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Exploring the Sensitivity of Horn's Parallel Analysis to the Distributional Form of Random Data

Dados Bibliográficos

ID19289751
AutoresAlexis Dinno (0000-0003-2560-310X, a Center for Tobacco Control Research and Education , University of California , San Francisco, autor correspondente)
Ano2009
Volume44
Fascículo3
Páginas362-388
Data de publicação2009-06-22
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoMultivariate Behavioral Research (JOURNAL)
Identificadores do periódicoISSN: 0027-3171 • E-ISSN: 1532-7906
EditoraInforma UK Limited (PUBLISHER • GB)
DOI10.1080/00273170902938969
PMID20234802
PMCIDPMC2838619
OpenAlexW2093636276
IdiomaEN
Citações recebidas43
Referências citadas32

Horn's parallel analysis (PA) is the method of consensus in the literature on empirical methods for deciding how many components/factors to retain. Different authors have proposed various implementations of PA. Horn's seminal 1965 article, a 1996 article by Thompson and Daniel, and a 2004 article by Hayton, Allen, and Scarpello all make assertions about the requisite distributional forms of the random data generated for use in PA. Readily available software is used to test whether the results of PA are sensitive to several distributional prescriptions in the literature regarding the rank, normality, mean, variance, and range of simulated data on a portion of the National Comorbidity Survey Replication (Pennell et al., 2004) by varying the distributions in each PA. The results of PA were found not to vary by distributional assumption. The conclusion is that PA may be reliably performed with the computationally simplest distributional assumptions about the simulated data

Combinatorics · Econometrics · Normality · Range (aeronautics) · Rank (graph theory) · Replication (statistics) · Sensitivity (control systems) · Statistics · Variance (accounting) · Advanced Statistical Modeling Techniques · Computer Science · Genetics and Plant Breeding · Mathematics · Statistical Methods and Bayesian Inference

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