Evaluation of the split-data strategy in factor analysis

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).

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Educational Psychology

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Zhou, Xinchang

Contributors dc:contributor

• Xia, Yan
• Jiang, Ge
• Zhang, Jinming

• cross-validation
• parallel analysis
• model-data fit
• confirmatory factor analysis
• exploratory factor analysis

Statement dc:rights

• Copyright 2022 Xinchang Zhou

Language dc:language

en, eng

Handle dc:identifier

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

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F116043