Model Specification Searches in Structural Equation Modeling Using Bee Swarm Optimization.
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) algor...
| Publicado en: | Educational & Psychological Measurement Vol. 84; no. 1; pp. 40 - 62 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Sage Publications Inc.
Feb2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=174837565&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 174837565 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00131644 EPM jtl: Educational & Psychological Measurement issn: 00131644 maglogo: Y pubinfo: dt: Feb2024 vid: 84 iid: 1 pid: 344 pub: Sage Publications Inc. artinfo: ui: 174837565 10.1177/00131644231160552 ppf: 40 ppct: 22 formats: tig: atl: Model Specification Searches in Structural Equation Modeling Using Bee Swarm Optimization. aug: au: Schroeders, Ulrich Scharf, Florian Olaru, Gabriel affil: University of Kassel, Germany Tilburg University, Netherlands su: Problem solving Psychological tests Information resources Intelligence tests Structural equation modeling Wasps Internet searching Health outcome assessment Questionnaires Factor analysis Descriptive statistics Bees Search engines Cognitive testing Statistical models Medical informatics Algorithms sug: subj: Problem solving Psychological tests Information resources Intelligence tests Structural equation modeling Wasps Internet searching Health outcome assessment Questionnaires Factor analysis Descriptive statistics Bees Search engines Cognitive testing Statistical models Medical informatics Algorithms keyword: bee swarm optimization dimensionality metaheuristics model specification search structural equation modeling bee swarm optimization dimensionality metaheuristics model specification search structural equation modeling ab: 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 psychological measurement instrument. The algorithm assigns items to an unknown number of nested factors in a confirmatory bifactor model, while simultaneously selecting items for the final scale. To achieve this, the algorithm follows the biological template of bees' foraging behavior: Scout bees explore new food sources, whereas onlooker bees search in the vicinity of previously explored, promising food sources. Analogously, scout bees in BSO introduce major changes to a model specification (e.g., adding or removing a specific factor), whereas onlooker bees only make minor changes (e.g., adding an item to a factor or swapping items between specific factors). Through this division of labor in an artificial bee colony, the algorithm aims to strike a balance between two opposing strategies diversification (or exploration) versus intensification (or exploitation). We demonstrate the usefulness of the algorithm to find the underlying structure in two empirical data sets (Holzinger–Swineford and short dark triad questionnaire, SDQ3). Furthermore, we illustrate the influence of relevant hyperparameters such as the number of bees in the hive, the percentage of scouts to onlookers, and the number of top solutions to be followed. Finally, useful applications of the new algorithm are discussed, as well as limitations and possible future research opportunities. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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