A general dichotomous condition is derived for the unidimensional polytomous Rasch model. The robustness of the dichotomous analysis is investigated in a simulation study. The model shows a close relation with the two-parameter Birnbaum model. (TJH)

A simulation study compares nonhierarchical clustering capabilities of a class of neural networks using Kohonen learning with a K-means clustering procedure. The focus is on the ability of the procedures to recover correctly the known cluster structure in the data. Advantages and disadvantages of the procedures are reviewed. (SLD)

Some uniqueness properties are presented for the PARAFAC2 model for covariance matrices, focusing on uniqueness in the rank two case of PARAFAC2. PARAFAC2 is shown to be usually unique with four matrices, but not unique with three unless a certain additional assumption is introduced. (SLD)

A complete enumeration method and a Monte Carlo method are presented to calculate the probability distribution of arbitrary statistics of adjacency matrices when these matrices have the uniform distribution conditional on given row and column sums, and possibly on a given set of structural zeros. (SLD)

A variety of paired comparison, triple comparison, and ranking experiments are discussed as generalized linear models. All such models can be easily fit by maximum likelihood using the GLIM computer package. Examples are presented for a variety of cases using GLIM. (SLD)

A Fisher's Z transformation is developed for the corrected correlation for conditions when the criterion data are missing because of selection on the predictor and when the criterion was missing at random, not because of selection. The two Z transformations were evaluated in a computer simulation and found accurate. (SLD)

Smallest exact confidence intervals for the ability parameter of the Rasch model are derived and compared to the traditional asymptotically valid intervals based on Fisher information. Tables of exact confidence intervals, termed Clopper-Pearson intervals, can be drawn up with a computer program developed by K. Klauer. (SLD)

Satisfactory results for interpersonally incomparable ordinal survey responses can be obtained by assuming that rankings are based upon a set of multivariate normal latent variables that satisfy factor or ideal point models of choice. Two statistical methods based upon those assumptions are described and illustrated via simulations. (TJH)

Describes algorithms used in the computer program LOGIMO for obtaining maximum likelihood estimates of the parameters in loglinear models. These algorithms are also useful for the analysis of loglinear item-response theory models. Presents modified versions of the iterative proportional fitting and Newton-Raphson algorithms. Simulated data…

An adaptive testing algorithm is presented based on an alternative modeling framework, and its effectiveness is investigated in a simulation based on real data. The algorithm uses a latent class modeling framework in which assessed latent attributes are assumed to be categorical variables. (SLD)

Kernel smoothing methods for nonparametric item characteristic curve estimation are reviewed. A simulation with 500 examinees and real data from 3,000 records of the Graduate Record Examination illustrate the rapidity of kernel smoothing. Even when population curves are three-parameter logistic, simulation suggests no loss of efficiency. (SLD)

Hierarchical Bayes procedures for the two-parameter logistic item response model were compared for estimating item and ability parameters through two joint and two marginal Bayesian procedures. Marginal procedures yielded smaller root mean square differences for item and ability, but results for larger sample size and test length were similar.…

Asymptotic distributions of the estimators of communalities are derived for the maximum likelihood method in factor analysis. It is shown that equating the asymptotic standard error of the communality estimate to the unique variance estimate is not correct for the unstandardized case. Monte Carlo simulations illustrate the study. (SLD)

The algorithm developed by B. A. Schneider (1980) for analysis of paired comparisons of psychological intervals is replaced by one proposed by R. M. Johnson. Monte Carlo simulations of pairwise dissimilarities and pairwise conjoint effects show that Johnson's algorithm can provide good metric recovery. (SLD)

New methods for comparing the medians corresponding to independent treatment groups are suggested. Procedures are based on the estimator of F. Harrell and C. Davis in conjunction with a modification and extension of the bootstrap calibration technique suggested by W. Loh. Data from two groups provide an illustration. (SLD)