Multilevel structural equation modeling-based quasi-experimental synthetic cohort design

Qiu Wang, Richard T. Houang, Kimberly Maier

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

This paper provides a theoretical foundation to examine the effectiveness of post-hoc adjustment approaches such as propensity score matching in reducing the selection bias of synthetic cohort design (SCD) for causal inference and program evaluation. Compared with the Solomon four-group design, the SCD often encounters selection bias due to the imbalance of covariates between the two cohorts. The efficiency of SCD is ensured by the historical equivalence of groups (HEoG) assumption, indicating the comparability between the two cohorts. The multilevel structural equation modeling framework is used to define the HEoG assumption. According to the mathematical proof, HEoG ensures that the use of SCD results in an unbiased estimator of the schooling effect. Practical considerations and suggestions for future research and use of SCD are discussed.

Original languageEnglish (US)
Pages (from-to)261-294
Number of pages34
JournalBehaviormetrika
Volume45
Issue number2
DOIs
StatePublished - Oct 1 2018

Keywords

  • Causal inference
  • Matching
  • Multilevel analysis
  • Multilevel structural equation modeling
  • Propensity score matching
  • Quasi-longitudinal design
  • Solomon four-group design
  • Synthetic cohort design

ASJC Scopus subject areas

  • Analysis
  • Applied Mathematics
  • Clinical Psychology
  • Experimental and Cognitive Psychology

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