Investigated the data demands associated with the marginal maximum likelihood (MML) expected a posterior (EAP) methodology and the precision of the resulting parameter estimates when data fit the underlying model through simulation. Also studied the extent to which a misspecified prior distribution would affect the item and person parameter…

Examined alternative ways of specifying models in the complier average causal effects (CACE) estimation method when the major interest is in estimating causal effects of treatments for compliers. Explored modeling possibilities of CACE estimation in a maximum likelihood-expectation maximization framework in the presence of covariate information.…

Notes that the article reviewed contributes to the expanding literature on noncompliance by explicating the assumptions involving covariates that can be used to identify maximum likelihood estimates in place of exclusion restrictions. Notes points that require further discussion. (SLD)

Generalizing Boolean matrix decomposition to a larger class of matrix decomposition models is demonstrated, and probability matrix decomposition (PMD) models are introduced as a probabilistic version of the larger class. An algorithm is presented for the computation of maximum likelihood and maximum a posteriori estimates of the parameters of PMD…

The success of applications of item response theory (IRT) depends upon the properties of the estimates of model parameters. Many theoretical properties of these estimates have been extensively studied. However, the properties of estimates obtained empirically from real data depend not only on the theoretical results, but also on the data and the…

A new parametric maximum likelihood procedure is proposed for estimating ultrametric trees for the analysis of conditional rank order proximity data. Technical aspects of the model and the estimation algorithm are discussed, and Monte Carlo results illustrate its application. A consumer psychology application is also examined. (SLD)

Discusses a full-information maximum likelihood method for fitting a multidimensional latent variable model to a set of ordinal observed variables. Also discusses estimating the model, scoring persons on the latent dimensions, and goodness of fit. Applies the method to a data set of attitudes of 392 respondents toward technology. (SLD)

The real problems of contextual analysis concern the conceptualization of contextual effects, the kinds of data with which to estimate them, and the selection and implementation of appropriate statistical techniques. This paper focuses on detection; specifically, an approach to contextual analysis based on the estimation and interpretation of a…

Latent class models for mastery testing differ from continuum models in that they do not postulate a latent mastery continuum but conceive mastery and non-mastery as two latent classes, each characterized by different probabilities of success. Several researchers use a simple latent class model that is basically a simultaneous application of the…

The third section of this two-volume report examines the utility of the model developed in section I for application in a classroom testing situation. The model was applied in arithmetic classes through a weekly testing program. The teacher specified the generic task being taught, tests were constructed using a table of random numbers to select…