Rotation to Simple Loadings Using Component Loss Functions: The Oblique Case

By Robert I. Jennrich. 2006

81 citations recorded by the source corpus.

Subfields: Applied Mathematics, Control and Systems Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/0a5c16eab50e2fcef244726d6e83e94a4dd1b573

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In Applied Mathematics

In Control and Systems Engineering

In Management Science and Operations Research

Citations in the corpus

Estimation of oblique structure via penalized likelihood factor analysis (2013, background): “Note that the lasso loss function is included in the class of component loss function (Jennrich 2004, 2006).”
Should Regularization Replace Simple Structure Rotation in Exploratory Factor Analysis? (2019, methodology): “However, applying rotation techniques has two major drawbacks: First, researchers have to choose among a multitude of rotation techniques that is still growing (e.g., Beauducel, 2018; Ertel, 2011; Jennrich, 2004, 2006; Yamamoto & Jennrich, 2013).”
Robust factor analysis in the presence of normality violations, missing data, and outliers: Empirical questions and possible solutions (2014, methodology): “When the performance of the rotation criteria are inspected by means of sorted absolute loading (SAL) plots (Jennrich, 2006; Fleming, 2012) as shown in figure 5, it appears that Simplimax delivers the best performance.”
Item Response Theory—A Statistical Framework for Educational and Psychological Measurement (2021, unknown): “First, these methods do not give a sparse estimate of the loading matrix, though they may resolve the rotational indeterminacy issue and lead to a consistent estimator of the true loading matrix (Jennrich, 2004, 2006).”
Measurement Invariance Testing with Many Groups: A Comparison of Five Approaches (2017, methodology): “In the function F for every measurement parameter the difference between the parameters of every pair of groups scaled via f, which is the component loss function (Jennrich, 2006), is added up to the total loss function.”
Rotation Criteria and Hypothesis Testing for Exploratory Factor Analysis: Implications for Factor Pattern Loadings and Interfactor Correlations (2011, background): “Additional details related to the geomin criterion may be obtained from Asparouhov and Muthén (2009), Browne (2001), Jennrich (2006), and Yates (1987).”
An Investigation of the Alignment Method With Polytomous Indicators Under Conditions of Partial Measurement Invariance (2018, methodology): “The CLF has also been used in exploratory factor analysis (EFA) rotation algorithms to estimate factor loadings with the simplest possible structure (Jennrich, 2006).”
Rotated Word Vector Representations and their Interpretability (2017, methodology): “Meanwhile, various rotation methods are proposed such as CF-family (Crawford and Ferguson, 1970), Infomax (McKeon, 1968), Minimum Entropy (Jennrich, 2006), Geomin (Yates, 1988), procrustues (Hurley and Cattell, 1962), and pro-max rotation criteria.”