Marginal modeling of multilevel binary data with by Miglioretti D.L.

By Miglioretti D.L.

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By Miglioretti D.L.

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Analytic attention to issues of causal inference is important, but it is rarely satisfactory. Analytic approaches to studying social causes always suffer from either untestable assumptions, or limitations of the information available to operationalize these approaches, or both (Heckman 2000; Moffitt 2003, 2005). Many innovative research designs hold tremendous promise for addressing causal questions because or opportunities to exclude alternative explanations of observed associations in the research design itself (Campbell and Stanley 1963; Cook and Campbell 1979; Rosenbaum 1999, 2001).

3). Following Lieberson’s charge, the remainder of this book is devoted to thinking through various strategies for combining data collection approaches that contribute to the social science research process as a whole. A-Head The Study of Cause and Consequence in the Social Sciences Much of science is consumed with the study of cause and consequence, particularly the fit between theories of causation and empirical observations of the world. The social 42 sciences have experienced a great deal of internal controversy about the feasibility of studying cause and consequence.

This point of view usually leads to an argument for observation as a better means of obtaining data on social phenomena. Of course both participant-observation and direct-observation methods involve the presence of a researcher as well, which is also likely to influence the behavior of the people being studied. For some topics this is very easy to imagine. For example, we are convinced that using direct observation to record couples’ contraceptive use during sex might influence their behavior. In this type of situation it seems likely that direct observation, or for that matter participant observation, would generate at least as much distortion of the social realty as interviewing.

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