Book contents
- Frontmatter
- Contents
- List of Figures
- List of Tables
- List of Boxes
- List of Contributors
- Acknowledgements
- 1 A New Era of Experimental Political Science
- Part I Experimental Designs
- Part II Experimental Data
- Part III Experimental Treatments and Measures
- Part IV Experimental Analys is and Presentation
- Part V Experimental Reliability and Generalizability
- 18 Transparency in Experimental Research
- 19 Threats to the Scientific Credibility of Experiments: Publication Bias and P-Hacking
- 20 What Can Multi-Method Research Add to Experiments?
- 21 Generalizing Experimental Results
- 22 Conducting Experiments in Multiple Contexts
- Part VI Using Experiments to study Identity
- Part VII Using Experiments to Study Government Actions
- Author Index
- Subject Index
22 - Conducting Experiments in Multiple Contexts
from Part V - Experimental Reliability and Generalizability
Published online by Cambridge University Press: 08 March 2021
- Frontmatter
- Contents
- List of Figures
- List of Tables
- List of Boxes
- List of Contributors
- Acknowledgements
- 1 A New Era of Experimental Political Science
- Part I Experimental Designs
- Part II Experimental Data
- Part III Experimental Treatments and Measures
- Part IV Experimental Analys is and Presentation
- Part V Experimental Reliability and Generalizability
- 18 Transparency in Experimental Research
- 19 Threats to the Scientific Credibility of Experiments: Publication Bias and P-Hacking
- 20 What Can Multi-Method Research Add to Experiments?
- 21 Generalizing Experimental Results
- 22 Conducting Experiments in Multiple Contexts
- Part VI Using Experiments to study Identity
- Part VII Using Experiments to Study Government Actions
- Author Index
- Subject Index
Summary
In an effort to assess the generalizability of treatment effects across contexts, scholars (or teams of scholars) are increasingly conducting experiments around the same research questions in multiple country and subnational contexts. In this chapter, we categorize recent and ongoing efforts to conduct cross-context experiments into three types: “uncoordinated,” “coordinated, sequential,” and “coordinated, simultaneous.” We discuss some practical trade-offs across these types, arguing that coordinated cross-context designs offer the most promise for meta-analyses. We then draw attention to four areas in which the current approaches arguably all fall short in facilitating cumulative learning about treatment effects and treatment effect heterogeneity across contexts. We conclude by proposing some ways forward to continue improving our approach to learning about generalizability across contexts.
Keywords
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- Information
- Advances in Experimental Political Science , pp. 411 - 428Publisher: Cambridge University PressPrint publication year: 2021
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