Model Specification Searches in Structural Equation Modeling with a Hybrid Ant Colony Optimization Algorithm

Abstract

Model specification is a crucial aspect of structural equation modeling (SEM), since a misspecified model may lead to biased parameter estimation and result in inaccurate conclusions. We propose the Hybrid Ant Colony Optimization Algorithm (hACO), an improved metaheuristic algorithm to conduct model specification searches in SEM. This data mining algorithm combines aspects of the Ant Colony Optimization algorithm with the Tabu search algorithm to increase both accuracy and efficiency. A Monte Carlo simulation study showed that the hACO algorithm provided accurate and efficient SEM specification searches across all designed simulation conditions. The hACO algorithm can help applied researchers conduct specification searches while avoiding potential model misspecifications.

Keywords:

• Ant Colony Optimization algorithm
• data mining
• metaheuristic algorithm
• model misspecification
• specification search
• structural equation modeling
• Tabu Search

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Correction

Correction Statement

This article was originally published with errors, which have now been corrected in the online version. Please see Correction (http://dx.doi.org/10.1080/10705511.2022.2123641)

Notes

1 The complete code to run the hACO is available at: https://github.com/jingzeyuan/hACO_SEM.

2 The code used in Marcoulides and Falk (2018) was available in the ShortForm package when we finalized the simulations in 2021.