A Comparison of Metaheuristic Optimization Algorithms for Scale Short-Form Development

Autor/inn/en	Raborn, Anthony W.; Leite, Walter L.; Marcoulides, Katerina M.
Titel	A Comparison of Metaheuristic Optimization Algorithms for Scale Short-Form Development
Quelle	In: Educational and Psychological Measurement, 80 (2020) 5, S.910-931 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Leite, Walter L.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164420906600
Schlagwörter	Test Construction; Automation; Heuristics; Mathematics; Factor Structure; Test Length; Test Reliability; Goodness of Fit; Test Items + Suchen Sie Ihr Suchwort? Testaufbau; Heuristik; Mathematik; Faktorenstruktur; Testreliabilität; Test content; Testaufgabe
Abstract	This study compares automated methods to develop short forms of psychometric scales. Obtaining a short form that has both adequate internal structure and strong validity with respect to relationships with other variables is difficult with traditional methods of short-form development. Metaheuristic algorithms can select items for short forms while optimizing on several validity criteria, such as adequate model fit, composite reliability, and relationship to external variables. Using a Monte Carlo simulation study, this study compared existing implementations of the ant colony optimization, Tabu search, and genetic algorithm to select short forms of scales, as well as a new implementation of the simulated annealing algorithm. Selection of short forms of scales with unidimensional, multidimensional, and bifactor structure were evaluated, with and without model misspecification and/or an external variable. The results showed that when the confirmatory factor analysis model of the full form of the scale was correctly specified or had only minor misspecification, the four algorithms produced short forms with good psychometric qualities that maintained the desired factor structure of the full scale. Major model misspecification resulted in worse performance for all algorithms, but including an external variable only had minor effects on results. The simulated annealing algorithm showed the best overall performance as well as robustness to model misspecification, while the genetic algorithm produced short forms with worse fit than the other algorithms under conditions with model misspecification. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01