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Matched Comparison Group Design Standards in Systematic Reviews of Early Childhood Interventions

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  • Jaime Thomas
  • Sarah A. Avellar
  • John Deke
  • Philip Gleason

Abstract

Background: Systematic reviews assess the quality of research on program effectiveness to help decision makers faced with many intervention options. Study quality standards specify criteria that studies must meet, including accounting for baseline differences between intervention and comparison groups. We explore two issues related to systematic review standards: covariate choice and choice of estimation method. Objective: To help systematic reviews develop/refine quality standards and support researchers in using nonexperimental designs to estimate program effects, we address two questions: (1) How well do variables that systematic reviews typically require studies to account for explain variation in key child and family outcomes? (2) What methods should studies use to account for preexisting differences between intervention and comparison groups? Methods: We examined correlations between baseline characteristics and key outcomes using Early Childhood Longitudinal Study—Birth Cohort data to address Question 1. For Question 2, we used simulations to compare two methods—matching and regression adjustment—to account for preexisting differences between intervention and comparison groups. Results: A broad range of potential baseline variables explained relatively little of the variation in child and family outcomes. This suggests the potential for bias even after accounting for these variables, highlighting the need for systematic reviews to provide appropriate cautions about interpreting the results of moderately rated, nonexperimental studies. Our simulations showed that regression adjustment can yield unbiased estimates if all relevant covariates are used, even when the model is misspecified, and preexisting differences between the intervention and the comparison groups exist.

Suggested Citation

  • Jaime Thomas & Sarah A. Avellar & John Deke & Philip Gleason, 2017. "Matched Comparison Group Design Standards in Systematic Reviews of Early Childhood Interventions," Evaluation Review, , vol. 41(3), pages 240-279, June.
    • Handle: RePEc:sae:evarev:v:41:y:2017:i:3:p:240-279
    DOI: 10.1177/0193841X17708721
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    References listed on IDEAS

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    1. Fortson, Kenneth & Gleason, Philip & Kopa, Emma & Verbitsky-Savitz, Natalya, 2015. "Horseshoes, hand grenades, and treatment effects? Reassessing whether nonexperimental estimators are biased," Economics of Education Review, Elsevier, vol. 44(C), pages 100-113.
    2. John Deke & Hanley Chiang, 2017. "The WWC Attrition Standard," Evaluation Review, , vol. 41(2), pages 130-154, April.
    3. Kenneth Fortson & Philip Gleason & Emma Kopa & Natalya Verbitsky-Savitz, 2015. "Horseshoes, Hand Grenades, and Treatment Effects? Reassessing Whether Nonexperimental Estimators are Biased," Mathematica Policy Research Reports 88154a3523cc492dbca5bcb47, Mathematica Policy Research.
    4. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
    5. Zhao, Zhong, 2008. "Sensitivity of propensity score methods to the specifications," Economics Letters, Elsevier, vol. 98(3), pages 309-319, March.
    6. Joshua D. Angrist, 1998. "Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants," Econometrica, Econometric Society, vol. 66(2), pages 249-288, March.
    7. Kenneth A. Couch & Robert Bifulco, 2012. "Can Nonexperimental Estimates Replicate Estimates Based on Random Assignment in Evaluations of School Choice? A Within‐Study Comparison," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 31(3), pages 729-751, June.
    8. Thomas D. Cook & William R. Shadish & Vivian C. Wong, 2008. "Three conditions under which experiments and observational studies produce comparable causal estimates: New findings from within-study comparisons," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 27(4), pages 724-750.
    9. Neil Seftor, "undated". "Raising the Bar," Mathematica Policy Research Reports 135e82100e784dad803fe9c89, Mathematica Policy Research.
    10. Peter Z. Schochet, "undated". "Statistical Power for Random Assignment Evaluations of Education Programs," Mathematica Policy Research Reports 6749d31ad72d4acf988f7dce5, Mathematica Policy Research.
    11. repec:mpr:mprres:5863 is not listed on IDEAS
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