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Blanchard, Simon J.; DeSarbo, Wayne S. – Psychometrika, 2013

We introduce a new statistical procedure for the identification of unobserved categories that vary between individuals and in which objects may span multiple categories. This procedure can be used to analyze data from a proposed sorting task in which individuals may simultaneously assign objects to multiple piles. The results of a synthetic…

Descriptors: Statistical Analysis, Identification, Classification, Data Analysis

Calhoun, Vince D.; Allen, Elena – Psychometrika, 2013

There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in…

Descriptors: Brain, Diagnostic Tests, Neurological Organization, Statistical Analysis

San Martin, Ernesto; Rolin, Jean-Marie; Castro, Luis M. – Psychometrika, 2013

In this paper, we study the identification of a particular case of the 3PL model, namely when the discrimination parameters are all constant and equal to 1. We term this model, 1PL-G model. The identification analysis is performed under three different specifications. The first specification considers the abilities as unknown parameters. It is…

Descriptors: Item Response Theory, Models, Identification, Statistical Analysis

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

Keller, Bryan – Psychometrika, 2012

Randomization tests are often recommended when parametric assumptions may be violated because they require no distributional or random sampling assumptions in order to be valid. In addition to being exact, a randomization test may also be more powerful than its parametric counterpart. This was demonstrated in a simulation study which examined the…

Descriptors: Statistical Analysis, Nonparametric Statistics, Simulation, Sampling

Grasshoff, Ulrike; Holling, Heinz; Schwabe, Rainer – Psychometrika, 2012

In this paper, optimal designs will be derived for estimating the ability parameters of the Rasch model when difficulty parameters are known. It is well established that a design is locally D-optimal if the ability and difficulty coincide. But locally optimal designs require that the ability parameters to be estimated are known. To attenuate this…

Descriptors: Item Response Theory, Test Items, Psychometrics, Statistical Analysis

Maris, Gunter; van der Maas, Han – Psychometrika, 2012

Starting from an explicit scoring rule for time limit tasks incorporating both response time and accuracy, and a definite trade-off between speed and accuracy, a response model is derived. Since the scoring rule is interpreted as a sufficient statistic, the model belongs to the exponential family. The various marginal and conditional distributions…

Descriptors: Item Response Theory, Scoring, Reaction Time, Accuracy

Anselmi, Pasquale; Robusto, Egidio; Stefanutti, Luca – Psychometrika, 2012

The Gain-Loss model is a probabilistic skill multimap model for assessing learning processes. In practical applications, more than one skill multimap could be plausible, while none corresponds to the true one. The article investigates whether constraining the error probabilities is a way of uncovering the best skill assignment among a number of…

Descriptors: Item Response Theory, Learning Processes, Simulation, Probability

Mooijaart, Ab; Satorra, Albert – Psychometrika, 2012

Starting with Kenny and Judd ("Psychol. Bull." 96:201-210, 1984) several methods have been introduced for analyzing models with interaction terms. In all these methods more information from the data than just means and covariances is required. In this paper we also use more than just first- and second-order moments; however, we are aiming to…

Descriptors: Structural Equation Models, Computation, Goodness of Fit, Statistical Analysis

Elrod, Terry; Haubl, Gerald; Tipps, Steven W. – Psychometrika, 2012

Recent research reflects a growing awareness of the value of using structural equation models to analyze repeated measures data. However, such data, particularly in the presence of covariates, often lead to models that either fit the data poorly, are exceedingly general and hard to interpret, or are specified in a manner that is highly data…

Descriptors: Structural Equation Models, Preferences, Data, Statistical Analysis

Bartolucci, F.; Montanari, G. E.; Pandolfi, S. – Psychometrika, 2012

With reference to a questionnaire aimed at assessing the performance of Italian nursing homes on the basis of the health conditions of their patients, we investigate two relevant issues: dimensionality of the latent structure and discriminating power of the items composing the questionnaire. The approach is based on a multidimensional item…

Descriptors: Foreign Countries, Probability, Item Analysis, Test Items

Yuan, Ke-Hai; Zhang, Zhiyong – Psychometrika, 2012

The paper develops a two-stage robust procedure for structural equation modeling (SEM) and an R package "rsem" to facilitate the use of the procedure by applied researchers. In the first stage, M-estimates of the saturated mean vector and covariance matrix of all variables are obtained. Those corresponding to the substantive variables…

Descriptors: Structural Equation Models, Tests, Federal Aid, Psychometrics

Jordan, Pascal; Spiess, Martin – Psychometrika, 2012

Maximum likelihood and Bayesian ability estimation in multidimensional item response models can lead to paradoxical results as proven by Hooker, Finkelman, and Schwartzman ("Psychometrika" 74(3): 419-442, 2009): Changing a correct response on one item into an incorrect response may produce a higher ability estimate in one dimension.…

Descriptors: Item Response Theory, Statistical Analysis, Factor Analysis, Generalization

Choulakian, Vartan – Psychometrika, 2011

The Maxbet method is a generalized principal components analysis of a data set, where the group structure of the variables is taken into account. Similarly, 3-block[12,13] partial Maxdiff method is a generalization of covariance analysis, where only the covariances between blocks (1, 2) and (1, 3) are taken into account. The aim of this paper is…

Descriptors: Factor Analysis, Statistical Analysis

de la Torre, Jimmy – Psychometrika, 2011

The G-DINA ("generalized deterministic inputs, noisy and gate") model is a generalization of the DINA model with more relaxed assumptions. In its saturated form, the G-DINA model is equivalent to other general models for cognitive diagnosis based on alternative link functions. When appropriate constraints are applied, several commonly used…

Descriptors: Structural Equation Models, Identification, Models, Comparative Analysis