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

Hanjoe Kim1, Roger Millsap1

  • 1Arizona State University, Tempe, AZ.

Multivariate Behavioral Research
|January 6, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for evaluating model fit indices, using the Bollen-Stine bootstrapping procedure. This approach improves upon existing simulation methods, especially for non-normal data, to reduce decision errors in statistical modeling.

Keywords:
Bollen-Stine bootstrappingRMSEAapproximate fit indicescut-points

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Area of Science:

  • Statistics
  • Psychometrics
  • Quantitative Psychology

Background:

  • Global approximate fit indices measure model misfit in statistical modeling.
  • Conventional cut-points for fit indices can lead to errors, as shown by Chen et al. (2008).
  • Millsap's (2012) simulation method for evaluating fit indices assumes multivariate normality.

Purpose of the Study:

  • To introduce a new method for evaluating approximate fit indices.
  • To address limitations in existing simulation-based methods, particularly the assumption of normality.
  • To propose the Bollen-Stine bootstrapping procedure as a supplement for evaluating fit indices.

Main Methods:

  • Utilizing the Bollen-Stine bootstrapping procedure (1993) to evaluate approximate fit indices.
  • Comparing results from the Bollen-Stine method with Millsap's (2012) simulation method.
  • Illustrating the application of the Bollen-Stine procedure for evaluating the Root Mean Square Error of Approximation (RMSEA).

Main Results:

  • The Bollen-Stine bootstrapping procedure offers an alternative for evaluating approximate fit indices.
  • Conclusions derived from the Bollen-Stine method can differ from simulation methods when data are non-normal.
  • The proposed method provides a more robust evaluation of model fit under non-normality.

Conclusions:

  • The Bollen-Stine bootstrapping procedure is a valuable supplement for evaluating approximate fit indices, especially with non-normal data.
  • This method helps mitigate decision errors associated with fixed cut-points.
  • Recommendations are provided for the practical application of the proposed evaluation method.