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Bootstrap Model-Based Constrained Optimization Tests of Indirect Effects

Authors
  • Tofighi, Davood
Type
Published Article
Journal
Frontiers in Psychology
Publisher
Frontiers Media SA
Publication Date
Jan 20, 2020
Volume
10
Identifiers
DOI: 10.3389/fpsyg.2019.02989
PMID: 32038377
PMCID: PMC6984355
Source
PubMed Central
Keywords
License
Unknown

Abstract

In mediation analysis, conditions necessary for commonly recommended tests, including the confidence interval (CI)-based tests, to produce an accurate Type I error, do not generally hold for finite sample sizes and non-normally distributed model residuals. This is typically the case because of the complexity of testing a null hypothesis about indirect effects. To remedy these issues, we propose two extensions of the recently developed asymptotic Model-based Constrained Optimization (MBCO) likelihood ratio test (LRT), a promising new model comparison method for testing a general function of indirect effects. The proposed tests, semi-parametric and parametric bootstrap MBCO LRT are shown to yield a more accurate Type I error rate in smaller sample sizes and under various degrees of non-normality of the model residuals compared to the asymptotic MBCO LRT and the CI-based methods. We provide R script in the Supplemental Materials to perform all three MBCO LRTs.

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