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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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Gaer, Eva Vande; Ceulemans, Eva; Van Mechelen, Iven; Kuppens, Peter – Psychometrika, 2012
In many psychological research domains stimulus-response profiles are explained by conjecturing a sequential process in which some variables mediate between stimuli and responses. Charting sequential processes is often a complex task because (1) many possible mediating variables may exist, and (2) interindividual differences may occur in the…
Descriptors: Stimuli, Responses, Psychological Studies, Sequential Approach
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Molenaar, Dylan; Dolan, Conor V.; de Boeck, Paul – Psychometrika, 2012
The Graded Response Model (GRM; Samejima, "Estimation of ability using a response pattern of graded scores," Psychometric Monograph No. 17, Richmond, VA: The Psychometric Society, 1969) can be derived by assuming a linear regression of a continuous variable, Z, on the trait, [theta], to underlie the ordinal item scores (Takane & de Leeuw in…
Descriptors: Simulation, Regression (Statistics), Psychometrics, Item Response Theory
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Yang, Manshu; Chow, Sy-Miin – Psychometrika, 2010
Facial electromyography (EMG) is a useful physiological measure for detecting subtle affective changes in real time. A time series of EMG data contains bursts of electrical activity that increase in magnitude when the pertinent facial muscles are activated. Whereas previous methods for detecting EMG activation are often based on deterministic or…
Descriptors: Test Bias, Error of Measurement, Human Body, Diagnostic Tests
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Iliopoulos, G.; Kateri, M.; Ntzoufras, I. – Psychometrika, 2009
Association models constitute an attractive alternative to the usual log-linear models for modeling the dependence between classification variables. They impose special structure on the underlying association by assigning scores on the levels of each classification variable, which can be fixed or parametric. Under the general row-column (RC)…
Descriptors: Markov Processes, Classification, Bayesian Statistics, Probability
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Leenen, Iwin; Van Mechelen, Iven; Gelman, Andrew; De Knop, Stijn – Psychometrika, 2008
Hierarchical classes models are models for "N"-way "N"-mode data that represent the association among the "N" modes and simultaneously yield, for each mode, a hierarchical classification of its elements. In this paper we present a stochastic extension of the hierarchical classes model for two-way two-mode binary data. In line with the original…
Descriptors: Simulation, Bayesian Statistics, Models, Classification
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Ceulemans, Eva; Van Mechelen, Iven – Psychometrika, 2008
In psychological research, one often aims at explaining individual differences in S-R profiles, that is, individual differences in the responses (R) with which people react to specific stimuli (S). To this end, researchers often postulate an underlying sequential process, which boils down to the specification of a set of mediating variables (M)…
Descriptors: Stimuli, Psychological Studies, Simulation, Individual Differences
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Kiers, Henk A. L.; Vicari, Donatella; Vichi, Maurizio – Psychometrika, 2005
For the exploratory analysis of a matrix of proximities or (dis)similarities between objects, one often uses cluster analysis (CA) or multidimensional scaling (MDS). Solutions resulting from such analyses are sometimes interpreted using external information on the objects. Usually the procedures of CA, MDS and using external information are…
Descriptors: Classification, Multidimensional Scaling, Multivariate Analysis, Models
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Kohli, Rajeev; Jedidi, Kamel – Psychometrika, 2005
The authors introduce subset conjunction as a classification rule by which an acceptable alternative must satisfy some minimum number of criteria. The rule subsumes conjunctive and disjunctive decision strategies as special cases. Subset conjunction can be represented in a binary-response model, for example, in a logistic regression, using only…
Descriptors: Psychometrics, Probability, Models, Classification
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Cooil, Bruce; Rust, Roland T. – Psychometrika, 1995
A proportional reduction in loss (PRL) measure for reliability of categorical data is explored for the situation in which each of "N" judges assigns a subject to one of "K" categories. Calculating a lower bound for reliability under more general conditions than had been proposed is demonstrated. (SLD)
Descriptors: Bayesian Statistics, Classification, Equations (Mathematics), Estimation (Mathematics)
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Klauer, Karl Christoph; Batchelder, William H. – Psychometrika, 1996
A general approach to the analysis of nominal-scale ratings is discussed that is based on a simple measurement error model for a rater's judgments. The basic measurement error model gives rise to an agreement model for the agreement matrix of two or more raters. (SLD)
Descriptors: Classification, Data Analysis, Equations (Mathematics), Error of Measurement
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Peay, Edmund R. – Psychometrika, 1975
A class of closely related hierarchical grouping methods are discussed and a procedure which implements them in an integrated fashion is presented. These methods avoid some theoretical anomalies inherent in clustering and provide a framework for viewing partitioning and nonpartitioning grouping. Significant relationships between these methods and…
Descriptors: Classification, Cluster Grouping, Computer Programs, Data Analysis