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Declaring and Diagnosing Research Designs

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  • BLAIR, GRAEME
  • COOPER, JASPER
  • COPPOCK, ALEXANDER
  • HUMPHREYS, MACARTAN

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.

Suggested Citation

  • Blair, Graeme & Cooper, Jasper & Coppock, Alexander & Humphreys, Macartan, 2019. "Declaring and Diagnosing Research Designs," American Political Science Review, Cambridge University Press, vol. 113(3), pages 838-859, August.
  • Handle: RePEc:cup:apsrev:v:113:y:2019:i:3:p:838-859_15
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    Cited by:

    1. Karthik Muralidharan & Mauricio Romero & Kaspar Wüthrich, 2019. "Factorial Designs, Model Selection, and (Incorrect) Inference in Randomized Experiments," NBER Working Papers 26562, National Bureau of Economic Research, Inc.
    2. McKenzie, David & Mohpal, Aakash & Yang, Dean, 2022. "Aspirations and financial decisions: Experimental evidence from the Philippines," Journal of Development Economics, Elsevier, vol. 156(C).
    3. Brodeur, Abel & Esterling, Kevin & Ankel-Peters, Jörg & Bueno, Natália S & Desposato, Scott & Dreber, Anna & Genovese, Federica & Green, Donald P & Hepplewhite, Matthew & de la Guardia, Fernando Hoces, 2024. "Promoting Reproducibility and Replicability in Political Science," Department of Economics, Working Paper Series qt23n3n3dg, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    4. Dawid, Philip & Humphreys, Macartan & Musio, Monica, 2022. "Bounding Causes of Effects With Mediators," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue OnlineFir, pages 1-1.
    5. Chad Hazlett & Tanvi Shinkre, 2024. "Demystifying and avoiding the OLS "weighting problem": Unmodeled heterogeneity and straightforward solutions," Papers 2403.03299, arXiv.org, revised Oct 2024.

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