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

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 C

SUBMITTER: Jung K 

PROVIDER: S-EPMC6797821 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature