Demonstrated, through simulation, that stationary autoregressive moving average (ARMA) models may be fitted readily when T>N, using normal theory raw maximum likelihood structural equation modeling. Also provides some illustrations based on real data. (SLD)

Proposed a maximum likelihood procedure for combining the standardized mean differences based on a noncentral-t-distribution and developed an EM algorithm. Simulation results favor the proposed procedure over the existing normal theory maximum likelihood procedure and the commonly used generalized least squares procedure. (SLD)

Using explicit formulas for the information matrix of maximum likelihood factor analysis under multivariate normal theory, gross and net information for estimating the parameters in a covariance structure gained by adding the associated mean structure are defined. (Author/SLD)

Describes the maximum likelihood (ML) estimation of mean and covariance structure models when data are missing. Describes expectation maximization (EM), generalized expectation maximization, Fletcher-Powell, and Fisher-scoring algorithms for parameter estimation and shows how software can be used to implement each algorithm. (Author/SLD)

Using simulation, studied the influence of each of 12 imputation methods and 2 methods using the EM algorithm on the results of maximum likelihood factor analysis as compared with results from the complete data factor analysis (no missing scores). Discusses why EM methods recovered complete data factor loadings better than imputation methods. (SLD)

Proposes a class of multivariate Tobit models with a factor structure on the covariance matrix. Such models are useful in the exploratory analysis of multivariate censored data and the identification of latent variables from behavioral data. The factor structure provides a parsimonious representation of the censored data. Models are estimated with…

A didactic discussion of covariance structure modeling in longitudinal studies with missing data is presented. Use of the full-information maximum likelihood method is considered for model fitting, parameter estimation, and hypothesis testing purposes, particularly when interested in patterns of temporal change as well as its covariates and…

This article introduces a bootstrap generalization to the Modified Parallel Analysis (MPA) method of test dimensionality assessment using factor analysis. This methodology, based on the use of Marginal Maximum Likelihood nonlinear factor analysis, provides for the calculation of a test statistic based on a parametric bootstrap using the MPA…

There is currently a gap in the consultation literature related to how teacher and student gender may affect teacher perceptions of and responses to student behavior. In this study, 147 preservice and practicing teachers were presented with four "gender-neutral" student-centered problems in the form of short vignettes in which the gender of the…

An approximation to the likelihood for the generalized linear models with random coefficients is derived and is the basis for an approximate Fisher scoring algorithm. The method is illustrated on the logistic regression model for one-way classification, but it has an extension to the class of generalized linear models and to more complex data…

If a parametric model for the ability distribution is not assumed, then the customary two-parameter and three-parameter logistic models for item response analysis present identifiability problems not encountered with the Rasch model. These problems impose substantial restrictions on possible models for ability distributions.

A stochastic model for paired comparisons of multi-attribute social stimuli is proposed where one objective is to find the relative importance of the attributes for a judge. Is is related to the Bradley-Terry model where log-parameters are linear combinations of functions of the stimuli's attributes. This model neither assumes that the functions…

The problem of estimating item parameters of latent trait models in a multistage testing design is considered. Using the Rasch model and conditional maximum likelihood estimates does not lead to solvable estimation equations, but the use of marginal maximum likelihood estimation leads to solvable equations for both Rasch and Birnbaum models. (TJH)

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)

A maximum likelihood method of estimating the parameters of the multiple factor model when data are missing from the sample is presented. A Monte Carlo study compares the method with five heuristic methods of dealing with the problem. The present method shows some advantage in accuracy of estimation. (Author/CTM)