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Factor Extraction in Exploratory Factor Analysis for Ordinal Indicators: Is Principal Component Analysis the Best Option?

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Title: Factor Extraction in Exploratory Factor Analysis for Ordinal Indicators: Is Principal Component Analysis the Best Option?
Language: English
Authors: Tugay Kaçak (ORCID 0000-0002-5319-7148), Abdullah Faruk Kiliç (ORCID 0000-0003-3129-1763)
Source: International Journal of Assessment Tools in Education. 2025 12(1):113-130.
Availability: International Journal of Assessment Tools in Education. Pamukkale University, Faculty of Education, Kinikli Campus, Denizli 20070, Turkey. e-mail: ijate.editor@gmail.com; Web site: https://dergipark.org.tr/en/pub/ijate
Peer Reviewed: Y
Page Count: 18
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Factor Analysis, Monte Carlo Methods, Mathematical Models, Sample Size, Evaluation Methods, Research Methodology, Simulation
ISSN: 2148-7456
Abstract: Researchers continue to choose PCA in scale development and adaptation studies because it is the default setting and overestimates measurement quality. When PCA is utilized in investigations, the explained variance and factor loadings can be exaggerated. PCA, in contrast to the models given in the literature, should be investigated in categorical/ordered, severely skewed data, and multidimensional structures. The purpose of this study is to compare the relative bias and percent correct estimation of PCA, PAF, and MINRES techniques with Monte Carlo simulations. In Monte Carlo simulations sample size, level of skewness, number of categories, average factor loadings, number of factors, level of inter-factor correlation and test length were manipulated. The results show that PCA overestimates most models with lower average factor loadings, but PAF and MINRES provide unbiased results even with low factor loadings. PAF and MINRES produce more accurate and impartial results, and it is projected that PCA will lead researchers to believe that the items in scale development or adaptation studies are of "high quality."
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1463499
Database: ERIC
FullText Text:
  Availability: 0
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  Data: Factor Extraction in Exploratory Factor Analysis for Ordinal Indicators: Is Principal Component Analysis the Best Option?
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  Data: <searchLink fieldCode="AR" term="%22Tugay+Kaçak%22">Tugay Kaçak</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-5319-7148">0000-0002-5319-7148</externalLink>)<br /><searchLink fieldCode="AR" term="%22Abdullah+Faruk+Kiliç%22">Abdullah Faruk Kiliç</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3129-1763">0000-0003-3129-1763</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Assessment+Tools+in+Education%22"><i>International Journal of Assessment Tools in Education</i></searchLink>. 2025 12(1):113-130.
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  Data: International Journal of Assessment Tools in Education. Pamukkale University, Faculty of Education, Kinikli Campus, Denizli 20070, Turkey. e-mail: ijate.editor@gmail.com; Web site: https://dergipark.org.tr/en/pub/ijate
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  Data: Researchers continue to choose PCA in scale development and adaptation studies because it is the default setting and overestimates measurement quality. When PCA is utilized in investigations, the explained variance and factor loadings can be exaggerated. PCA, in contrast to the models given in the literature, should be investigated in categorical/ordered, severely skewed data, and multidimensional structures. The purpose of this study is to compare the relative bias and percent correct estimation of PCA, PAF, and MINRES techniques with Monte Carlo simulations. In Monte Carlo simulations sample size, level of skewness, number of categories, average factor loadings, number of factors, level of inter-factor correlation and test length were manipulated. The results show that PCA overestimates most models with lower average factor loadings, but PAF and MINRES provide unbiased results even with low factor loadings. PAF and MINRES produce more accurate and impartial results, and it is projected that PCA will lead researchers to believe that the items in scale development or adaptation studies are of "high quality."
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      – Text: English
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      Pagination:
        PageCount: 18
        StartPage: 113
    Subjects:
      – SubjectFull: Factor Analysis
        Type: general
      – SubjectFull: Monte Carlo Methods
        Type: general
      – SubjectFull: Mathematical Models
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      – SubjectFull: Sample Size
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      – SubjectFull: Evaluation Methods
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      – SubjectFull: Research Methodology
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      – SubjectFull: Simulation
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      – TitleFull: Factor Extraction in Exploratory Factor Analysis for Ordinal Indicators: Is Principal Component Analysis the Best Option?
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              Y: 2025
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