Computing Robust, Bootstrap-Adjusted Fit Indices for Use With Nonnormal Data

ABSTRACT

Nonnormality of data presents unique challenges for researchers who wish to carry out structural equation modeling. The subsequent SPSS syntax program computes bootstrap-adjusted fit indices (comparative fit index, Tucker–Lewis index, incremental fit index, and root mean square error of approximation) that adjust for nonnormality, along with the Bollen–Stine bootstrap-adjusted χ² equivalent statistic and associated scaling factor.

KEYWORDS:

• Confirmatory factor analysis
• goodness-of-fit index
• model fit
• structural equation modeling

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Additional information

Notes on contributors

David A. Walker

David A. Walker is a professor of Educational Research and Assessment at Northern Illinois University. His research interests include mathematical statistics, effect sizes, structural analyses, and predictive analyses.

Thomas J. Smith

Thomas j. Smith is a professor of Education Research and Assessment at Northern Illinois University. His research interests include statistics, research methodology, and large-scale data analysis.