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Lingbo Tong; Wen Qu; Zhiyong Zhang – Grantee Submission, 2025
Factor analysis is widely utilized to identify latent factors underlying the observed variables. This paper presents a comprehensive comparative study of two widely used methods for determining the optimal number of factors in factor analysis, the K1 rule, and parallel analysis, along with a more recently developed method, the bass-ackward method.…
Descriptors: Factor Analysis, Monte Carlo Methods, Statistical Analysis, Sample Size
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Lee, Bitna; Sohn, Wonsook – Educational and Psychological Measurement, 2022
A Monte Carlo study was conducted to compare the performance of a level-specific (LS) fit evaluation with that of a simultaneous (SI) fit evaluation in multilevel confirmatory factor analysis (MCFA) models. We extended previous studies by examining their performance under MCFA models with different factor structures across levels. In addition,…
Descriptors: Goodness of Fit, Factor Structure, Monte Carlo Methods, Factor Analysis
Clark, D. Angus; Bowles, Ryan P. – Grantee Submission, 2018
In exploratory item factor analysis (IFA), researchers may use model fit statistics and commonly invoked fit thresholds to help determine the dimensionality of an assessment. However, these indices and thresholds may mislead as they were developed in a confirmatory framework for models with continuous, not categorical, indicators. The present…
Descriptors: Factor Analysis, Goodness of Fit, Factor Structure, Monte Carlo Methods
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Çokluk, Ömay; Koçak, Duygu – Educational Sciences: Theory and Practice, 2016
In this study, the number of factors obtained from parallel analysis, a method used for determining the number of factors in exploratory factor analysis, was compared to that of the factors obtained from eigenvalue and scree plot--two traditional methods for determining the number of factors--in terms of consistency. Parallel analysis is based on…
Descriptors: Factor Analysis, Comparative Analysis, Elementary School Teachers, Trust (Psychology)
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Asún, Rodrigo A.; Rdz-Navarro, Karina; Alvarado, Jesús M. – Sociological Methods & Research, 2016
This study compares the performance of two approaches in analysing four-point Likert rating scales with a factorial model: the classical factor analysis (FA) and the item factor analysis (IFA). For FA, maximum likelihood and weighted least squares estimations using Pearson correlation matrices among items are compared. For IFA, diagonally weighted…
Descriptors: Likert Scales, Item Analysis, Factor Analysis, Comparative Analysis
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Green, Samuel B.; Levy, Roy; Thompson, Marilyn S.; Lu, Min; Lo, Wen-Juo – Educational and Psychological Measurement, 2012
A number of psychometricians have argued for the use of parallel analysis to determine the number of factors. However, parallel analysis must be viewed at best as a heuristic approach rather than a mathematically rigorous one. The authors suggest a revision to parallel analysis that could improve its accuracy. A Monte Carlo study is conducted to…
Descriptors: Monte Carlo Methods, Factor Structure, Data Analysis, Psychometrics
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McGrath, Robert E.; Walters, Glenn D. – Psychological Methods, 2012
Statistical analyses investigating latent structure can be divided into those that estimate structural model parameters and those that detect the structural model type. The most basic distinction among structure types is between categorical (discrete) and dimensional (continuous) models. It is a common, and potentially misleading, practice to…
Descriptors: Factor Structure, Factor Analysis, Monte Carlo Methods, Computation
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Marcus, David K.; Poythress, Norman G.; Edens, John F.; Lilienfeld, Scott O. – Psychological Assessment, 2010
In "Dusky v. United States" (1960), the U.S. Supreme Court articulated 3 abilities that determine a criminal defendant's competence to stand trial: He or she must be able to consult with counsel, have a factual understanding of the proceedings, and have a rational understanding of the proceedings. Although the legal determination of a defendant's…
Descriptors: Criminals, Factor Structure, Court Litigation, Competence
Mumford, Karen R.; Ferron, John M.; Hines, Constance V.; Hogarty, Kristine Y.; Kromrey, Jeffery D. – 2003
This study compared the effectiveness of 10 methods of determining the number of factors to retain in exploratory common factor analysis. The 10 methods included the Kaiser rule and a modified Kaiser criterion, 3 variations of parallel analysis, 4 regression-based variations of the scree procedure, and the minimum average partial procedure. The…
Descriptors: Comparative Analysis, Factor Structure, Monte Carlo Methods, Simulation