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A Design-Based Approach to Improve External Validity in Welfare Policy Evaluations.

Elizabeth Tipton1, Laura R Peck2

  • 11 Department of Human Development, Teachers College, Columbia University, New York, NY, USA.

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PubMed
Summary
This summary is machine-generated.

Balanced sampling enhances external validity in policy experiments by creating representative samples. This strategic site selection method addresses recruitment challenges and high nonresponse rates effectively.

Keywords:
content areajob trainingmethodological development

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Area of Science:

  • Social Sciences
  • Policy Evaluation
  • Experimental Design

Background:

  • Large-scale randomized experiments are crucial for assessing policy intervention impacts on average outcomes.
  • Improving the external validity of such experiments is an active area of research.
  • Balanced sampling offers a novel approach to site selection, bypassing random sampling and accommodating practical recruitment issues like high nonresponse.

Purpose of the Study:

  • To explore the implementation of a balanced sampling strategic site selection method.
  • To evaluate its applicability within a welfare policy evaluation context.

Main Methods:

  • Balanced sampling aims to create a sample compositionally similar to the target inference population.
  • This involves developing a population frame, stratifying it, and ranking units within strata.
  • Ranking identifies potential replacement sites to mitigate nonresponse challenges.

Main Results:

  • Developing a suitable population frame presented challenges, with three viable options identified for welfare policy.
  • A recruitment plan was formulated incorporating study-specific contextual variables to manage nonresponse.

Conclusions:

  • The balanced sampling method offers a strategic approach to site selection in policy evaluations.
  • It provides a framework for addressing practical recruitment challenges, particularly nonresponse, thereby enhancing external validity.