When exploring missing data techniques in a realistic scenario, the current literature is limited: most studies only consider consequences with data missing on a single variable. This simulation study compares the relative bias of two commonly used missing data techniques when data are missing on more than one variable. Factors varied include type…

Analyzing examinees' responses using cognitive diagnostic models (CDMs) has the advantage of providing diagnostic information. To ensure the validity of the results from these models, differential item functioning (DIF) in CDMs needs to be investigated. In this article, the Wald test is proposed to examine DIF in the context of CDMs. This study…

DETECT is a nonparametric, conditional covariance-based procedure to identify dimensional structure and the degree of multidimensionality of test data. The ability composite or conditional score used to estimate conditional covariance plays a significant role in the performance of DETECT. The number correct score of all items in the test (T) and…

The purpose of this study was to assess the dimensionality of attitudinal data arising from unfolding models for discrete data and to compute rough estimates of item and individual parameters for use as starting values in other estimation parameters. One- and two-dimensional simulated test data were analyzed in this study. Results of limited…

A modification of the SIBTEST procedure to assess differential item functioning (DIF) for two-dimensional test data (i.e., data for tests where two intended abilities are tapped by test items) is described. A small simulation study is carried out to assess the performance of the modified SIBTEST to detect DIF in such two-dimensional data. The…

Investigated the performance of the Poly-DIMTEST (PD) procedure (and associated computer program) in assessing the unidimensionality of test data produced by polytomous items through Monte Carlo simulation. Results show that PD can confirm unidimensionality for unidimensional simulated data and can detect lack of unidimensionality. (SLD)

A new procedure, CATSIB, for assessing differential item functioning (DIF) on computerized adaptive tests (CATs) is proposed. CATSIB, a modified SIBTEST procedure, matches test takers on estimated ability and controls for impact-induced Type 1 error inflation by employing a CAT version of the IBTEST "regression correction." The…

CATSIB is a differential item functioning (DIF) assessment methodology for computerized adaptive test (CAT) data. Kernel smoothing (KS) is a technique for nonparametric estimation of item response functions. In this study an attempt has been made to develop a more efficient DIF procedure for CAT data, KS-CATSIB, by combining CATSIB with kernel…

Performance in assessing the unidimensionality of tests was examined for four methods: (1) W. F. Stout's procedure (1987); (2) the approach of P. W. Holland and P. R. Rosenbaum (1986); (3) linear factor analysis; and (4) non-linear factor analysis. Each method was examined and compared with the others using simulated and real test data. Seven data…

The theoretical differences between the traditional definition of dimensionality and the more recently defined notion of essential dimensionality are presented. Monte Carlo simulations are used to demonstrate the utility of W. F. Stout's procedure to assess the essential unidimensionality of the latent space underlying a set of terms. The…

Computerized adaptive tests (CATs) pose major obstacles to the traditional assessment of differential item functioning (DIF). This paper proposes a modification of the SIBTEST DIF procedure for CATs, called CATSIB. CATSIB matches test takers on estimated ability based on unidimensional item response theory. To control for impact-induced Type I…

Using simulated and real data, this study compares the performance of three methodologies for assessing unidimensionality: (1) DIMTEST; (2) the approach of Holland and Rosenbaum; and (3) nonlinear factor analysis. All three models correctly confirm unidimensionality, but they differ in their ability to detect the lack of unidimensionality.…

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…

The performance of the following four methodologies for assessing unidimensionality was examined: (1) DIMTEST; (2) the approach of P. W. Holland and P. R. Rosenbaum; (3) linear factor analysis; and (4) non-linear factor analysis. Each method is examined and compared with other methods using simulated data sets and real data sets. Seven data sets,…

DIMTEST is a statistical test procedure for assessing essential unidimensionality of binary test item responses. The test statistic T used for testing the null hypothesis of essential unidimensionality is a nonparametric statistic. That is, there is no particular parametric distribution assumed for the underlying ability distribution or for the…