By Chris Barton
Precise crime, desperation, fraud, and event: From the impoverished younger lady who enchanted nineteenth-century British society as a fake Asian princess, to the sixteen-year-old boy who "stole" a subway teach in 1993, to the lonely yet smart Frank Abagnale of Catch Me should you Can repute, those ten vignettes provide riveting perception into miraculous masquerades. photograph panels draw you into the exploits of those pretenders, and meticulously researched information continue you at the fringe of your seat. each one scene is gifted within the moment individual, a special viewpoint that actually areas you contained in the faker's brain. With motivations that come with survival, myth, and undeniable, outdated greed, the psychology of deception hasn't ever been so interesting or so shut at hand.
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Additional resources for Can I See Your I.D.?: True Stories of False Identities
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.