University of Illinois Urbana-Champaign

Evaluation of the split-data strategy in factor analysis

Zhou, Xinchang

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ZHOU-THESIS-2022.pdf

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https://hdl.handle.net/2142/116043

Description

Title
Evaluation of the split-data strategy in factor analysis
Author(s)
Zhou, Xinchang
Issue Date
2022-07-08
Director of Research (if dissertation) or Advisor (if thesis)
Xia, Yan
Committee Member(s)
  • Jiang, Ge
  • Zhang, Jinming
Department of Study
Educational Psychology
Discipline
Educational Psychology
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
  • confirmatory factor analysis
  • exploratory factor analysis
  • cross-validation
  • parallel analysis
  • model-data fit
Abstract
When evaluating the psychometric properties of an assessment, researchers can perform an exploratory factor analysis (EFA), followed by a confirmatory factor analysis (CFA) on the same dataset (the whole-sample strategy) to evaluate the model structure. However, the model structure obtained by the whole-sample strategy is based on only one dataset and is, therefore, subject to capitalization on chance. To strengthen the generalizability of models, researchers suggest conducting cross-validation and applying different datasets in practice. Nevertheless, because collecting multiple datasets are not always feasible in practice, researchers commonly conduct the split-data strategy by randomly splitting the dataset into two halves, performing EFA on the first half, and conducting CFA on the second half to validate the structure obtained from EFA. Despite the popularity of the split-data strategy, evidence supporting this strategy is not sufficient in the literature. To examine the utility of the split-data strategy, this thesis research includes two studies using Monte Carlo simulations to explore whether the split-data strategy has advantages over the whole-sample strategy in correctly identifying two critical aspects of model structures in psychological assessments: the number of latent factors and the existence of cross-loadings. Results show that the split-data strategy is less effective than the whole-sample strategy in evaluating the number of factors and cross-loadings in all simulation conditions. Using the split-data strategy is only acceptable, though not necessary, under conditions with large samples (greater than 1,000 for the investigated models) and good model quality (i.e., large primary loadings, no cross-loading, and small factor correlations).
Graduation Semester
2022-08
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/116043
Copyright and License Information
Copyright 2022 Xinchang Zhou

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