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Model Specification Search in Correlated Factor Models Using Bee Swarm Optimization
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AI
DOI：10.1080/10705511.2025.2612170.png)
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Abstract
En 中文
We introduce a Bee Swarm Optimization (BSO), a metaheuristic algorithm for correlated factor models that optimizes test construction by simultaneously determining the dimensionality and the optimal item set of a measure. In an extensive simulation study, we systematically varied the number of factors and indicators, the structural complexity, the magnitude of factor correlations, cross-loadings, and the extent of noise items. We benchmarked the BSO performance against four EFA–CFA pipelines. Performance was assessed via multiple criteria, including global and local model fit, maximal factor correlation, and item retention. BSO consistently recovered optimal models, achieving higher factor-retention accuracy while removing problematic cross-loading and noise items. In 97.2% of datasets and conditions, BSO outperformed the EFA–CFA pipelines. Based on a second simulation, we provide recommendations for hyperparameters. Overall, BSO offers a robust, data-driven alternative to prevalent approaches for developing measures with a correlated factor structure that are interpretable and parsimonious.
Keywords:
BSO
item selection
metaheuristics
model specification search
test development
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