Akaike's information criterion is suggested as a tool for evaluating fit and dimensionality in metric multidimensional scaling that uses least squares methods of estimation. This criterion combines the least squares loss function with the number of estimated parameters. Numerical examples are presented. The results from analyses of both simulation…

Motivated by Gibbons et al.'s (Appl. Psychol. Meas. 31:4-19, "2007") full-information maximum marginal likelihood item bifactor analysis for polytomous data, and Rijmen, Vansteelandt, and De Boeck's (Psychometrika 73:167-182, "2008") work on constructing computationally efficient estimation algorithms for latent variable…

Investigated the usefulness of multidimensional scaling (MDS) for assessing the dimensionality of dichotomous test data. Focused on two MDS proximity measures, one based on the PC statistic (T. Chen and M. Davidson, 1996) and other, on interitem Euclidean distances. Simulation results show that both MDS procedures correctly identify…

For the exploratory analysis of a matrix of proximities or (dis)similarities between objects, one often uses cluster analysis (CA) or multidimensional scaling (MDS). Solutions resulting from such analyses are sometimes interpreted using external information on the objects. Usually the procedures of CA, MDS and using external information are…

Presents an approach to multidimensional unfolding that reduces the occurrence of degenerate solutions and conducts a Monte Carlo study to demonstrate the superiority of the new method to the ALSCAL and KYST nonmetric procedures for student preference data. (SLD)

A method for multidimensional scaling that is highly resistant to the effects of outliers is described. Some Monte Carlo simulations illustrate the efficacy of the procedure, which performs well with or without outliers. (SLD)

Most researchers agree that psychological/educational tests are sensitive to multiple traits, implying the need for a multidimensional item response theory (MIRT). One limitation of applying a MIRT in practice is the difficulty in establishing equivalent scales of multiple traits. In this study, a new MIRT linking method was proposed and evaluated…

In many cross-cultural research studies, assessment instruments are translated or adapted for use in multiple languages. However, it cannot be assumed that different language versions of an assessment are equivalent across languages. A fundamental issue to be addressed is the comparability or equivalence of the construct measured by each language…

A Monte Carlo study was conducted to investigate the robustness of the assumed error distribution in maximum likelihood estimation models for multidimensional scaling. Results show that violations of the assumed error distribution have virtually no effect on the estimated distance parameters. (SLD)

Recently, nonparametric methods have been proposed that provide a dimensionally based description of test structure for tests with dichotomous items. Because such methods are based on different notions of dimensionality than are assumed when using a psychometric model, it remains unclear whether these procedures might lead to a different…

One of the major assumptions of latent trait theory is that the items in a test measure a single dimension. This report describes an investigation of procedures for forming a set of items that meet this assumption. Factor analysis, nonmetric multidimensional scaling, cluster analysis and latent trait analysis were applied to simulated and real…

Computer Adaptive Sequential Testing (CAST) is a test delivery model that combines features of the traditional conventional paper-and-pencil testing and item-based computerized adaptive testing (CAT). The basic structure of CAST is a panel composed of multiple testlets adaptively administered to examinees at different stages. Current applications…