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Technology-Based Selection
Published in Michael D. Coovert, Lori Foster Thompson, The Psychology of Workplace Technology, 2013
Alan D. Mead, Julie B. Olson-Buchanan, Fritz Drasgow
A CAT starts with some initial guess about the examinee's ability (designated by the Greek letter theta with a caret, θ̂), typically the mean ability of the applicant population. Two steps are then repeated: (1) using IRT, an item is selected from among the most informative items given the current ability estimate, θ̂; and (2) the item is administered and is updated. Note, θ̂ increases following correct answers and decreases following incorrect answers. The CAT stops when it reaches a preset number of items, or a preset level of precision, a time limit, or similar stopping rule. Given a large pool of items calibrated using IRT, a CAT substantially shorter than a fixed test can produce more reliable scores (Weiss, 1982).
Designing Developmentally Tailored Driving Assessment Tasks
Published in Gavriel Salvendy, Advances in Human Aspects of Road and Rail Transportation, 2012
Erik Roelofs, Marieke van Onna, Karel Brookhuis, Maarten Marsman, Leo de Penning
IRT offers the possibility to select test items that are tuned to the level of ability of the learner. In the current project we aimed to identify a collection of driving tasks that differ in terms of task demands. However, IRT analyses are useless without sound item construction, based on elaborated ideas of task difficulty. Without those ideas, the range of task difficulties may be too narrow, or the average task difficulty may be off target. The Student model described above specifies these ideas on task difficulty.
Assessing Psychometric Scale Properties of Patient Safety Culture
Published in Patrick Waterson, Patient Safety Culture, 2018
Jeanette Jackson, Theresa Kline
The purpose of this study was to probe deeper into the psychometric properties of the HSOPSC using the IRT approach. In particular, using IRT will allow a determination of how well items can discriminate between individuals who have varying levels of perceptions of patient safety culture. This can be used to judge the quality of single items and thus, enhance the HSOPSC’s ability to provide valid and reliable measurement information independently of the sample under study.
Learning and teaching of calculus: performance analysis in a unified system
Published in International Journal of Mathematical Education in Science and Technology, 2022
Adail de Castro Cavalheiro, Guy Grebot
The objective tests mentioned above were given to all students in all the sections from the second semester of 2015 to the first semester of 2019, as part of a project which aims at constructing a database of items that would be used in the implementation of a computer-assisted test (CAT). These tests were made under the precepts of the Item Response Theory (IRT) which is a statistical theory based on the principle that the probability of solving an item correctly depends on the examinees' ability (latent traits) level in a given subject. This theory was created in order to overcome one of the problems of the classical test theory, which is the fact that the respondents evaluation depends on the items included in the test (de Andrade et al., 2000; Embretson & Reise, 2013; Hambleton et al., 1991).
Modern Psychometrics With R
Published in Technometrics, 2020
Chapter 4 describes the item response theory (IRT) model, also referred to as latent trait analysis or modern test theory. Prior to fitting in IRT, the scale dimensionality can be assessed by the categorical principal component analysis (Princals), EDA on tetrachoric/polychoric correlations, or item FA (IFA), which can be run using Gifi, psych, mirt, sirt, and ltm packages. Unidimensional dichotomous IRT, such as Rasch model and its extensions to logistic regressions with two and three parameters can be built in the eRm package. Polytomous multinomial logit model of rating scale and partial credit model, graded and nominal response models can be run in eRm package as well. Questions of the item and test information, and IRT sample size determination in the SimDesign and mirt packages are described. Different item behavior across person subgroups, or different item functioning (DIF) is considered by logistic regression for the proportional odds models in the lordif package, and by tree-based detection in the psychotree package. Multidimensional IRT models (MIRT) are described in relation with exploratory and confirmatory factor analyses, EFA and CFA, using ltm and mirt packages. Longitudinal IRT for measuring change, including linear logistic test model (LLTM) and many others, are implemented in the eRm package. Two-tier approach to longitudinal IRT and latent growth IRT can be modeled via lavaan and mirt packages. Bayesian IRT and dynamic 2-PL IRT models is estimated with help of the Markov chain Monte Carlo (MCMC) functions from MCMCpack and ltm packages.