Sensitivity analyses encompass a broad set of post-analytic techniques that are characterized as measuring the potential impact of any factor that has an effect on some output variables of a model. This research focuses on the utility of the simulated annealing algorithm to automatically identify path configurations and parameter values of omitted…

Cheating detection in large-scale assessment received considerable attention in the extant literature. However, none of the previous studies in this line of research investigated the stacking ensemble machine learning algorithm for cheating detection. Furthermore, no study addressed the issue of class imbalance using resampling. This study…

Metaheuristics are optimization algorithms that efficiently solve a variety of complex combinatorial problems. In psychological research, metaheuristics have been applied in short-scale construction and model specification search. In the present study, we propose a bee swarm optimization (BSO) algorithm to explore the structure underlying a…

Recent approaches to the detection of cheaters in tests employ detectors from the field of machine learning. Detectors based on supervised learning algorithms achieve high accuracy but require labeled data sets with identified cheaters for training. Labeled data sets are usually not available at an early stage of the assessment period. In this…

We develop a "latent variable forest" (LV Forest) algorithm for the estimation of latent variable scores with one or more latent variables. LV Forest estimates unbiased latent variable scores based on "confirmatory factor analysis" (CFA) models with ordinal and/or numerical response variables. Through parametric model…

We conducted an extensive Monte Carlo study of factor-rotation local solutions (LS) in multidimensional, two-parameter logistic (M2PL) item response models. In this study, we simulated more than 19,200 data sets that were drawn from 96 model conditions and performed more than 7.6 million rotations to examine the influence of (a) slope parameter…

In real-world situations, multidimensional data may appear on large-scale tests or psychological surveys. The purpose of this study was to investigate the effects of the quantity and magnitude of cross-loadings and model specification on item parameter recovery in multidimensional Item Response Theory (MIRT) models, especially when the model was…

The questionnaire method has always been an important research method in psychology. The increasing prevalence of multidimensional trait measures in psychological research has led researchers to use longer questionnaires. However, questionnaires that are too long will inevitably reduce the quality of the completed questionnaires and the efficiency…

An algorithm for estimation of means and standard deviations or raw scores underlying table values of standard test scores is presented. An application of a computer program, operationalizing the suggested algorithm, is discussed within the framework of a comparison of estimated and known table values for sample Minnesota Multiphasic Personality…

An empirical method (PRESS) for examining and contrasting the cross-validated prediction accuracies of some popular algorithms for weighting predictor variables was advanced and examined. The sample specific PRESS method was suggested as most appropriate when selecting among several alternative weighting algorithms to achieve maximum…

This paper presents an algorithm for determining the number of items to measure each objective in a criterion-referenced test when testing time is fixed and when the objectives vary in their levels of importance, reliability, and validity. Results of four special applications of the algorithm are presented. (BS)

Studied the ability of logical test design (LTD) to predict student performance in reading Roman numerals for 211 sixth graders in Mexico City tested on Roman numeral items varying on LTD-related and non-LTD-related variables. The LTD-related variable item iterativity was found to be the best predictor of item difficulty. (SLD)

The extent to which adolescents diagnosed with one or more personality disorders through the Personality Diagnostic Questionnaire-Revised were at an elevated risk of substance abuse was studied with 441 undergraduates. Those with one or more personality disorders showed higher levels of psychiatric symptomatology and substance use. (SLD)