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Ulrich Schroeders; Florian Scharf; Gabriel Olaru – Educational and Psychological Measurement, 2024
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…
Descriptors: Structural Equation Models, Heuristics, Algorithms, Measurement Techniques
Harold Doran; Testsuhiro Yamada; Ted Diaz; Emre Gonulates; Vanessa Culver – Journal of Educational Measurement, 2025
Computer adaptive testing (CAT) is an increasingly common mode of test administration offering improved test security, better measurement precision, and the potential for shorter testing experiences. This article presents a new item selection algorithm based on a generalized objective function to support multiple types of testing conditions and…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Algorithms
Youmi Suk; Kyung T. Han – Journal of Educational and Behavioral Statistics, 2024
As algorithmic decision making is increasingly deployed in every walk of life, many researchers have raised concerns about fairness-related bias from such algorithms. But there is little research on harnessing psychometric methods to uncover potential discriminatory bias inside decision-making algorithms. The main goal of this article is to…
Descriptors: Psychometrics, Ethics, Decision Making, Algorithms
Michael Bass; Scott Morris; Sheng Zhang – Measurement: Interdisciplinary Research and Perspectives, 2025
Administration of patient-reported outcome measures (PROs), using multidimensional computer adaptive tests (MCATs) has the potential to reduce patient burden, but the efficiency of MCAT depends on the degree to which an individual's responses fit the psychometric properties of the assessment. Assessing patients' symptom burden through the…
Descriptors: Adaptive Testing, Computer Assisted Testing, Patients, Outcome Measures
Weicong Lyu; Chun Wang; Gongjun Xu – Grantee Submission, 2024
Data harmonization is an emerging approach to strategically combining data from multiple independent studies, enabling addressing new research questions that are not answerable by a single contributing study. A fundamental psychometric challenge for data harmonization is to create commensurate measures for the constructs of interest across…
Descriptors: Data Analysis, Test Items, Psychometrics, Item Response Theory
Chenchen Ma; Jing Ouyang; Chun Wang; Gongjun Xu – Grantee Submission, 2024
Survey instruments and assessments are frequently used in many domains of social science. When the constructs that these assessments try to measure become multifaceted, multidimensional item response theory (MIRT) provides a unified framework and convenient statistical tool for item analysis, calibration, and scoring. However, the computational…
Descriptors: Algorithms, Item Response Theory, Scoring, Accuracy
Chengyu Cui; Chun Wang; Gongjun Xu – Grantee Submission, 2024
Multidimensional item response theory (MIRT) models have generated increasing interest in the psychometrics literature. Efficient approaches for estimating MIRT models with dichotomous responses have been developed, but constructing an equally efficient and robust algorithm for polytomous models has received limited attention. To address this gap,…
Descriptors: Item Response Theory, Accuracy, Simulation, Psychometrics