2009
DOI: 10.1177/1536867x0900900207
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Implementing Horn's Parallel Analysis for Principal Component Analysis and Factor Analysis

Abstract: I present paran, an implementation of Horn's parallel analysis criteria for factor or component retention in common factor analysis or principal component analysis in Stata. The command permits classical parallel analysis and more recent extensions to it for the pca and factor commands. paran provides a needed extension to Stata's built-in factor-and component-retention criteria.

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Cited by 200 publications
(170 citation statements)
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“…In addition to Kaiser's eigenvalue of 1 criterion and examination of the scree plot, Horn's parallel analysis with Glorfeld's Monte Carlo extension (Dinno, 2009;Glorfeld, 1995;Horn, 1965) was used to identify the optimal number of factors to retain for the final factor solution. Through generation of a large number of random data sets based on the number of subjects and variables of the target data set, parallel analysis compares the eigenvalues from the random data sets with the target data set to determine the optimal number of factors to retain.…”
Section: Ata a Nalysismentioning
confidence: 99%
“…In addition to Kaiser's eigenvalue of 1 criterion and examination of the scree plot, Horn's parallel analysis with Glorfeld's Monte Carlo extension (Dinno, 2009;Glorfeld, 1995;Horn, 1965) was used to identify the optimal number of factors to retain for the final factor solution. Through generation of a large number of random data sets based on the number of subjects and variables of the target data set, parallel analysis compares the eigenvalues from the random data sets with the target data set to determine the optimal number of factors to retain.…”
Section: Ata a Nalysismentioning
confidence: 99%
“…We applied two decision rules to determine whether there was sufficient evidence for combining survey items into a composite index including a Kaiser-Meyer-Olkin Measure and a Bartlett’s Test of Sphericity [29]. We conducted a parallel analysis test to determine the number of factors to retain by comparing the observed eigenvalues extracted from the correlation matrix analyzed with those obtained from uncorrelated normal variables [30]. Based on the results, we retained one factor.…”
Section: Methodsmentioning
confidence: 99%
“…To determine the factors underlying ratings, we conducted principal-components analyses 1 using the principal procedure (Revelle, 2009) from the R statistics package (R Development Core Team, 2008) and determined the number of components by examining the scree plot and by using R's paran function (Dinno, 2008) to conduct a parallel analysis (Horn, 1965). We then used a varimax rotation to obtain orthogonal components and a promax procedure to obtain oblique components.…”
Section: Methodsmentioning
confidence: 99%