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)

Presents a hierarchical Bayes approach to modeling parameter heterogeneity in generalized linear models. The approach combines the flexibility of semiparametric latent class models that assume common parameters for each subpopulation and the parsimony of random effects models that assume normal distributions for the regression parameters.…

Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute…

Coefficient alpha is a simple and very useful index of test reliability that is widely used in educational and psychological measurement. Classical statistical inference for coefficient alpha is well developed. This paper presents two methods for Bayesian statistical inference for a single sample alpha coefficient. An approximate analytic method…

Demonstrates the consequences on total misdiagnosis rates of using conventional (fixed) rather than flexible (Bayesian) rules when using the Minnesota Multiphasic Personality Inventory in diagnosis. Concludes that the range of base rates and the separation of distributions of normal and abnormal scores affect the difference in the misdiagnosis…

A Bayesian alternative to interpretations based on classical reliability theory is presented. Procedures are detailed for calculation of a posterior score and credible interval with joint consideration of item sample and occasion error. (Author/SLD)

This paper investigates methods that avoid using multiple groups to represent the missing data patterns in covariance structure modeling, attempting instead to do a single-group analysis where the only action the analyst has to take is to indicate that data is missing. A new covariance structure approach developed by B. Muthen and G. Arminger is…

In clinical trials, adaptive allocation means that the therapies assigned to the next patient or patients depend on the results obtained thus far in the trial. Although many adaptive allocation procedures have been proposed for clinical trials, few have actually used adaptive assignment, largely because classical frequentist measures of inference…

Probability-based inference in complex networks of interdependent variables is an active topic in statistical research, spurred by such diverse applications as forecasting, pedigree analysis, troubleshooting, and medical diagnosis. This paper concerns the role of Bayesian inference networks for updating student models in intelligent tutoring…

Bayesian methods for estimating dose response curves in quantal bioassay are studied. A linearized multi-stage model is assumed for the shape of the curves. A Gibbs sampling approach with data augmentation is employed to compute the Bayes estimates. In addition, estimation of the "relative additional risk" and the "risk specific…

Results from two Monte Carlo studies in item response theory (comparisons of computer item analysis programs and Bayes estimation procedures) are analyzed with inferential methods to illustrate the procedures' strengths. It is recommended that researchers in item response theory use both descriptive and inferential methods to analyze Monte Carlo…

A Bayesian approach to sensitivity of inferences to possible outliers involves recalculating marginal posterior distributions of parameters of interest under assumptions of heavy tails. This strategy is implemented in the hierarchical model setting through Gibbs sampling, a Monte Carlo technique, and illustrated through a reanalysis of data on…

The quality of approximations to first- and second-order moments based on latent ability estimates is discussed. The ability estimates are based on the Rasch or the two-parameter logistic model, and true score theory is used to account for the fact that the basic quantities are estimates. (SLD)

Two psychometric models are presented for evaluating the difficulty of the distractors in multiple-choice items. They are based on the criterion of rising distractor selection ratios, which facilitates interpretation of the subject and item parameters. Statistical inferential tools are developed in a Bayesian framework: modal a posteriori…

The challenges of modeling students' performance in simulation-based assessments include accounting for multiple aspects of knowledge and skill that arise in different situations and the conditional dependencies among multiple aspects of performance in a complex assessment. This paper describes a Bayesian approach to modeling and estimating…