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Maydeu-Olivares, Alberto; Brown, Gregory – Psychometrika, 2013
We offer an introduction to the five papers that make up this special section. These papers deal with a range of the methodological challenges that face researchers analyzing fMRI data--the spatial, multilevel, and longitudinal nature of the data, the sources of noise, and so on. The papers all provide analyses of data collected by a multi-site…
Descriptors: Data Analysis, Measurement, Brain, Diagnostic Tests
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Huang, Guan-Hua; Wang, Su-Mei; Hsu, Chung-Chu – Psychometrika, 2011
Statisticians typically estimate the parameters of latent class and latent profile models using the Expectation-Maximization algorithm. This paper proposes an alternative two-stage approach to model fitting. The first stage uses the modified k-means and hierarchical clustering algorithms to identify the latent classes that best satisfy the…
Descriptors: Mathematics, Data Analysis, Classification, Models
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Bocci, Laura; Vichi, Maurizio – Psychometrika, 2011
A weighted Euclidean distance model for analyzing three-way dissimilarity data (stimuli by stimuli by subjects) for heterogeneous subjects is proposed. First, it is shown that INDSCAL may fail to identify a common space representative of the observed data structure in presence of heterogeneity. A new model that removes the rotational invariance of…
Descriptors: Models, Data Analysis, Multidimensional Scaling
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Jennrich, Robert I.; Bentler, Peter M. – Psychometrika, 2012
Bi-factor analysis is a form of confirmatory factor analysis originally introduced by Holzinger and Swineford ("Psychometrika" 47:41-54, 1937). The bi-factor model has a general factor, a number of group factors, and an explicit bi-factor structure. Jennrich and Bentler ("Psychometrika" 76:537-549, 2011) introduced an exploratory form of bi-factor…
Descriptors: Factor Structure, Factor Analysis, Models, Comparative Analysis
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Tenenhaus, Arthur; Tenenhaus, Michel – Psychometrika, 2011
Regularized generalized canonical correlation analysis (RGCCA) is a generalization of regularized canonical correlation analysis to three or more sets of variables. It constitutes a general framework for many multi-block data analysis methods. It combines the power of multi-block data analysis methods (maximization of well identified criteria) and…
Descriptors: Multivariate Analysis, Correlation, Data Analysis, Mathematics
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Fong, Duncan K. H.; Ebbes, Peter; DeSarbo, Wayne S. – Psychometrika, 2012
Multiple regression is frequently used across the various social sciences to analyze cross-sectional data. However, it can often times be challenging to justify the assumption of common regression coefficients across all respondents. This manuscript presents a heterogeneous Bayesian regression model that enables the estimation of…
Descriptors: Monte Carlo Methods, Social Sciences, Computation, Models
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Ligtvoet, Rudy – Psychometrika, 2012
In practice, the sum of the item scores is often used as a basis for comparing subjects. For items that have more than two ordered score categories, only the partial credit model (PCM) and special cases of this model imply that the subjects are stochastically ordered on the common latent variable. However, the PCM is very restrictive with respect…
Descriptors: Simulation, Item Response Theory, Comparative Analysis, Scores
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Poon, Wai-Yin; Wang, Hai-Bin – Psychometrika, 2010
A new class of parametric models that generalize the multivariate probit model and the errors-in-variables model is developed to model and analyze ordinal data. A general model structure is assumed to accommodate the information that is obtained via surrogate variables. A hybrid Gibbs sampler is developed to estimate the model parameters. To…
Descriptors: Correlation, Psychometrics, Models, Measurement
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von Oertzen, Timo; Boker, Steven M. – Psychometrika, 2010
This paper investigates the precision of parameters estimated from local samples of time dependent functions. We find that "time delay embedding," i.e., structuring data prior to analysis by constructing a data matrix of overlapping samples, increases the precision of parameter estimates and in turn statistical power compared to standard…
Descriptors: Instructional Effectiveness, Computation, Simulation, Data Analysis
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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
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Vera, J. Fernando; Macias, Rodrigo; Heiser, Willem J. – Psychometrika, 2009
In this paper, we propose a cluster-MDS model for two-way one-mode continuous rating dissimilarity data. The model aims at partitioning the objects into classes and simultaneously representing the cluster centers in a low-dimensional space. Under the normal distribution assumption, a latent class model is developed in terms of the set of…
Descriptors: Multidimensional Scaling, Probability, Item Response Theory, Models
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Karabatsos, George; Walker, Stephen G. – Psychometrika, 2009
A Bayesian nonparametric model is introduced for score equating. It is applicable to all major equating designs, and has advantages over previous equating models. Unlike the previous models, the Bayesian model accounts for positive dependence between distributions of scores from two tests. The Bayesian model and the previous equating models are…
Descriptors: Nonparametric Statistics, Item Response Theory, Models, Comparative Analysis
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Suh, Youngsuk; Bolt, Daniel M. – Psychometrika, 2010
Nested logit item response models for multiple-choice data are presented. Relative to previous models, the new models are suggested to provide a better approximation to multiple-choice items where the application of a solution strategy precedes consideration of response options. In practice, the models also accommodate collapsibility across all…
Descriptors: Computation, Simulation, Psychometrics, Models
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Park, Joonwook; Desarbo, Wayne S.; Liechty, John – Psychometrika, 2008
Multidimensional scaling (MDS) models for the analysis of dominance data have been developed in the psychometric and classification literature to simultaneously capture subjects' "preference heterogeneity" and the underlying dimentional structure for a set of designated stimuli in a parsimonious manner. There are two major types of latent utility…
Descriptors: Multidimensional Scaling, Models, Bayesian Statistics, Data Analysis
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De Boeck, Paul – Psychometrika, 2008
It is common practice in IRT to consider items as fixed and persons as random. Both, continuous and categorical person parameters are most often random variables, whereas for items only continuous parameters are used and they are commonly of the fixed type, although exceptions occur. It is shown in the present article that random item parameters…
Descriptors: Test Items, Goodness of Fit, Item Response Theory, Models
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