The application of diagnostic classification models (DCMs) in the field of educational measurement is getting more attention in recent years. To make a valid inference from the model, it is important to ensure that the model fits the data. The purpose of the present study was to investigate the performance of the limited information…

Cognitive diagnosis models (CDMs) are latent class multidimensional statistical models that help classify people accurately by using a set of discrete latent variables, commonly referred to as attributes. These models require a Q-matrix that indicates the attributes involved in each item. A potential problem is that the Q-matrix construction…

Tatsuoka (1984) suggested several extended caution indices and their standardized versions that have been used as person-fit statistics by researchers such as Drasgow, Levine, and McLaughlin (1987), Glas and Meijer (2003), and Molenaar and Hoijtink (1990). However, these indices are only defined for tests with dichotomous items. This paper extends…

The information matrix can equivalently be determined via the expectation of the Hessian matrix or the expectation of the outer product of the score vector. The identity of these two matrices, however, is only valid in case of a correctly specified model. Therefore, differences between the two versions of the observed information matrix indicate…

Cognitive diagnostic models (CDMs) attempt to uncover latent skills or attributes that examinees must possess in order to answer test items correctly. The DINA (deterministic input, noisy "and") model is a popular CDM that has been widely used. It is shown here that a logistic version of the model can easily be fit with standard software for…

This paper discusses how full model dummy variables can be used with partial correlation or multiple regression procedures to compute matrices of pooled within-group correlations. (Author/CTM)

The restrictions on item difficulties that must be met when binomial models are applied to domain-referenced testing are examined. Both a deterministic and a stochastic conception of item responses are discussed with respect to difficulty and Guttman-type items. (Author/BH)

A strategy for overcoming problems with the Rasch model's inability to handle missing data involves a pairwise algorithm which manipulates the data matrix to separate out the information needed for the estimation of item difficulty parameters in a test. The method of estimation compares two or three items at a time, separating out the ability…

Empirical evidence is presented related to the effects of using a stratified sampling of items in multiple matrix sampling on the accuracy of estimates of the population mean. Data were obtained from a sample of 600 high school students for a 36-item mathematics test and a 40-item vocabulary test, both subtests of the Iowa Tests of Educational…

Researchers studying monitoring accuracy currently use two different indexes to estimate accuracy: relative accuracy and absolute accuracy. The authors compared the distributional properties of two measures of monitoring accuracy using Monte Carlo procedures that fit within these categories. They manipulated the accuracy of judgments (i.e., chance…

TAILOR is a computer program that uses the implied orders concept as the basis for computerized adaptive testing. The basic characteristics of TAILOR, which does not involve pretesting, are reviewed here and two studies of it are reported. One is a Monte Carlo simulation based on the four-parameter Birnbaum model and the other uses a matrix of…