Using the Bollen-Stine Bootstrapping Method for Evaluating Approximate Fit Indices

Hanjoe Kim, Roger Millsap

Research output: Contribution to journalArticlepeer-review

43 Citations (Scopus)


Accepting that a model will not exactly fit any empirical data, global approximate fit indices quantify the degree of misfit. Recent research (Chen, Curran, Bollen, Kirby, & Paxton, 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 (2012) 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 nonnormal, 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 the Root Mean Squared Error of Approximation (RMSEA). Comparisons are made with the simulation method. The results are discussed, and suggestions are given for the use of proposed method.

Original languageEnglish
Pages (from-to)581-596
Number of pages16
JournalMultivariate Behavioral Research
Issue number6
Publication statusPublished - 2014 Nov 2

Bibliographical note

Publisher Copyright:
© 2014, Copyright © Taylor & Francis Group, LLC.

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)


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