Quasi-experimental study designs series –Paper 9: Collecting Data from Quasi-Experimental Studies

Ariel M. Aloe (Lead Author), Betsy Jane Becker, Maren Duvendack, Jeffrey C Valentine, Ian Shemilt, Hugh Waddington

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
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Abstract

Objective: To identify variables that must be coded when synthesizing primary studies that use quasi-experimental designs. 
Study Design and Setting: All quasi-experimental (QE) designs. 
Results: When designing a systematic review of QE studies potential sources of heterogeneity – both theory-based and methodological – must be identified. We outline key components of inclusion criteria for syntheses of quasi-experimental studies. We provide recommendations for coding content-relevant and methodological variables, and outlined the distinction between bivariate effect sizes and partial (i.e., adjusted) effect sizes. Designs used and controls employed are viewed as of greatest importance. Potential sources of bias and confounding are also addressed. 
Conclusion: Careful consideration must be given to inclusion criteria and the coding of theoretical and methodological variables during the design phase of a synthesis of quasi-experimental studies. The success of the meta-regression analysis relies on the data available to the meta-analyst. Omission of critical moderator variables (i.e., effect modifiers) will undermine the conclusions of a meta-analysis.
Original languageEnglish
Pages (from-to)77-83
JournalJournal of Clinical Epidemiology
Volume89
Early online date29 Mar 2017
DOIs
Publication statusPublished - Sep 2017

Keywords

  • Meta-analysis
  • quasi-experiment
  • bivariate effect size
  • partial effect size
  • moderator variables
  • effect modifiers

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