Confirmatory factor analysis is considered from a Bayesian viewpoint, in which prior information concerning parameters is incorporated in the analysis. An interactive algorithm is developed to obtain the Bayesian estimates. A numerical example is presented. (Author/JKS)

An Extended Two-Way Euclidean Multidimensional Scaling (MDS) model that assumes both common and specific dimensions is described and contrasted with the "standard" (Two-Way) MDS model. Illustrations with both artificial and real data on the judged similarity of nations are provided. (TJH)

A longitudinal factor analysis model that is entirely exploratory is proposed for use with multiple populations. Factorial collapse, period/practice effects, and an invariant and/or stationary factor pattern are allowed. The model is formulated stochastically and implemented via a stage-wise EM algorithm. (TJH)

The details of EM algorithms for maximum likelihood factor analysis are presented for both the exploratory and confirmatory models. An example is presented to demonstrate potential problems in other approaches to maximum likelihood factor analysis. (Author/JKS)

A jackknife-like procedure is developed for producing standard errors of estimate in maximum likelihood factor analysis. Unlike earlier methods based on information theory, the procedure developed is computationally feasible on larger problems. Examples are given to demonstrate the feasibility of the method. (Author/JKS)

Rubin and Thayer recently presented equations to implement maximum likelihood estimation in factor analysis via the EM algorithm. It is argued here that the advantages of using the EM algorithm remain to be demonstrated. (Author/JKS)

The authors respond to a criticism of their earlier article concerning the use of the EM algorithm in maximum likelihood factor analysis. Also included are the comments made by the reviewers of this article. (JKS)

Two algorithms for marginal maximum likelihood estimation for the Rasch model are provided. The more efficient of the two algorithms is extended to estimation for the linear logistic model. Numerical examples of both procedures are presented. (Author/JKS)

L. A. Goodman's loglinear formulation for bi-way contingency tables is extended to tables with or without missing cells and is used for exploratory purposes. Three-way tables and generalizations of correspondence analysis are deduced, and a generalized version of Goodman's algorithm is used to estimate scores in all cases. (Author/TJH)

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…

A general approach for fitting a model to a data matrix by weighted least squares (WLS) is studied. The approach consists of iteratively performing steps of existing algorithms for ordinary least squares fitting of the same model and is based on maximizing a function that majorizes WLS loss function. (Author/SLD)

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)

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)

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)

An extension of component analysis to longitudinal or cross-sectional data is presented. Components are derived under the restriction of invariant and/or stationary compositing weights. Multiple occasion and multiple group analyses, the computing algorithm, component pattern and structure matrices, and an example are discussed. (TJH)