Trait emotional intelligence refers to a constellation of emotional self-perceptions located at the lower levels of personality hierarchies. In 2 studies, we sought to examine the psychometric properties of the Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF; Petrides, 2009) using item response theory (IRT). Study 1 (N= 1,119, 455 men) showed that most items had good discrimina...

This paper follows previous research we have already performed in the area of Bayesian networks models for CAT. We present models using Item Response Theory (IRT standard CAT method), Bayesian networks, and neural networks. We conducted simulated CAT tests on empirical data. Results of these tests are presented for each model separately and compared.

We introduce Item Response Theory (IRT) from psychometrics as an alternative to majority voting to create an IRT gold standard (GSIRT ). IRT describes characteristics of individual items in GSIRT their difficulty and discriminating power and is able to account for these characteristics in its estimation of human intelligence or ability for an NLP task. In this paper, we evaluated IRT’s model-fi...

To overcome problems with previous psychometric approaches to hypnosis scales, the authors applied full-information factor analysis, based on multidimensional item-response theory (IRT), to a 39-year sample of 11,517 records of the Harvard Group Scale of Hypnotic Susceptibility, Form A (HGSHS:A). They also performed a comparable analysis on the standardization sample of the Waterloo-Stanford Gr...

An item response theory (IRT) model is used as a measurement error model for the dependent variable of a multilevel model. The dependent variable is latent but can be measured indirectly by using tests or questionnaires. The advantage of using latent scores as dependent variables of a multilevel model is that it offers the possibility of modelling response variation and measurement error and se...

What it is now known as item response modeling [for an overview see van der Linden and Hambleton (1997)] originated as an effort to overcome the limitations of the factor model when applied to test items. Test items are most often categorical in nature, whereas the factor model was designed for continuous data. Unfortunately, over the years item response modeling and factor modeling have develo...

This work presents a model for student knowledge diagnosis that can be used in ITSs for student model update. The diagnosis is accomplished through Computerized Adaptive Testing (CAT). CATs are assessment tools with theoretical background. They use an underlying psychometric theory, the Item Response Theory (IRT), for question selection, student knowledge estimation and test finalization. In pr...

Title of Dissertation: IRT VS. FACTOR ANALYSIS APPROACHES IN ANALYZING MULTIGROUP MULTIDIMENSIONAL BINARY DATA: THE EFFECT OF STRUCTURAL ORTHOGONALITY, AND THE EQUIVALENCE IN TEST STRUCTURE, ITEM DIFFICULTY, & EXAMINEE GROUPS Peng Lin, Doctor of Philosophy, 2008 Directed By: Professor Robert W. Lissitz Department of Measurement, Statistics and Evaluation The purpose of this study was to investi...

This Monte Carlo study assessed Type I error in differential item functioning analyses using Lord’s chi-square (LC), Likelihood Ratio Test (LRT), and Mantel-Haenszel (MH) procedure. Two research interests were investigated: response theory (IRT) model specification LC the LRT continuity correction MH enhances literature by investigating correct incorrect model-data fit comparing those results t...

This paper compares three approaches for solving the problem of measurement error in a hierarchical gain score model. The pre-test and post-test scores are IRT scores with measurement error. Explanatory variables at student level and class level are considered in the model. Simulation results show that the gain score model that does not consider measurement error overestimates the explanatory v...