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Behaviormetrika

, Volume 45, Issue 2, pp 261–294 | Cite as

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

  • Qiu Wang
  • Richard T. Houang
  • Kimberly Maier
Original Paper
  • 54 Downloads

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.

Keywords

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

Mathematics Subject Classification

62-P25 62B15 62-07 

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Copyright information

© The Behaviormetric Society 2018

Authors and Affiliations

  1. 1.Measurement and Research Methodology, Department of Higher Education, School of EducationSyracuse UniversitySyracuseUSA
  2. 2.Center for the Study of Curriculum Policy, College of EducationMichigan State UniversityEast LansingUSA
  3. 3.Department of Counseling, Educational Psychology, and Special Education, College of EducationMichigan State UniversityEast LansingUSA

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