This study aims to analyze the differential bundle functioning in multidimensional tests with a specific purpose to detect this effect through differentiating the location of the item with DIF in the test, the correlation between the dimensions, the sample size, and the ratio of reference to focal group size. The first 10 items of the test that is…

Modeling multidimensional test data with a unidimensional model can result in serious statistical errors, such as bias in item parameter estimates. Many methods exist for assessing the dimensionality of a test. The current study focused on DIMTEST. Using simulated data, the effects of sample size splitting for use with the ATFIND procedure for…

Recent research suggests that competitive work environments may influence individual's attitudes, behaviors, stress, and performance. Unfortunately, adequate measures of competitive environments are lacking. This article traces the development of a new multidimensional competitive work environment scale. An initial 59-item pool covering five…

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…

The G index of agreement does not permit the use of various weights for its various items. The weighted G index described here, make it possible to use unequal weights. An example of the procedure is provided. (Author/JKS)

Introduces a new approach for partitioning test items into dimensionally distinct item clusters. The core of this approach is a new item-pair conditional-covariance-based proximity measure that can be used with hierarchical cluster analysis. The procedure can correctly classify, on average, over 90% of the items for correlations as high as 0.9.…

Generalizations of the G index as a measure of similarity between persons beyond the dichotomous situation are discussed. An attempt is made to present a generalization that does not require dichotomization of the items for cases where the number of response alternatives may differ. (Author/JKS)

The purpose of this paper is to further the cause of clarifying construct interpretations of tests, by proposing that non-metric multidimensional scaling may be more useful than factor analysis or other latent structure models for investigating the internal structure of tests. It also suggests that typical problems associated with scaling…

DETECT, the acronym for Dimensionality Evaluation To Enumerate Contributing Traits, is an innovative and relatively new nonparametric dimensionality assessment procedure used to identify mutually exclusive, dimensionally homogeneous clusters of items using a genetic algorithm ( Zhang & Stout, 1999). Because the clusters of items are mutually…

Academic psychology has long been composed of two disciplines, one experimental and one correlational. These two disciplines each developed their own method of studying structure in data: multidimensional scaling (MDS) and factor analysis. Both methods use similar kinds of input data, proximity measures on object pairs. Both represent the object…

In order to determine the effectiveness of multidimensional scaling (MDS) in recovering the dimensionality of a set of dichotomously-scored items, data were simulated in one, two, and three dimensions for a variety of correlations with the underlying latent trait. Similarity matrices were constructed from these data using three margin-sensitive…

Various methods used to assess the content of a test are reviewed, and a new procedure designed to improve on these methods is presented. The two tests considered are a professional licensure examination, the auditing section of the Uniform Certified Public Accountant Examination, and an educational achievement test, a nationally standardized…