Social desirability bias (SDB) is a common threat to the validity of conclusions from responses to a scale or survey. There is a wide range of person-fit statistics in the literature that can be employed to detect SDB. In addition, machine learning classifiers, such as logistic regression and random forest, have the potential to distinguish…

Oftentimes in many fields of the social and natural sciences, data are obtained within a nested structure (e.g., students within schools). To effectively analyze data with such a structure, multilevel models are frequently employed. The present study utilizes a Monte Carlo simulation to compare several novel multilevel classification algorithms…

The purpose of this study was to evaluate the degree of classification quality in the basic latent class model when covariates are either included or are not included in the model. To accomplish this task, Monte Carlo simulations were conducted in which the results of models with and without a covariate were compared. Based on these simulations,…

This study seeks to compare fixed and mixed effects models for the purposes of predictive classification in the presence of multilevel data. The first part of the study utilizes a Monte Carlo simulation to compare fixed and mixed effects logistic regression and random forests. An applied examination of the prediction of student retention in the…

The current study proposes novel methods to predict multistage testing (MST) performance without conducting simulations. This method, called MST test information, is based on analytic derivation of standard errors of ability estimates across theta levels. We compared standard errors derived analytically to the simulation results to demonstrate the…

When conducting item reviews, analysts evaluate an array of statistical and graphical information to assess the fit of a field test (FT) item to an item response theory model. The process can be tedious, particularly when the number of human reviews (HR) to be completed is large. Furthermore, such a process leads to decisions that are susceptible…

Proposes the use of the group overlap concept as a basis for determining effect size. Group overlap may be assessed via prediction of group assignment, using predictive discriminant analysis. The effect-size index proposed is that of improvement-over-chance "(I)" classification. Makes some suggestions for cutoffs of "I" values…

A procedure is described that involves iterative use of univariable optimal discriminant analysis (UniODA) to construct a classification tree model for discriminating observations from different groups. The procedure is illustrated using an application that involved discriminating 125 geriatric and nongeriatric patients on the basis of their…

Shows how a theorem proven by H. Brogden (1951, 1959) can be used to estimate the allocation average (a predictor based classification of a test battery) assuming that the predictor intercorrelations and validities are known and that the predictor variables have a joint multivariate normal distribution. (SLD)

The potential importance of suppressor variables in the personality domain was studied in classifying white-collar criminals with a validation sample of 435 prisoners and a cross-validation sample of 214. Results suggest that suppressor variables can increase prediction and may contribute to knowledge and theory development. (SLD)

An optimal unbiased classification rule is proposed based on a longitudinal model for the measurement of change in ability. In general, the rule predicts future level of knowledge by using information about level of knowledge at entrance, its rate of growth, and the amount of within-individual variation. (SLD)