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Choosing the Optimal Number of Factors in Exploratory Factor Analysis: A Model Selection Perspective
Preacher, Kristopher J.; Zhang, Guangjian; Kim, Cheongtag; Mels, Gerhard – Multivariate Behavioral Research, 2013
A central problem in the application of exploratory factor analysis is deciding how many factors to retain ("m"). Although this is inherently a model selection problem, a model selection perspective is rarely adopted for this task. We suggest that Cudeck and Henly's (1991) framework can be applied to guide the selection process.…
Descriptors: Factor Analysis, Models, Selection, Goodness of Fit
Kammeyer-Mueller, John; Steel, Piers D. G.; Rubenstein, Alex – Multivariate Behavioral Research, 2010
Common source bias has been the focus of much attention. To minimize the problem, researchers have sometimes been advised to take measurements of predictors from one observer and measurements of outcomes from another observer or to use separate occasions of measurement. We propose that these efforts to eliminate biases due to common source…
Descriptors: Statistical Bias, Predictor Variables, Measurement, Data Collection