title: Using the Bollen-Stine Bootstrapping Method for Evaluating Approximate Fit Indices.
abstract: Accepting that a model will not exactly fit any empirical data, global approximate fit indices quantify the degree of misfit. Recent research (Chen et al., 2008) has shown that using fixed conventional cut-points for approximate fit indices can lead to decision errors. Instead of using fixed cut-points for evaluating approximate fit indices, this study focuses on the meaning of approximate fit and introduces a new method to evaluate approximate fit indices. Millsap (2012) introduced a simulation-based method to evaluate approximate fit indices. A limitation of Millsap's work was that a rather strong assumption of multivariate normality was implied in generating simulation data. In this study, the Bollen-Stine bootstrapping procedure (Bollen & Stine, 1993) is proposed to supplement the former study. When data are non-normal, the conclusions derived from Millsap's (2012) simulation method and the Bollen-Stine method can differ. Examples are given to illustrate the use of the Bollen-Stine bootstrapping procedure for evaluating RMSEA. Comparisons are made with the simulation method. The results are discussed, and suggestions are given for the use of proposed method.
authors.affiliation: Arizona State University, Tempe, AZ.
authors.forename: Hanjoe
authors.initials: H
authors.lastname: Kim
authors.affiliation: Arizona State University, Tempe, AZ.
authors.forename: Roger
authors.initials: R
authors.lastname: Millsap
keywords: Bollen-Stine bootstrapping; RMSEA; approximate fit indices; cut-points