Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation

Kwanghee Jung, Jaehoon Lee, Vibhuti Gupta, Gyeongcheol Cho

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Generalized structured component analysis (GSCA) is a theoretically well-founded approach to component-based structural equation modeling (SEM). This approach utilizes the bootstrap method to estimate the confidence intervals of its parameter estimates without recourse to distributional assumptions, such as multivariate normality. It currently provides the bootstrap percentile confidence intervals only. Recently, the potential usefulness of the bias-corrected and accelerated bootstrap (BCa) confidence intervals (CIs) over the percentile method has attracted attention for another component-based SEM approach—partial least squares path modeling. Thus, in this study, we implemented the BCa CI method into GSCA and conducted a rigorous simulation to evaluate the performance of three bootstrap CI methods, including percentile, BCa, and Student's t methods, in terms of coverage and balance. We found that the percentile method produced CIs closer to the desired level of coverage than the other methods, while the BCa method was less prone to imbalance than the other two methods. Study findings and implications are discussed, as well as limitations and directions for future research.

Original languageEnglish
Article number2215
JournalFrontiers in Psychology
Volume10
DOIs
StatePublished - Oct 11 2019

Keywords

  • Monte Carlo simulation
  • bootstrap methods
  • confidence intervals
  • generalized structured component analysis (GSCA)
  • structural equation modeling (SEM)

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