Developed a full maximum likelihood method for obtaining joint estimates of variances and correlations among continuous and polytomous variables with incomplete data that are missing at random with an ignorable missing mechanism. Simulation results and an empirical example illustrate the approach. (SLD)

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…

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…

Simulated items from two test forms using joint maximum likelihood estimation (JMLE) and marginal maximum likelihood estimation (MML) in the vertical equating situation (using an anchor test) when data were nonrandomly missing. Under MML, when the different ability parameters of students were not taken into account, the item difficulty parameters…

Proposes an estimation method for the Rasch model that is based on the pseudolikelihood theory of B. Arnold and D. Strauss (1988). Simulation results show great similarity between estimates from this method with those from conditional maximum likelihood and unconditional maximum likelihood estimates for the item parameters of the Rasch model. (SLD)

The generalized graded unfolding model (GGUM) (J. Roberts, J. Donoghue, and J. Laughlin, 1998) is an item response theory model designed to analyze binary or graded responses that are based on a proximity relation. The purpose of this study was to assess conditions under which item parameter estimation accuracy increases or decreases, with special…

Developed two methods for estimating the individual level correlation coefficient that combines information from aggregate data with a small fraction of the individual level data. Results of a simulation study support the use of these methods. (SLD)

Developed an EM type algorithm for maximum likelihood estimation of a general nonlinear structural equation model in which the E-step is completed by a Metropolis-Hastings algorithm. Illustrated the methodology with results from a simulation study and two real examples using data from previous studies. (SLD)

Proposes a two-stage estimation method for the analysis of covariance structure models with ordinal ipsative data (OID). A goodness-of-fit statistic is given for testing the hypothesized covariance structure matrix, and simulation results show that the method works well with a large sample. (SLD)

Properties of the pseudolikelihood method of estimating Rasch model item parameters, which is based on comparing responses to pairs of items without regard to other items, are studied. Simulation found pseudolikelihood estimates comparable to conditional and marginal maximum likelihood estimates. (SLD)

The Rasch Poisson counts model is a latent trait model for the situation in which "K" tests are administered to "N" examinees and the test score is a count (repeated number of some event). A mixed model is presented that applies the EM algorithm and that can allow for missing data. (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…

This study investigated the effect on examinees' ability estimate under item response theory (IRT) when they are presented an item, have ample time to answer the item, but decide not to respond to the item. Simulation data were modeled on an empirical data set of 25,546 examinees that was calibrated using the 3-parameter logistic model. The study…

A simulation study was conducted to investigate the application of expected a posteriori (EAP) trait estimation in computerized adaptive tests (CAT) based on the partial credit model and compare it with maximum likelihood estimation (MLE). Results show the conditions under which EAP and MLE provide relatively accurate estimation in CAT. (SLD)

The utility of the three-form design coupled with maximum likelihood methods for estimation of missing values was evaluated. Simulation studies demonstrate that maximum likelihood estimation and multiple imputation methods produce the most efficient and least biased estimates of variances and covariances for normally distributed and slightly…