Determining the Number of Factors To Retain.

By Jesus Tanguma. 2000

129 citations recorded by the source corpus.

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Citations in the corpus

Investigating the Effects of Moral Disengagement and Participation on Unethical Work Behavior (2011, methodology): “The approach used to select factors for rotation was parallel analysis, which estimates what eigenvalues one would expect by chance alone given a specified number of items and participants (see Kaufman & Dunlap, 2000).”
Social vulnerability indices: a comparative assessment using uncertainty and sensitivity analysis (2012, methodology): “The capability for parallel analysis with PCA is currently lacking from most of the popular statistical packages, so a web-based utility for parallel analysis was employed (Patil et al. 2007).”
Leadership effects on innovation propensity: A two-factor full range leadership model (2013, methodology): “Horn's (1965) parallel analysis determines the number of factors to retain by comparing observed eigenvalues extracted from the correlation matrix with those obtained from randomly generated uncorrelated normal variables (Ledesma & Valero-Mora, 2007).”
Parallel Analysis with Unidimensional Binary Data (2005, methodology): “…have not included parallel analysis as an option for factor analysis, researchers have developed programs to facilitate the implementation of the method (Enzmann, 1997; Kaufman & Dunlap, 2000; Lautenschlager, 1989b; Longman, Cota, Holden, & Fekken, 1989a; O’Connor, 2000; Thompson & Daniel, 1996).”
Is Parceling Really Necessary? A Comparison of Results From Item Parceling and Categorical Variable Methodology (2008, background): “Although such underfactoring has been studied extensively in the area of exploratory factor analysis (EFA; e.g., Hakstian, Rogers, & Cattell, 1982; Zwick & Velicer, 1982, 1986), it has received much less attention in the confirmatory factor analysis (CFA) literature.”