlying processes between independent and dependent variables. Despite their popularity and importance, the appropriate sample sizes for estimating those models are not well known. Although several approaches (such as Monte Carlo methods) exist, applied researchers tend to use insufficient sample sizes to estimate their models of interest, which might result in unstable and inaccurate estimation of the model parameters including mediation effects. In the present study, sample size requirements were investigated for four frequently used mediation models: one simple mediation model and three complex mediation models. For each model, path and structural equation modeling approaches were examined, and partial and complete mediation conditions were considered. Both the percentile bootstrap method and the multivariate delta method were compared for testing mediation effects. A series of Monte Carlo simulations was conducted under various simulation conditions, including those concerning the level of effect sizes, the number of indicators, the magnitude of factor loadings, and the proportion of missing data. The results not only present practical and general guidelines for substantive researchers to determine minimum required sample sizes but also improve understanding of which factors are related to sample size requirements in mediation models.

Original languageEnglish
Pages (from-to)76-106
Number of pages31
JournalEducational and Psychological Measurement
Volume82
Issue number1
DOIs
StatePublished - Feb 2022

Keywords

  • bootstrap method
  • indirect effect
  • mediation analysis
  • mediation model
  • sample size

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