An automated item selection procedure in Mokken scale analysis partitions a set of items into one or more Mokken scales, if the data allow. Two algorithms are available that pursue the same goal of selecting Mokken scales of maximum length: Mokken's original automated item selection procedure (AISP) and a genetic algorithm (GA). Minimum…

Most dichotomous item response models share the assumption of latent monotonicity, which states that the probability of a positive response to an item is a nondecreasing function of a latent variable intended to be measured. Latent monotonicity cannot be evaluated directly, but it implies manifest monotonicity across a variety of observed scores,…

We propose three latent scales within the framework of nonparametric item response theory for polytomously scored items. Latent scales are models that imply an invariant item ordering, meaning that the order of the items is the same for each measurement value on the latent scale. This ordering property may be important in, for example,…

The performance of five simple multiple imputation methods for dealing with missing data were compared. In addition, random imputation and multivariate normal imputation were used as lower and upper benchmark, respectively. Test data were simulated and item scores were deleted such that they were either missing completely at random, missing at…

Short tests containing at most 15 items are used in clinical and health psychology, medicine, and psychiatry for making decisions about patients. Because short tests have large measurement error, the authors ask whether they are reliable enough for classifying patients into a treatment and a nontreatment group. For a given certainty level,…

The absence of the invariant item ordering (IIO) property in two nonparametric polytomous item response theory (IRT) models is discussed, and two nonparametric models are discussed that imply an IIO. Only two parametric polytomous IRT models are found to imply an IIO. A method is proposed to investigate whether an IIO is implied with empirical…

Used simulation to study the problem of missing item responses in tests and questionnaires when factor analysis is used to study the structure of the items. Factor loadings based on the EM algorithm best approximated the loading structure, with imputation of the mean per person across the scores for that person being the best alternative. (SLD)

Methods for detecting item score patterns that are unlikely (aberrant) given that a parametric item response theory (IRT) model gives an adequate description of the data or given the responses of the other persons in the group are discussed. The emphasis here is on the latter group of statistics. These statistics can be applied when a…

Methods for detecting item score patterns that are unlikely, given that a parametric item response theory model gives an adequate description of the data or given the responses of other persons in the group, are discussed. The use of person-fit statistics in empirical data analysis is briefly discussed. (SLD)

Reviews developments in nonparametric item-response theory (NIRT), from its historic origins in item-response theory (IRT) and scale analysis to new theoretical results for practical test construction. Discusses theoretical results from NIRT often relevant to IRT. Contains 134 references. (SLD)

Methods are discussed that can be used to investigate the fit of an item score pattern to a test model. Model-based tests and personality inventories are administered to more than 100 million people a year and, as a result, individual fit is of great concern. Item Response Theory (IRT) modeling and person-fit statistics that are formulated in the…

The person-response function (PRF) relates the probability of an individual's correct answer to the difficulty of items measuring the same latent trait. Local deviations of the observed PRF from the expected PRF indicate person misfit. We discuss two new approaches to investigate person fit. The first approach uses kernel smoothing to estimate…