Bootstrap Model-Based Constrained Optimization Tests of Indirect Effects
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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AUTHORS (1)
CATEGORIES
- Psychology and Cognitive Sciences not elsewhere classified
- Applied Psychology
- Clinical Psychology
- Developmental and Educational Psychology
- Neuroscience and Physiological Psychology
- Organizational Behavioral Psychology
- Personality, Social and Criminal Psychology
- Gender Psychology
- Health, Clinical and Counselling Psychology
- Industrial and Organisational Psychology
- Psychology not elsewhere classified