Determining the Number of Factors in Exploratory and Confirmatory Factor Analysis

By R. Hoyle, Jamieson L. Duvall. 2004

146 citations recorded by the source corpus.

Subfields: Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/fad4d2d5f370b0738155a3b29ac9c86d4c8ab0ac

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In Information Systems

In Management Science and Operations Research

Citations in the corpus

Robust factor analysis in the presence of normality violations, missing data, and outliers: Empirical questions and possible solutions (2014, methodology): “Parallel Analysis, originally proposed by Horn (1965), has been described by several authors as one of the best methods of deciding how many factors to extract, particularly with social science data (Hoyle & Duvall, 2004).”
Breaking rules for the right reasons? An investigation of pro-social rule breaking (2012, methodology): “Further, we conducted a parallel analysis (Hoyle & Duvall, 2004) to provide a more objective indication of the number of factors to retain instead of using more subjective criteria, such as the Kaiser criterion or a Scree test (Thompson, 2004).”
A study of the factorial invariance of the Student Engagement Instrument (SEI): Results from middle and high school students (2010, methodology): “Alternative model specifications for this type of analysis are available (see Jöreskog, 1979, or Hoyle & Duvall, 2004).”
Psychometric Properties of the Penn Computerized Neurocognitive Battery (2014, unknown): “If the main goal is to explore the data, several numeric methods exist for estimating the optimal number of factors (see Hoyle & Duvall, 2004).”
Non-Graphical Solutions for Cattell’s Scree Test (2013, unknown): “Many challenged its validity (Hoyle and Duvall, 2004; Zwick and Velicer, 1986).”
On the Likelihood Ratio Test for the Number of Factors in Exploratory Factor Analysis (2007, methodology): “The most frequently used criteria and methods are listed in, for example, Gorsuch (1983, 2003), Hoyle and Duvall (2004), Keeling (2000), Yanai, Shigemasu, Maekawa, and Ichikawa (1990), and Zwick and Velicer (1986), as follows: (a) the number of eigenvalues of the sample correlation matrix that are…”