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Using cluster analysis in program evaluation.

Laura R Peck1

  • 1Arizona State University, USA.

Evaluation Review
|February 26, 2005
PubMed
Summary
This summary is machine-generated.

Analyzing program impacts requires looking beyond average treatment effects to understand subgroup heterogeneity. Cluster analysis offers a method to identify these subgroups, ensuring unbiased impact estimates for targeted interventions.

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Area of Science:

  • Social Sciences
  • Program Evaluation
  • Statistics

Background:

  • Traditional program impact measurement focuses on average treatment effects.
  • Average treatment effects can mask significant variations in impact across different subgroups.
  • Understanding subgroup impacts is crucial for effective policy and intervention design.

Purpose of the Study:

  • To address the challenge of treatment group heterogeneity in program evaluations.
  • To propose cluster analysis as a method for identifying subgroups within evaluation data.
  • To provide unbiased estimates of program impacts for identified subgroups.

Main Methods:

  • Explains the challenge of treatment group heterogeneity.
  • Proposes using cluster analysis to identify subgroups.

Related Experiment Videos

  • Maintains the integrity of experimental evaluation designs for unbiased subgroup impact estimation.

    • Main Results:

    • Cluster analysis can identify otherwise difficult-to-identify subgroups within evaluation data.
    • The proposed method yields unbiased estimates of program impacts by subgroup.
    • Applied to New York State's Child Assistance Program evaluation, revealing substantive findings on work and earnings.

    Conclusions:

    • Cluster analysis is a valuable tool for subgroup analysis in program evaluation.
    • This method enhances the understanding of differential program impacts.
    • It offers advantages for refining interventions and policies based on subgroup-specific outcomes.