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• W2139156375 abstract "Previous research evaluating the performance of Horn's parallel analysis (PA) factor retention method with ordinal variables has produced unexpected findings. Specifically, PA with Pearson correlations has performed as well as or better than PA with the more theoretically appropriate polychoric correlations. Seeking to clarify these findings, the current study employed a more comprehensive simulation study that included the systematic manipulation of 7 factors related to the data (sample size, factor loading, number of variables per factor, number of factors, factor correlation, number of response categories, and skewness) as well as 3 factors related to the PA method (type of correlation matrix, extraction method, and eigenvalue percentile). The results from the simulation study show that PA with either Pearson or polychoric correlations is particularly sensitive to the sample size, factor loadings, number of variables per factor, and factor correlations. However, whereas PA with polychorics is relatively robust to the skewness of the ordinal variables, PA with Pearson correlations frequently retains difficulty factors and is generally inaccurate with large levels of skewness. In light of these findings, we recommend the use of PA with polychoric correlations for the dimensionality assessment of ordinal-level data." @default.
• W2139156375 created "2016-06-24" @default.
• W2139156375 creator A5028636802 @default.
• W2139156375 creator A5031662619 @default.
• W2139156375 creator A5054211533 @default.
• W2139156375 date "2013-12-01" @default.
• W2139156375 modified "2023-09-25" @default.
• W2139156375 title "A new look at Horn’s parallel analysis with ordinal variables." @default.
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• W2139156375 doi "https://doi.org/10.1037/a0030005" @default.
• W2139156375 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/23046000" @default.
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