Author ORCID Identifier

https://orcid.org/0000-0001-6734-3268

Year of Publication

2020

Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation

College

Education

Department

Education Sciences

First Advisor

Dr. Michael D. Toland

Abstract

Bifactor confirmatory factor analysis models and statistical indices computed from them have previously been used to provide evidence for the appropriateness of utilizing a unidimensional interpretation of multidimensional data. However, the ability of bifactor indices to aid in the assessment of subscore strength has not been investigated.

A simulation study was conducted to relate bifactor indices to the strength of subscores corresponding to specific factors. The bifactor indices OmegaHS and ECVSS were found to be strongly predictive of subscore strength conditional upon OmegaS. The number of factors was also found to play a minor role in this relationship. Cutoffs for assessing the appropriateness of interpreting subscores were constructed.

The overarching goal of this work was to extend a framework for using bifactor models and their indices as diagnostic tools for dimensionality assessment. This goal is accomplished in two steps. First, a package for the R statistical computing environment was developed to enable the efficient computation of bifactor indices. Second, the aforementioned simulation study was conducted to discover relationships between bifactor indices and classical test theoretic measures of subscore strength.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2020.078

Available for download on Saturday, October 24, 2020

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