By Thad Dunning
This special e-book is the 1st accomplished consultant to the invention, research, and overview of normal experiments - an more and more well known technique within the social sciences. Thad Dunning presents an advent to key concerns in causal inference, together with version specification, and emphasizes the significance of sturdy learn layout over complicated statistical research. Surveying many examples of normal normal experiments, regression-discontinuity designs, and instrumental-variables designs, Dunning highlights either the strengths and capability weaknesses of those equipment, assisting researchers in larger harnessing the promise of typical experiments whereas fending off the pitfalls. Dunning additionally demonstrates the contribution of qualitative tips on how to traditional experiments and proposes new how you can combine qualitative and quantitative ideas. Chapters whole with workouts and appendices masking really expert themes corresponding to cluster-randomized average experiments, make this a great educating device in addition to a helpful ebook for pro researchers.
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Extra info for Natural Experiments in the Social Sciences: A Design-Based Approach (Strategies for Social Inquiry)
Sample text
Perhaps even more crucial is the idea that the parameters (coefficients) of regression equations tell us how units would respond if a researcher intervened to change values of the independent variable—which is sometimes called the invariance of structural parameters to intervention. , Chapters 5, 6, and 9). In light of such difficulties, the focus on complex statistical models and advanced techniques for estimating those models appears to be giving way to greater concern with simplicity and transparency in data analysis, and in favor of more foundational issues of research design—the trend (2) identified above.
Yet, there is a second important rationale for the growth of design-based research in the social sciences, one that relates closely to the second difficulty mentioned above in relation to model-based inference (Dunning 2008a). 22 Moreover, this simplicity rests on a model of data-generating processes that is often credible for experiments and natural experiments. In later chapters, I describe a simple model that often is the right starting point for natural experiments—the so-called Neyman potential outcomes model, also known as the Neyman–Holland– Rubin model—and examine the conditions under which it applies to the analysis of natural-experimental data.
The process of treatment assignment itself appears to obviate confounding variables. Like Galiani and Schargrodsky’s study, Snow’s study of cholera transmission suggests some possible lessons in the virtues of successful natural experiments. If assignment to receive a source of water supply is really as good as random, then confounding is not an issue—just as in true experiments. Straightforward contrasts between the treatment and control groups may then suffice to demonstrate or reject a causal effect of land titling.