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), adatastrategy(D), andananswerstrategy(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.
Highlights
As empirical social scientists, we routinely face two research design problems
The methods proposed in this paper are implemented in an accompanying open-source software package, DeclareDesign (Blair et al 2018)
Contrary to claims that regression analysis and Qualitative Comparative Analysis (QCA) stem from fundamentally different ontologies (Thiem, Baumgartner, and Bol 2016), we show that saturated regression analysis may mitigate measurement error concerns in QCA
Summary
We routinely face two research design problems. First, we need to select high-quality designs, given resource constraints. Component of a design while assuming optimal conditions for others These relatively informal practices can result in the selection of suboptimal designs, or worse, designs that are too weak to deliver useful answers. Researchers concerned about the policy impact of a given treatment might require a design that is diagnosand-complete for an out-of-sample diagnosand, such as bias relative to the population average treatment effect. They may consider a diagnosand directly related to policy choices, such as the probability of making the right policy decision after research is conducted
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