Using Ant Colony Optimization for Sensitivity Analysis in Structural Equation Modeling

ABSTRACT

Studies using structural equation modeling (SEM) to evaluate theories against observed data rely on multiple sources of evidence to support a proposed model, such as fit indices, variance explained, and comparison of alternative models. Additional evidence can be obtained by evaluating the model results’ sensitivity to an omitted confounder. The phantom variable approach to SEM sensitivity analysis requires manual specification of sensitivity parameters. This study improves on the phantom variable approach by employing the ant colony optimization algorithm to automatically search for sensitivity parameters, if any, that would lead to a change in the study’s conclusions. The proposed method is implemented in the package SEMsens for the R statistical software, and demonstrated with a sensitivity analysis of a model of the complex relation between working memory and writing.

KEYWORDS:

• Structural equation modeling
• ant-colony optimization algorithm
• sensitivity analysis
• validity
• omitted confounder
• external model misspecification

Notes

¹ https://osf.io/f6mgj/.

² The sens.tables function also provides two tables which report the sensitivity parameters that lead to the minimum and maximum path coefficients.