Date of Graduation
12-2012
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Educational Statistics and Research Methods (PhD)
Degree Level
Graduate
Department
Rehabilitation, Human Resources and Communication Disorders
Advisor/Mentor
Wen-Juo Lo
Committee Member
Ronna Turner
Second Committee Member
George Marcoulides
Third Committee Member
Sean Mulvenon
Keywords
Pure sciences, Bifactor models, Measurement invariance, Sensitivity of goodnesss-of-fit idices, Structural equation modeling
Abstract
A Monte Carlo simulation study was conducted to evaluate the sensitivities of five commonly used goodness-of-fit indices to detect metric invariance properties of the bifactor model. The fit indices that performed the best in terms of power were Gamma and Mc. In addition, Gamma, Mc, CFI, and RMSEA all held Type I error to a minimum. However, only Gamma and CFI are recommended to use in the bifactor model because the other GOF indices have cutoff values that are too large. For Gamma and CFI values of -.026 to -.045 and -.004 to -.009, respectively indicate a lack of metric invariance. In the variance component analysis, the magnitude of the factor loading differences contributed the most variation to each GOF except SRMR. For SRMR the largest contribution of variance was model complexity (i.e., simple or complex). Finally, the Arkansas Benchmark Examination data was analyzed to compare the recommended cutoff criteria for Gamma and CFI of the current study to the chi-square difference (likelihood ratio) test between configural and metric level invariance. The likelihood ratio test was consistent with Gamma and CFI for rejecting the test of metric invariance in the Arkansas Benchmark data.
Citation
Khojasteh, J. (2012). Investigating the Sensitivity of Goodness-of-fit Indices to Detect Measurement Invariance in the Bifactor Model. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/610
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