Comparison of Item Selection Criteria in Multidimensional Computer Adaptive Testing with the Graded Response Items — Scott Morris (IIT), Michael Bass (Northwestern), Matthew Lauritson (IIT), Sheng Zhang (IIT), & Richard Neapolitan (Northwestern) abstract slides
Applicant reactions to AIG: A CAT AIG feasibility study — Alan Mead (Talent Algorithms Inc.), Sheng Zhang (IIT), & Daniel Stopka (IIT) abstract slides
Abstract: A pilot study of respondent perceptions of flawed verbal analogy items automatically generated by software ("AIG items") was conducted to understand examinees' perceptions of flawed items and to estimate the psychometric properties of AIG items. A small sample of respondents (N=23) flagged items partially in line with expectations and partially in unexpected ways. Notably, respondents were far more likely to flag items with no keys or an awkward analogy as flawed than items with other flaws, including items with two keys. Difficulty of AIG items had an acceptable range and central tendency, despite being generated without control of the difficulty.
Computer Adaptive Testing via Adaptive Table of Specification with a multistage consideration: A simulation study — Ye Ma (University of Iowa) & Johnny Denbleyker (Houghton Mifflin Harcourt) abstract
Evaluating Alpha/Beta/Gamma Change with Ordinal Confirmatory Factor Analysis — Sean Wright, Scott Morris, & Daniel Gandara (Illinois Institute of Technology) abstract slides
Evaluation of R Packages with IRT 2PL Dichotomous Model — Shuya Zhang, Maxwell Hong, & Ying Cheng (Notre Dame) abstract
A Pseudo Power Analysis for CTT Item Analysis — Alan Mead (Talent Algorithms Inc.) abstract slides
Abstract: Presents a heuristic power analysis for detecting flawed items using CTT item analysis corrected item-total correlations (CITCs). Samples as low as N=25 are shown to have excellent power for detecting large effect sizes (e.g., due to miskeyed items) and more modest power to detect items with zero population CITCs. Type I error rates were uncontrolled and often excessive.
An Agenda for Psychometric Research Alan Mead (Talent Algorithms Inc.), Kirk Becker (Pearson), & Scott Morris, (IIT)
A Review of Games Based Assessment — Reya Green & Kristina Bauer (Illinois Institute of Technology) abstract slides
Abstract: Presents a review of the GBA literature organized by three dimensions (i.e., constructs assessed, research methodology, and elements of the game) that highlights the common practices and methods utilized (i.e., what is known) as well as summarizes key suggestions for future research (i.e., what is unknown). The literature review revealed that cognitive constructs were most commonly assessed, correlational designs were the most frequently employed, and games varied in the number and type of game elements utilized.
Automating Job Analysis using Natural Language Processing — Cavan Gray (Pearson) abstract
Automatic Item Generation: Methods, Applications, and Sample Statistics — Kirk Becker (Pearson) abstract
Semi-Supervised Learning for Criterion-Related Validity Studies — Alan Mead (Talent Algorithms Inc.) & Daniel Stopka (IIT) abstract
Abstract: The criterion-related validity studies conducted by I/O psychologists are an example of a class of data science studies called "supervised learning" studies. Recently, data scientists have investigated a new hybrid class of studies, called "semi-supervised learning" which blend supervised learning and unsupervised learning to use cases with missing criterion (or predictor) data. Unfortunately, our simulation of a typical criterion-related validity study suggested that semi-supervised learning does not improve either accuracy or efficiency.
Reviving Lord-McNemar's Estimated True Gain Score in the Modern World — Johnny Denbleyker (Houghton Mifflin Harcourt) & Ye Ma (University of Iowa) abstract slides
Rating Scale Anlysis Using Ideal Point Response Process — Georgi Petkov (Bowling Green State University) abstract slides
Questions about the seminar may be directed to Alan Mead (), Scott Morris (), or Kirk Becker (). We hope you will join us.
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