How Good Is Good Enough? A Multidimensional, Best-Possible Standard for Research Design

Dados Bibliográficos

ID6340435
AutoresJohn Gerring (0000-0001-9858-2050, Boston University, autor correspondente)
Ano2011
Volume64
Fascículo3
Páginas625-636
Data de publicação2011-09-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoPolitical Research Quarterly (JOURNAL)
Identificadores do periódicoISSN: 1065-9129 • E-ISSN: 1938-274X
EditoraSAGE Publications Inc (PUBLISHER)
DOI10.1177/1065912910361221
OpenAlexW2113801600
IdiomaEN
Citações recebidas10
Referências citadas43

Recent years have seen a shift in methodological emphasis from the observable properties of a sample to its unobservable properties, that is, judgments about the process by which the data were generated. Considerations of research design have moved front and center. This article attempts to bridge discussions of experimental and quasi-experimental data and of quantitative and qualitative approaches, so as to provide a unified framework for understanding research design in causal analysis. Specifically, the author argues that all research designs aim to satisfy certain fundamental criteria, applicable across methods and across fields. These criteria are understood as desirable, ceteris paribus, and as matters of degree. The implications of this framework for methodological standards in the social sciences are taken up in the final section of the article. There, the author argues for a best-possible standard of proof that judges overall methodological adequacy in light of other possible research designs that might be applied to a particular research question

Ceteris paribus · Data science · Econometrics · Economics · Epistemology · Indeterminacy (philosophy · Management science · Positive economics · Research design · Sample (material · Social science · Sociology · Unobservable · Advanced Causal Inference Techniques · Computer Science · Evaluation and Performance Assessment · Qualitative Comparative Analysis Research