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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
Woods, Carol M. – Multivariate Behavioral Research, 2009
Differential item functioning (DIF) occurs when an item on a test or questionnaire has different measurement properties for 1 group of people versus another, irrespective of mean differences on the construct. This study focuses on the use of multiple-indicator multiple-cause (MIMIC) structural equation models for DIF testing, parameterized as item…
Descriptors: Test Bias, Structural Equation Models, Item Response Theory, Testing
Sijtsma, Klaas; van der Ark, L. Andries – Multivariate Behavioral Research, 2003
This article first discusses a statistical test for investigating whether or not the pattern of missing scores in a respondent-by-item data matrix is random. Since this is an asymptotic test, we investigate whether it is useful in small but realistic sample sizes. Then, we discuss two known simple imputation methods, person mean (PM) and two-way…
Descriptors: Test Items, Questionnaires, Statistical Analysis, Models

Leutner, Detlev; Weinsier, Philip D. – Multivariate Behavioral Research, 1991
An interest questionnaire with 72 university course descriptions based on a facet design was used to determine whether computer anxiety or computer disinterest was related to interest in or willingness to take selected courses of 200 Belgian and German students. Analysis of variance supports conclusions from the multidimensional scalings. (SLD)
Descriptors: Analysis of Variance, College Students, Computer Literacy, Course Selection (Students)