2026-09-20T18:35:23Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/116043 2023-07-11 col_2142_5131 col_2142_11614 com_2142_5130 com_2142_11613 com_2142_8903

Xia, Yan Jiang, Ge Zhang, Jinming 2022-08 application/pdf en text Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-08-01 The student, Xinchang Zhou, accepted the attached license on 2022-06-30 at 10:29. The student, Xinchang Zhou, submitted this Thesis for approval on 2022-06-30 at 10:37. This Thesis was approved for publication on 2022-07-08 at 13:54. DSpace SAF Submission Ingestion Package generated from Vireo submission #18121 on 2022-11-15 at 19:16:51 Evaluation of the split-data strategy in factor analysis Zhou, Xinchang 2022-07-08 cross-validation parallel analysis model-data fit confirmatory factor analysis exploratory 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). Thesis eng https://hdl.handle.net/2142/116043 Copyright 2022 Xinchang Zhou M.S. Thesis Educational Psychology University of Illinois at Urbana-Champaign Educational Psychology