Eric Neumayer, Thomas PlümperCambridge University Press, 8/17/2017EAN 9781108415392, ISBN10: 1108415393Hardcover, 268 pages, 23.7 x 16.7 x 1.5 cmLanguage: EnglishOriginally published in EnglishThe uncertainty that researchers face in specifying their estimation model threatens the validity of their inferences. In regression analyses of observational data, the 'true model' remains unknown, and researchers face a choice between plausible alternative specifications. Robustness testing allows researchers to explore the stability of their main estimates to plausible variations in model specifications. This highly accessible book presents the logic of robustness testing, provides an operational definition of robustness that can be applied in all quantitative research, and introduces readers to diverse types of robustness tests. Focusing on each dimension of model uncertainty in separate chapters, the authors provide a systematic overview of existing tests and develop many new ones. Whether it be uncertainty about the population or sample, measurement, the set of explanatory variables and their functional form, causal or temporal heterogeneity, or effect dynamics or spatial dependence, this book provides guidance and offers tests that researchers from across the social sciences can employ in their own research.1. IntroductionPart I. Robustness – A Conceptual Framework2. Causal complexity and the limits to inferential validity3. The logic of robustness testing4. The concept of robustness5. A typology of robustness tests6. Alternatives to robustness testing?Part II. Robustness Tests and the Dimensions of Model Uncertainty7. Population and sample8. Concept validity and measurement9. Explanatory and omitted variables10. Functional forms beyond default11. Causal heterogeneity and context conditionality12. Structural change as temporal heterogeneity13. Effect dynamics14. Spatial correlation and dependence15. Conclusion.
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