An analytical solution for approximating simple structure in factor analysis

By John B. Carroll. 1953

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Citations in the corpus

Interpretable Decision Sets: A Joint Framework for Description and Prediction (2016, background): “Research on interpretable models has a long history [8, 32, 7, 33], but with the availability of ever larger datasets and the adoption of computer-based decision making in all parts of society the need for interpretable machine learning models has only increased.”
Exploratory Structural Equation Modeling, Integrating CFA and EFA: Application to Students' Evaluations of University Teaching (2009, unknown): “…useful factor structure, nor even a requirement of traditional definitions of “simple structure” in which nontarget loadings are ideally small relative to target loadings but not required to be zero (Carroll, 1953; Holzinger & Harman, 1941; Kaiser, 1958; McDonald, 1985; Thurstone, 1947).”
Rotated Word Vector Representations and their Interpretability (2017, methodology): “The criterion is a generalized version of the widely used criteria, the orthomax family (Harman, 1960) which includes quartimax (Carroll, 1953; Ferguson, 1954; Neuhaus and Wrigley, 1954), varimax (Kaiser, 1958), and direct quartimin (Carroll, 1960).”
Exploratory Structural Equation Modeling (2009, unknown): “…useful factor structure, nor even a requirement of traditional definitions of " simple structure " in which nontarget loadings are ideally small relative to target loadings but not required to be zero (Carroll, 1953; Holzinger & Harman, 1941; Kaiser, 1958; McDonald, 1985; Thurstone, 1947).”
Exploratory structural equation modeling: an integration of the best features of exploratory and confirmatory factor analysis. (2014, background): “…items that load only on a single factor, this is not a requirement of a well-defined factor structure, nor even a requirement of simple structures in which nontarget loadings are ideally small relative to target loadings but are not required to be zero (Carroll 1953, McDonald 1985, Thurstone 1947).”
Rotation to Simple Loadings Using Component Loss Functions: The Oblique Case (2006, methodology): “For this we used the A given by Cureton and Mulaik (1975). Like the 24 psychological tests data in Harman this matrix has been used extensively to demonstrate and compare rotation methods.”
Use and Interpretation of Factor Analysis in The Journal of Educational Research: 1992-2002 (2004, methodology): “Ad hoc decisions about the number of factors (dimensionality) were replaced with precise rules for the number of factors (Cattell, 1958, 1966; Cattell & Vogelman, 1977; Guttman, 1954; Kaiser, 1960, 1961), and transformations of the factors were introduced to enhance interpretability (Carroll, 1953; ”
Factor analysis applied to regional geochemical data: problems and possibilities (2002, methodology): “Varimax and Promax are orthogonal rotations, i.e. the rotated factors are not correlated, Oblimin and Quartimin are oblique rotation methods, i.e. the rotated factors can be correlated.”
Rotation Criteria and Hypothesis Testing for Exploratory Factor Analysis: Implications for Factor Pattern Loadings and Interfactor Correlations (2011, background): “Unfortunately, its overemphasis on reducing row complexity can result in inflated interfactor correlations (Carroll, 1953; Gorsuch, 1983; Yates, 1987).”