Repairing Tom Swift's Electric Factor Analysis Machine

By Kristopher J Preacher, R. Maccallum. 2003

888 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Food Science, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/b0e78507503271c6b79b795ba16f0ccdbd938504

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Citations in the corpus

Responsive parenting: establishing early foundations for social, communication, and independent problem-solving skills. (2006, background): “This approach examines for underlying factors of a construct when inadequate information is available to confirm a hypothesized structure (Preacher & MacCallum, 2003).”
Examining the Effects of Gifted Programming in Mathematics and Reading Using the ECLS-K (2012, methodology): “Separately for school- and studentlevel covariates, we ran principal components analysis with oblimin rotation on the covariates to reduce the information in many variables into a set of weighted linear combinations of those variables (Pett, Lackey, & Sullivan, 2003; Preacher & MacCullum, 2003).”
Robust factor analysis in the presence of normality violations, missing data, and outliers: Empirical questions and possible solutions (2014, methodology): “…in defining latent variables generalizable beyond the current battery, and acknowledge that latent dimensions are likely to covary in the sample even if not in the population; in such cases FA is more appropriate than PCA (Costello & Osborne, 2005; Preacher & MacCallum, 2003; Widaman, 2007).”
Orientations Toward Disability: Differences over the lifecourse (2010, background): “Other items were dropped if they did not lower Cronbach’s alpha coefficients (Preacher & MacCallum, 2003 ).”
Cronbach's Alpha: Review of Limitations and Associated Recommendations (2010, methodology): “While PCA models components not to be correlated (Goldberg & Digman, 1994; Preacher & MacCallum, 2003), FA functions in way that the observed variable is taken as a composite of the common factor and unique factor (Preacher & MacCallum, 2003).”
On the Likelihood Ratio Test for the Number of Factors in Exploratory Factor Analysis (2007, background): “Beauducel (2001) , Fabrigar, Wegener, MacCallum, and Strahan (1999), and Preacher and MacCallum (2003) provided good recent reviews and discussions.”
Development and Initial Validation of the Online Learning Value and Self-Efficacy Scale (2008, methodology): “A principal axis factor (PAF) analysis with oblique rotation (Oblimin; delta = 0) was carried out on the 28 items from the OLVSES using SPSS 13.0 (see factor analysis recommendations in Preacher & MacCallum, 2003).”
Ten Steps in Scale Development and Reporting: A Guide for Researchers (2018, methodology): “Preacher and MacCallum (2003) argued the most important decisions in scale development include deciding between common factor analysis and PCA, the number of dimensions (i.e., factors) to retain, and the rotational method (oblique vs. orthogonal).”