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Methodological convergence of program evaluation designs.

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This study bridges the gap between experimental and ethnographic research, offering practical ways to improve methodological quality and enhance validity in program evaluation. It provides recommendations for better design elements across diverse methodologies.

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Area of Science:

  • Program evaluation methodology
  • Research design in social sciences

Background:

  • A persistent dichotomy exists between experimental/quasi-experimental and non-experimental/ethnographic study designs.
  • Systematic methodological quality work predominantly focuses on experimental designs, neglecting the extensive use of ethnographic studies.
  • The distinction between study types in empirical program evaluation is increasingly blurred, posing challenges for practitioners.

Purpose of the Study:

  • To analyze the convergence of design elements contributing to methodological quality.
  • To bridge the gap between experimental and ethnographic research methodologies.
  • To offer practical insights for improving validity and generalization in program evaluation.

Main Methods:

  • Literature review based on the classical validity framework of experimental/quasi-experimental studies.
  • Analysis of design elements in primary studies within systematic reviews and ethnographic research.
  • Examination of methodological quality across different research designs.

Main Results:

  • Identification of key design elements crucial for enhancing validity and generalization.
  • Specification of practical, complementary methodological approaches for diverse study types.
  • Demonstration of convergence in design elements between experimental and ethnographic research.

Conclusions:

  • Recommendations provided to improve design elements for enhanced validity and generalization in program evaluation.
  • Emphasizes a practical and complementary view of methodological quality across different research designs.
  • Aims to support evaluators and planners in navigating the evolving landscape of empirical program evaluation.