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The Evaluation of Community-Based Interventions: Group Randomization, Limits and Alternatives

  • Schmidt, Christoph M.



  • Baltussen, Rob

    (affiliation not available)

  • Sauerborn, Rainer


    (Heidelberg University)

The context of community-based interventions presents formidable problems for any evaluation analysis. Group-randomized studies do possess ideal properties in theory, but in practice, group- randomization might not be a feasible alternative at all or group-randomized studies might be contaminated. Thus, the decisive advantage of randomized controlled trials, that they and only they provide for a completely convincing identification strategy in the presence of observable and unobservable confounders, is lost. There are alternative strategies for the identification of treatment effects also in the case of unobservable confounders, however, although they specifically require unverifiable a priori information to be available. Moreover, when using non- experimental data, one can often extend sample size at low cost, and thus estimate parameters very precisely; therefore, for any particular situation the relative attractiveness of experimental and non-experimental approaches should be explored.

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Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 206.

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Length: 26 pages
Date of creation: Oct 2000
Date of revision:
Handle: RePEc:iza:izadps:dp206
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  1. Altman, David G., 1986. "A framework for evaluating community-based heart disease prevention programs," Social Science & Medicine, Elsevier, vol. 22(4), pages 479-487, January.
  2. Heckman, James J. & Robb, Richard Jr., 1985. "Alternative methods for evaluating the impact of interventions : An overview," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 239-267.
  3. Heckman, James J, 1996. "Randomization as an Instrumental Variable: Notes," The Review of Economics and Statistics, MIT Press, vol. 78(2), pages 336-41, May.
  4. J.D. Angrist & Guido W. Imbens & D.B. Rubin, 1993. "Identification of Causal Effects Using Instrumental Variables," NBER Technical Working Papers 0136, National Bureau of Economic Research, Inc.
  5. Joshua D. Angrist & Guido W. Imbens, 1995. "Identification and Estimation of Local Average Treatment Effects," NBER Technical Working Papers 0118, National Bureau of Economic Research, Inc.
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