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Kaplan, David; Chen, Jianshen – Psychometrika, 2012
A two-step Bayesian propensity score approach is introduced that incorporates prior information in the propensity score equation and outcome equation without the problems associated with simultaneous Bayesian propensity score approaches. The corresponding variance estimators are also provided. The two-step Bayesian propensity score is provided for…
Descriptors: Intervals, Bayesian Statistics, Scores, Prior Learning
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
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
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
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
Miyazaki, Kei; Hoshino, Takahiro – Psychometrika, 2009
In Item Response Theory (IRT), item characteristic curves (ICCs) are illustrated through logistic models or normal ogive models, and the probability that examinees give the correct answer is usually a monotonically increasing function of their ability parameters. However, since only limited patterns of shapes can be obtained from logistic models…
Descriptors: Nonverbal Communication, Probability, Item Response Theory, Bayesian Statistics
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
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
Lee, Sik-Yum; Xia, Ye-Mao – Psychometrika, 2008
In this paper, normal/independent distributions, including but not limited to the multivariate t distribution, the multivariate contaminated distribution, and the multivariate slash distribution, are used to develop a robust Bayesian approach for analyzing structural equation models with complete or missing data. In the context of a nonlinear…
Descriptors: Structural Equation Models, Bayesian Statistics, Evaluation Methods, Evaluation Research
Cao, Jing; Stokes, S. Lynne – Psychometrika, 2008
According to the recent Nation's Report Card, 12th-graders failed to produce gains on the 2005 National Assessment of Educational Progress (NAEP) despite earning better grades on average. One possible explanation is that 12th-graders were not motivated taking the NAEP, which is a low-stakes test. We develop three Bayesian IRT mixture models to…
Descriptors: Test Items, Simulation, National Competency Tests, Item Response Theory
Rouder, Jeffrey N.; Lu, Jun; Sun, Dongchu; Speckman, Paul; Morey, Richard; Naveh-Benjamin, Moshe – Psychometrika, 2007
The theory of signal detection is convenient for measuring mnemonic ability in recognition memory paradigms. In these paradigms, randomly selected participants are asked to study randomly selected items. In practice, researchers aggregate data across items or participants or both. The signal detection model is nonlinear; consequently, analysis…
Descriptors: Simulation, Recognition (Psychology), Computation, Mnemonics

Laughlin, James E. – Psychometrika, 1979
This paper details a Bayesian alternative to the use of least squares and equal weighting coefficients in regression. An equal weight prior distribution for the linear regression parameters is described with regard to the conditional normal regression model, and resulting posterior distributions for these parameters are detailed. (Author/CTM)
Descriptors: Bayesian Statistics, Multiple Regression Analysis, Simulation, Statistical Bias

Arminger, Gerhard; Muthen, Bengt O. – Psychometrika, 1998
Nonlinear latent variable models are specified that include quadratic forms and interactions of latent regressor variable as special cases. To estimate the parameters, the models are put in a Bayesian framework with conjugate priors for the parameters. The proposed estimation methods are illustrated by two simulation studies. (SLD)
Descriptors: Algorithms, Bayesian Statistics, Estimation (Mathematics), Mathematical Models

Shi, Jian-Qing; Lee, Sik-Yum – Psychometrika, 1997
Explores posterior analysis in estimating factor score in a confirmatory factor analysis model with polytomous, censored or truncated data, and studies the accuracy of Bayesian estimates through simulation. Results support these Bayesian estimates for statistical inference. (SLD)
Descriptors: Bayesian Statistics, Estimation (Mathematics), Factor Structure, Scores
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