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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
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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
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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
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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
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Golding, Stephen L.; Seidman, Edward – Multivariate Behavioral Research, 1974
A relatively simple technique for assessing the convergence of sets of variables across method domains is presented. The technique, two-step principal components analysis, empirically orthogonalizes each method domain into sets of components, and then analyzes convergence among components across domains. (Author)
Descriptors: Comparative Analysis, Correlation, Factor Analysis, Factor Structure
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Dunlap, William P.; Cornwell, John M. – Multivariate Behavioral Research, 1994
The fundamental problems that ipsative measures impose for factor analysis are shown analytically. Normative and ipsative correlation matrices are used to show that the factor pattern induced by ipsativity will overwhelm any factor structure seen with normative factor analysis, making factor analysis not interpretable. (SLD)
Descriptors: Correlation, Factor Analysis, Factor Structure, Matrices
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Tracey, Terence J. G.; And Others – Multivariate Behavioral Research, 1996
The relation of the general factor of the Inventory of Interpersonal Problems (IIP) to several response set and personality measures and the circumplex structure was studied with 105 and 1,093 undergraduates. Results support the general factor of the IIP as having a substantial nonbiasing interpretation and indicative of general interpersonal…
Descriptors: Correlation, Factor Analysis, Factor Structure, Higher Education
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Bernstein, Ira H.; And Others – Multivariate Behavioral Research, 1986
A three subscale inventory designed by Fenigstein, Scheier, and Buss to measure self-consciousness was administered to 297 college students. Fenigstein et al.'s representation was found to fit the data in its original form. Items on the subscales differ nearly as much statistically as they do substantively. (Author/LMO)
Descriptors: College Students, Correlation, Factor Analysis, Factor Structure
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De Ayala, R. J.; Hertzog, Melody A. – Multivariate Behavioral Research, 1991
Multidimensional scaling (MDS) and exploratory and confirmatory factor analyses were compared in the assessment of the dimensionality of data sets, using sets generated to be one-dimensional or two-dimensional and differing in degree of interdimensional correlation and number of items defining a dimension. (SLD)
Descriptors: Comparative Analysis, Correlation, Equations (Mathematics), Factor Structure
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Borg, Ingwer; Staufenbiel, Thomas – Multivariate Behavioral Research, 1992
The representation of multivariate data by icons is discussed. The factorial sun is suggested as superior to the commonly used snowflake or sun icons and as better representing the values of the different variables and their correlational structure. Two experiments with 60 college students demonstrate the factorial sun's superiority. (SLD)
Descriptors: College Students, Comparative Analysis, Computer Oriented Programs, Correlation
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Hsu, Louis M. – Multivariate Behavioral Research, 1994
Item overlap coefficient (IOC) formulas are discussed, providing six warnings about their calculation and interpretation and some explanations of why item overlap influences the Minnesota Multiphasic Personality Inventory and the Millon Clinical Multiaxial Inventory factor structures. (SLD)
Descriptors: Correlation, Definitions, Equations (Mathematics), Factor Structure
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Buja, Andreas; Eyuboglu, Nermin – Multivariate Behavioral Research, 1992
Use of parallel analysis (PA), a selection rule for the number-of-factors problem, is investigated from the viewpoint of permutation assessment through a Monte Carlo simulation. Results reveal advantages and limitations of PA. Tables of sample eigenvalues are included. (SLD)
Descriptors: Computer Simulation, Correlation, Factor Structure, Mathematical Models
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Klingler, Daniel E.; Saunders, David R. – Multivariate Behavioral Research, 1975
Descriptors: Adults, Correlation, Diagnostic Tests, Factor Analysis
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Marsh, Herbert W.; Byrne, Barbara M. – Multivariate Behavioral Research, 1993
Extensions of the confirmatory factor analysis approach to multitrait-multimethod data are demonstrated, and self-other agreement on multiple dimensions of self-concept are evaluated in 2 studies investigating the ability of significant others to infer multiple dimensions of self-concept for 151 Australian and 941 Canadian college students. (SLD)
Descriptors: College Students, Correlation, Cross Cultural Studies, Factor Structure