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Assessing the Accuracy of Generalized Inferences From Comparison Group Studies Using a Within-Study Comparison Approach

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  • Andrew P. Jaciw

Abstract

Background: Various studies have examined bias in impact estimates from comparison group studies (CGSs) of job training programs, and in education, where results are benchmarked against experimental results. Such within-study comparison (WSC) approaches investigate levels of bias in CGS-based impact estimates, as well as the success of various design and analytic strategies for reducing bias. Objectives: This article reviews past literature and summarizes conditions under which CGSs replicate experimental benchmark results. It extends the framework to, and develops the methodology for, situations where results from CGSs are generalized to untreated inference populations. Research design: Past research is summarized; methods are developed to examine bias in program impact estimates based on cross-site comparisons in a multisite trial that are evaluated against site-specific experimental benchmarks. Subjects: Students in Grades K–3 in 79 schools in Tennessee; students in Grades 4–8 in 82 schools in Alabama. Measures: Grades K–3 Stanford Achievement Test (SAT) in reading and math scores; Grades 4–8 SAT10 reading scores. Results: Past studies show that bias in CGS-based estimates can be limited through strong design, with local matching, and appropriate analysis involving pretest covariates and variables that represent selection processes. Extension of the methodology to investigate accuracy of generalized estimates from CGSs shows bias from confounders and effect moderators. Conclusion: CGS results, when extrapolated to untreated inference populations, may be biased due to variation in outcomes and impact. Accounting for effects of confounders or moderators may reduce bias.

Suggested Citation

  • Andrew P. Jaciw, 2016. "Assessing the Accuracy of Generalized Inferences From Comparison Group Studies Using a Within-Study Comparison Approach," Evaluation Review, , vol. 40(3), pages 199-240, June.
  • Handle: RePEc:sae:evarev:v:40:y:2016:i:3:p:199-240
    DOI: 10.1177/0193841X16664456
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    References listed on IDEAS

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    1. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    2. V. Joseph Hotz & Guido W. Imbens & Jacob A. Klerman, 2006. "Evaluating the Differential Effects of Alternative Welfare-to-Work Training Components: A Reanalysis of the California GAIN Program," Journal of Labor Economics, University of Chicago Press, vol. 24(3), pages 521-566, July.
    3. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    4. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    5. repec:mpr:mprres:3694 is not listed on IDEAS
    6. Colm O'Muircheartaigh & Larry V. Hedges, 2014. "Generalizing from unrepresentative experiments: a stratified propensity score approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(2), pages 195-210, February.
    7. Steven Glazerman & Dan M. Levy & David Myers, 2003. "Nonexperimental Versus Experimental Estimates of Earnings Impacts," The ANNALS of the American Academy of Political and Social Science, , vol. 589(1), pages 63-93, September.
    8. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    9. Shadish, William R. & Clark, M. H. & Steiner, Peter M., 2008. "Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random and Nonrandom Assignments," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1334-1344.
    10. Elizabeth Ty Wilde & Robinson Hollister, 2007. "How close is close enough? Evaluating propensity score matching using data from a class size reduction experiment," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 26(3), pages 455-477.
    11. Howard S. Bloom & Carolyn J. Hill & James A. Riccio, 2003. "Linking program implementation and effectiveness: Lessons from a pooled sample of welfare-to-work experiments," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 22(4), pages 551-575.
    12. 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.
    13. Roberto Agodini & Mark Dynarski, "undated". "Are Experiments the Only Option? A Look at Dropout Prevention Programs," Mathematica Policy Research Reports 51241adbf9fa4a26add6d54c5, Mathematica Policy Research.
    14. Roberto Agodini & Mark Dynarski, 2004. "Are Experiments the Only Option? A Look at Dropout Prevention Programs," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 180-194, February.
    15. Charles Michalopoulos & Howard S. Bloom & Carolyn J. Hill, 2004. "Can Propensity-Score Methods Match the Findings from a Random Assignment Evaluation of Mandatory Welfare-to-Work Programs?," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 156-179, February.
    16. Elizabeth A. Stuart & Stephen R. Cole & Catherine P. Bradshaw & Philip J. Leaf, 2011. "The use of propensity scores to assess the generalizability of results from randomized trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 369-386, April.
    17. Thomas Fraker & Rebecca Maynard, 1987. "The Adequacy of Comparison Group Designs for Evaluations of Employment-Related Programs," Journal of Human Resources, University of Wisconsin Press, vol. 22(2), pages 194-227.
    18. Joseph Hotz, V. & Imbens, Guido W. & Mortimer, Julie H., 2005. "Predicting the efficacy of future training programs using past experiences at other locations," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 241-270.
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