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Gignac, Gilles E.; Watkins, Marley W. – Multivariate Behavioral Research, 2013
Previous confirmatory factor analytic research that has examined the factor structure of the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) has endorsed either higher order models or oblique factor models that tend to amalgamate both general factor and index factor sources of systematic variance. An alternative model that has not yet…
Descriptors: Intelligence Tests, Test Reliability, Factor Structure, Models
Sass, Daniel A.; Schmitt, Thomas A. – Multivariate Behavioral Research, 2010
Exploratory factor analysis (EFA) is a commonly used statistical technique for examining the relationships between variables (e.g., items) and the factors (e.g., latent traits) they depict. There are several decisions that must be made when using EFA, with one of the more important being choice of the rotation criterion. This selection can be…
Descriptors: Factor Analysis, Criteria, Factor Structure, Correlation
Reise, Steven P. – Multivariate Behavioral Research, 2012
Bifactor latent structures were introduced over 70 years ago, but only recently has bifactor modeling been rediscovered as an effective approach to modeling "construct-relevant" multidimensionality in a set of ordered categorical item responses. I begin by describing the Schmid-Leiman bifactor procedure (Schmid & Leiman, 1957) and highlight its…
Descriptors: Models, Factor Structure, Factor Analysis, Correlation
de Winter, J. C. F.; Dodou, D.; Wieringa, P. A. – Multivariate Behavioral Research, 2009
Exploratory factor analysis (EFA) is generally regarded as a technique for large sample sizes ("N"), with N = 50 as a reasonable absolute minimum. This study offers a comprehensive overview of the conditions in which EFA can yield good quality results for "N" below 50. Simulations were carried out to estimate the minimum required "N" for different…
Descriptors: Sample Size, Factor Analysis, Enrollment, Evaluation Methods

Schneeweiss, Hans – Multivariate Behavioral Research, 1997
A sufficient condition in terms of the unique variances of a common factor model is given for the results of factor analysis to come closer to those of principal components analysis. In general, vectors corresponding to loading matrices can be related to each other by a specific measure of closeness, which is illustrated. (SLD)
Descriptors: Factor Analysis, Factor Structure, Matrices

Krzanowski, Wojtek J.; Kline, Paul – Multivariate Behavioral Research, 1995
A cross-validation method is described for selecting the significant components from a principal components analysis, and properties of the method are discussed. Parallels are drawn with other related methods in covariance structure modeling, and some comparisons among methods are illustrated with two data sets previously analyzed. (SLD)
Descriptors: Factor Analysis, Factor Structure, Research Methodology, Selection

McArdle, J. J.; Cattell, Raymond B. – Multivariate Behavioral Research, 1994
Some problems of multiple-group factor rotation based on the parallel proportional profiles and confactor rotation of R. B. Cattell are described, and several alternative modeling solutions are proposed. Benefits and limitations of the structural-modeling approach to oblique confactor resolution are examined, and opportunities for research are…
Descriptors: Factor Analysis, Factor Structure, Structural Equation Models
Haig, Brian D. – Multivariate Behavioral Research, 2005
This article examines the methodological foundations of exploratory factor analysis (EFA) and suggests that it is properly construed as a method for generating explanatory theories. In the first half of the article it is argued that EFA should be understood as an abductive method of theory generation that exploits an important precept of…
Descriptors: Scientific Methodology, Factor Analysis, Factor Structure, Theories

Veldman, Donald J. – Multivariate Behavioral Research, 1974
Descriptors: Factor Analysis, Factor Structure, Orthogonal Rotation, Research Problems

Trendafilov, Nickolay T. – Multivariate Behavioral Research, 1996
An iterative process is proposed for obtaining an orthogonal simple structure solution. At each iteration, a target matrix is constructed such that the relative contributions of the target majorize the original ones, factor by factor. The convergence of the procedure is proven, and the algorithm is illustrated. (SLD)
Descriptors: Algorithms, Factor Analysis, Factor Structure, Matrices

Linn, Robert L.; And Others – Multivariate Behavioral Research, 1975
Factor structures of student ratings of instruction resulting from total group, between group, and within group analyses were compared. Six factors obtained from responses by students to 31 items were used to approximate the between group covariance matrix based on 437 classroom means and the pooled within classroom covariance matrix. (Author/BJG)
Descriptors: Factor Analysis, Factor Structure, Matrices, Student Evaluation

Maraun, Michael D.; And Others – Multivariate Behavioral Research, 1996
The issue of indeterminacy in factor analysis and the debate between the proposed alternative solution and posterior moment position are explored in an article and 14 commentaries and rebuttals in two rounds. Implications for applied work involving factor analysis are discussed. (SLD)
Descriptors: Factor Analysis, Factor Structure, Mathematical Models, Metaphors

Trendafilov, Nickolay T. – Multivariate Behavioral Research, 1994
An alternative to the PROMAX exploratory method is presented for constructing a target matrix in Procrustean rotation in factor analysis. A technique is proposed based on vector majorization. The approach is illustrated with several standard numerical examples. (SLD)
Descriptors: Equations (Mathematics), Factor Analysis, Factor Structure, Matrices

Rozeboom, William W. – Multivariate Behavioral Research, 1992
Enriching factor rotation algorithms with the capacity to conduct repeated searches from random starting points can make the tendency to converge to optima that are merely local a way to catch rotations of the input factors that might otherwise elude discovery. Use of the HYBALL computer program is discussed. (SLD)
Descriptors: Algorithms, Comparative Analysis, Factor Analysis, Factor Structure

Millsap, Roger E. – Multivariate Behavioral Research, 1998
Two theorems are presented that describe the conditions under which intercept differences can exist under factorial invariance. In such cases, intercept differences do not result from measurement bias in either the tests or the criterion. The conditions of the theorems are testable, and the test procedures are illustrated. (SLD)
Descriptors: Factor Analysis, Factor Structure, Groups, Regression (Statistics)