Peer Reviewed Article

Declaring and Diagnosing Research Designs

Authors
  • Graeme Blair
  • Jasper Cooper
  • Alex Coppock
  • Macartan Humphreys
Published
August 30, 2019
Publication
American Political Science Review
Discipline
Areas of Study
Document Control Number(s)
  • ISPS 19-22
Citation

Blair, G., Cooper, J., Coppock, A., & Humphreys, M. (2019). Declaring and Diagnosing Research Designs. American Political Science Review, 113(3): 838-859. DOI:10.1017/S0003055419000194

Abstract

Researchers need to select high-quality research designs and communicate those designs clearly to readers. Both tasks are difficult. We provide a framework for formally “declaring” the analytically relevant features of a research design in a demonstrably complete manner, with applications to qualitative, quantitative, and mixed methods research. The approach to design declaration we describe requires defining a model of the world (M), an inquiry (I), a data strategy (D), and an answer strategy (A). Declaration of these features in code provides sufficient information for researchers and readers to use Monte Carlo techniques to diagnose properties such as power, bias, accuracy of qualitative causal inferences, and other “diagnosands.” Ex ante declarations can be used to improve designs and facilitate preregistration, analysis, and reconciliation of intended and actual analyses. Ex post declarations are useful for describing, sharing, reanalyzing, and critiquing existing designs. We provide open-source software, DeclareDesign, to implement the proposed approach.

Description

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Related Data:

Replication files are available at the American Political Science Review Dataverse: https://doi.org/10.7910/DVN/XYT1VB.