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Analysis Approaches To Community Evaluation

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  • Paul J. Gruenewald

    (Prevention Research Center)

Abstract

Analysis approaches to the evaluation of community interventions must be sensitive to a wide variety of analytic contaminants that may bias the statistical assessment of changes in outcome measures. These contaminants include model misspecifications related to failures to control for community-specific time trends, temporal autocorrelated errors in equations, spatial autocor related errors among geographic units, and other failures of unit independence otherwise indexed by estimated intraclass correlations. Although an enormous amount of progress has been made toward the solution of many of these analytic problems over the past years, the contemporary evaluator of community interventions is left with a number of unenviable design and analysis choices; choices that inevitably force an assessment of the relative threats of different sources of error to the internal and external validity of the evaluation. This article describes the choices made for the evaluation of the Community Trial Project outcome data.

Suggested Citation

  • Paul J. Gruenewald, 1997. "Analysis Approaches To Community Evaluation," Evaluation Review, , vol. 21(2), pages 209-230, April.
    • Handle: RePEc:sae:evarev:v:21:y:1997:i:2:p:209-230
    DOI: 10.1177/0193841X9702100205
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    References listed on IDEAS

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    1. Simpson, J.M. & Klar, N. & Donner, A., 1995. "Accounting for cluster randomization: A review of primary prevention trials, 1990 through 1993," American Journal of Public Health, American Public Health Association, vol. 85(10), pages 1378-1383.
    2. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
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