It is shown that it is not easy to apply the EM algorithm to latent class models in the general case with equality constraints because a nonlinear equation has to be solved. A simpler condition is given in which the EM algorithm can be easily applied. (SLD)

Latent class analysis is formulated as a problem of estimating parameters in a finite mixture distribution. The EM algorithm is used to find the maximum likelihood estimates, and the case of categorical variables with more than two categories is considered. (Author)

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)

A general approach is described for the analysis of categorical data when there are missing values on one or more observed variables. The method is based on generalized linear models with composite links. Situations in which the model can be used are described. (SLD)

Latent trait test models for responses to dichotomously scored items are considered from the point of view of parameter estimation using a Bayesian statistical approach and the EM estimation algorithm. An example using the Rasch model is presented. (Author/JKS)

An algorithm based on the majorization theory of J. de Leeuw and W. J. Heiser is presented for fitting the slide-vector model. It views the model as a constrained version of the unfolding model. A three-way variant is proposed, and two examples from market structure analysis are presented. (SLD)

This paper presents a detailed description of maximum parameter estimation for item response models using the general EM algorithm. In this paper the models are specified using a univariate discrete latent ability variable. When the latent ability variable is discrete the distribution of the observed item responses is a finite mixture, and the EM…

The probability integral of the multivariate normal distribution (ND) has received considerable attention since W. F. Sheppard's (1900) and K. Pearson's (1901) seminal work on the bivariate ND. This paper evaluates the formula that represents the "n x n" correlation matrix of the "chi(sub i)" and the standardized multivariate…

An algorithm for obtaining initial values for the minimization process in covariance structure analysis is developed that is more generally applicable for computing parameters connected to latent variables than the currently existing ones. The algorithm is formulated in terms of the RAM model but can be extended. (SLD)

An alternating least squares algorithm is offered for fitting the DEcomposition into DIrectional COMponents (DEDICOM) model for representing asymmetric relations among a set of objects via a set of coordinates for the objects on a limited number of dimensions. An algorithm is presented for fitting the IDIOSCAL model in the least squares sense.…

Item response curves for a set of binary responses are studied from a Bayesian viewpoint of estimating the item parameters. For the two-parameter logistic model with normally distributed ability, restricted bivariate beta priors are used to illustrate the computation of the posterior mode via the EM algorithm. (Author/LMO)

A stochastic multidimensional scaling procedure is presented for analysis of three-mode, three-way pick any/"J" data. The procedure fits both vector and ideal-point models and characterizes the effect of situations by a set of dimension weights. An application in the area of consumer psychology is discussed. (SLD)

Item response curves for a set of binary responses are studied from a Bayesian viewpoint of estimating the item parameters. For the two-parameter logistic model with normally distributed ability, restricted bivariate beta priors are used to illustrate the computation of the posterior mode via the EM algorithm. The procedure is illustrated by data…

Maximum likelihood procedures are presented for a general model to unify the various models and techniques that have been proposed for item analysis. Unconditional maximum likelihood estimation, proposed by Wright and Haberman, and conditional maximum likelihood estimation, proposed by Rasch and Andersen, are shown as important special cases. (JAZ)

This article describes a Bayesian framework for estimation in item response models, with two-stage distributions on both item and examinee populations. Strategies for point and interval estimation are discussed, and a general procedure based on the EM algorithm is presented. (Author/LMO)