Using Ant Colony Optimization for Sensitivity Analysis in Structural Equation Modeling

Walter L. Leite, Zuchao Shen, Katerina Marcoulides, Charles L. Fisk, Jeffrey Harring

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
JournalStructural Equation Modeling
DOIs
StateAccepted/In press - 2021

Bibliographical note

Publisher Copyright:
© 2021 Taylor & Francis Group, LLC.

Keywords

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

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