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Analysis approaches to community evaluation.

P J Gruenewald1

  • 1Prevention Research Center, Berkeley, California, USA.

Evaluation Review
|March 8, 1997
PubMed
Summary
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Evaluating community interventions requires careful consideration of analytic contaminants. This study details choices made to address potential biases in the Community Trial Project outcome data analysis.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health Research

Background:

  • Community intervention evaluations face analytic contaminants that can bias outcome assessments.
  • Common contaminants include model misspecifications, temporal/spatial autocorrelation, and failures of unit independence.
  • Addressing these issues is crucial for the validity of intervention evaluations.

Purpose of the Study:

  • To describe the analytical choices made for the Community Trial Project outcome data.
  • To highlight the challenges in evaluating community interventions due to potential biases.
  • To inform future community-based research design and analysis.

Main Methods:

  • The study focuses on the selection of statistical approaches for analyzing community intervention data.

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  • It addresses the need to control for community-specific time trends and error structures.
  • Methods account for spatial autocorrelation and intraclass correlations to ensure unit independence.

    • Main Results:

    • The article outlines specific analytical decisions made during the Community Trial Project evaluation.
    • It emphasizes the trade-offs evaluators face when balancing different error sources.
    • The chosen methods aim to mitigate bias in the assessment of intervention outcomes.

    Conclusions:

    • Accurate evaluation of community interventions necessitates robust analytical strategies sensitive to various contaminants.
    • The Community Trial Project evaluation involved deliberate choices to address potential biases.
    • Careful consideration of design and analysis is paramount for ensuring the internal and external validity of community intervention studies.