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Ritter, Nicola L. – Online Submission, 2012
Many researchers recognize that factor analysis can be conducted on both correlation matrices and variance-covariance matrices. Although most researchers extract factors from non-distribution free or parametric methods, researchers can also extract factors from distribution free or non-parametric methods. The nature of the data dictates the method…
Descriptors: Factor Analysis, Comparative Analysis, Correlation, Nonparametric Statistics
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Tchumtchoua, Sylvie; Dey, Dipak K. – Psychometrika, 2012
This paper proposes a semiparametric Bayesian framework for the analysis of associations among multivariate longitudinal categorical variables in high-dimensional data settings. This type of data is frequent, especially in the social and behavioral sciences. A semiparametric hierarchical factor analysis model is developed in which the…
Descriptors: Factor Analysis, Bayesian Statistics, Behavioral Sciences, Social Sciences
Headrick, Todd C.; Sawilowsky, Shlomo S. – 2000
Real world data often fail to meet the underlying assumption of population normality. The Rank Transformation (RT) procedure has been recommended as an alternative to the parametric factorial analysis of Covariance (ANCOVA). The purpose of this study was to compare the Type I error and power properties of the RT ANCOVA to the parametric procedures…
Descriptors: Analysis of Covariance, Factor Analysis, Factor Structure, Nonparametric Statistics
Pyo, Kyong Hyon – 2000
The primary purpose of this study was to compare the performance of three procedures to assess dimensionality that were investigated by R. Nandakumar (1994) at different test conditions to reflect the characteristics of language test data. Procedures investigated were nonlinear factor analysis, the procedure of P. Holland and P. Rosenbaum, and W.…
Descriptors: Evaluation Methods, Factor Analysis, Language Tests, Nonparametric Statistics
Mittag, Kathleen Cage – 1993
Most researchers using factor analysis extract factors from a matrix of Pearson product-moment correlation coefficients. A method is presented for extracting factors in a non-parametric way, by extracting factors from a matrix of Spearman rho (rank correlation) coefficients. It is possible to factor analyze a matrix of association such that…
Descriptors: Correlation, Factor Analysis, Heuristics, Mathematical Models
Headrick, Todd C.; Vineyard, George – 2000
The Type I error and power properties of the parametric F test and three nonparametric competitors were compared in terms of 3 x 4 factorial analysis of covariance layout. The focus of the study was on the test for interaction either in the presence or absence of main effects. A variety of conditional distributions, sample sizes, levels of variate…
Descriptors: Analysis of Covariance, Evaluation Methods, Factor Analysis, Interaction
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van Abswoude, Alexandra A. H.; van der Ark, L. Andries; Sijtsma, Klaas – Applied Psychological Measurement, 2004
In this article, an overview of nonparametric item response theory methods for determining the dimensionality of item response data is provided. Four methods were considered: MSP, DETECT, HCA/CCPROX, and DIMTEST. First, the methods were compared theoretically. Second, a simulation study was done to compare the effectiveness of MSP, DETECT, and…
Descriptors: Comparative Analysis, Computer Software, Simulation, Nonparametric Statistics
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Donoghue, John R.; Cliff, Norman – Applied Psychological Measurement, 1991
The validity of the assumptions under which the ordinal true score test theory was derived was examined using (1) simulation based on classical test theory; (2) a long empirical test with data from 321 sixth graders; and (3) an extensive simulation with 480 datasets based on the 3-parameter model. (SLD)
Descriptors: Computer Simulation, Elementary Education, Elementary School Students, Equations (Mathematics)