Description
Book SynopsisRobustness testing allows researchers to explore the stability of estimates to alternative plausible model specifications. This book explains why robustness tests help researchers to deal with model uncertainty in quantitative research. With little technical knowledge required, it will be relevant to all social scientists as well as graduate students.
Trade Review'Neumayer and Plümper have made an impressive contribution to research methodology. Rich in innovation and insight, Robustness Tests for Quantitative Research shows social scientists the way forward for improving the quality of inference with observational data. A must-read!' Harold D. Clarke, Ashbel Smith Professor, University of Texas, Dallas
Table of Contents1. Introduction; Part I. Robustness – A Conceptual Framework: 2. Causal complexity and the limits to inferential validity; 3. The logic of robustness testing; 4. The concept of robustness; 5. A typology of robustness tests; 6. Alternatives to robustness testing?; Part II. Robustness Tests and the Dimensions of Model Uncertainty: 7. Population and sample; 8. Concept validity and measurement; 9. Explanatory and omitted variables; 10. Functional forms beyond default; 11. Causal heterogeneity and context conditionality; 12. Structural change as temporal heterogeneity; 13. Effect dynamics; 14. Spatial correlation and dependence; 15. Conclusion.
