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Magis, David; Raiche, Gilles – Psychometrika, 2012
This paper focuses on two estimators of ability with logistic item response theory models: the Bayesian modal (BM) estimator and the weighted likelihood (WL) estimator. For the BM estimator, Jeffreys' prior distribution is considered, and the corresponding estimator is referred to as the Jeffreys modal (JM) estimator. It is established that under…
Descriptors: Item Response Theory, Computation, Bayesian Statistics, Models
San Martin, Ernesto; Jara, Alejandro; Rolin, Jean-Marie; Mouchart, Michel – Psychometrika, 2011
We study the identification and consistency of Bayesian semiparametric IRT-type models, where the uncertainty on the abilities' distribution is modeled using a prior distribution on the space of probability measures. We show that for the semiparametric Rasch Poisson counts model, simple restrictions ensure the identification of a general…
Descriptors: Identification, Probability, Item Response Theory, Bayesian Statistics
Tijmstra, Jesper; Hessen, David J.; van der Heijden, Peter G. M.; Sijtsma, Klaas – Psychometrika, 2011
A new observable consequence of the property of invariant item ordering is presented, which holds under Mokken's double monotonicity model for dichotomous data. The observable consequence is an invariant ordering of the item-total regressions. Kendall's measure of concordance "W" and a weighted version of this measure are proposed as measures for…
Descriptors: Item Response Theory, Bayesian Statistics, Regression (Statistics), Models
Geerlings, Hanneke; Glas, Cees A. W.; van der Linden, Wim J. – Psychometrika, 2011
An application of a hierarchical IRT model for items in families generated through the application of different combinations of design rules is discussed. Within the families, the items are assumed to differ only in surface features. The parameters of the model are estimated in a Bayesian framework, using a data-augmented Gibbs sampler. An obvious…
Descriptors: Simulation, Intelligence Tests, Item Response Theory, Models
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
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
Loeys, T.; Rosseel, Y.; Baten, K. – Psychometrika, 2011
In the psycholinguistic literature, reaction times and accuracy can be analyzed separately using mixed (logistic) effects models with crossed random effects for item and subject. Given the potential correlation between these two outcomes, a joint model for the reaction time and accuracy may provide further insight. In this paper, a Bayesian…
Descriptors: Reaction Time, Psycholinguistics, Simulation, Word Recognition
Craigmile, Peter F.; Peruggia, Mario; Van Zandt, Trisha – Psychometrika, 2010
Human response time (RT) data are widely used in experimental psychology to evaluate theories of mental processing. Typically, the data constitute the times taken by a subject to react to a succession of stimuli under varying experimental conditions. Because of the sequential nature of the experiments there are trends (due to learning, fatigue,…
Descriptors: Reaction Time, Models, Experimental Psychology, Stimuli
Duvvuri, Sri Devi; Gruca, Thomas S. – Psychometrika, 2010
Identifying price sensitive consumers is an important problem in marketing. We develop a Bayesian multi-level factor analytic model of the covariation among household-level price sensitivities across product categories that are substitutes. Based on a multivariate probit model of category incidence, this framework also allows the researcher to…
Descriptors: Marketing, Costs, Consumer Economics, Models
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
Morey, Richard D.; Rouder, Jeffrey N.; Speckman, Paul L. – Psychometrika, 2009
Human abilities in perceptual domains have conventionally been described with reference to a threshold that may be defined as the maximum amount of stimulation which leads to baseline performance. Traditional psychometric links, such as the probit, logit, and "t", are incompatible with a threshold as there are no true scores corresponding to…
Descriptors: Psychometrics, Computation, Item Response Theory, Models
Yang, Mingan; Dunson, David B. – Psychometrika, 2010
Structural equation models (SEMs) with latent variables are widely useful for sparse covariance structure modeling and for inferring relationships among latent variables. Bayesian SEMs are appealing in allowing for the incorporation of prior information and in providing exact posterior distributions of unknowns, including the latent variables. In…
Descriptors: Structural Equation Models, Markov Processes, Item Response Theory, Bayesian Statistics
Hooker, Giles; Finkelman, Matthew – Psychometrika, 2010
Hooker, Finkelman, and Schwartzman ("Psychometrika," 2009, in press) defined a paradoxical result as the attainment of a higher test score by changing answers from correct to incorrect and demonstrated that such results are unavoidable for maximum likelihood estimates in multidimensional item response theory. The potential for these results to…
Descriptors: Models, Scores, Item Response Theory, Psychometrics
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
Klauer, Karl Christoph – Psychometrika, 2010
Multinomial processing tree models are widely used in many areas of psychology. A hierarchical extension of the model class is proposed, using a multivariate normal distribution of person-level parameters with the mean and covariance matrix to be estimated from the data. The hierarchical model allows one to take variability between persons into…
Descriptors: Simulation, Bayesian Statistics, Computation, Models