By Jason Seawright
Reflecting the emerging approval for examine that mixes qualitative and quantitative social technology, Multi-Method Social technology offers the 1st systematic advisor to designing multi-method learn. It argues that tools may be productively mixed utilizing the framework of integrative multi-method learn, with one process used to hold out a last causal inference, and strategies from different traditions used to check the major assumptions taken with that causal inference. In making this argument, Jason Seawright considers quite a lot of statistical instruments together with regression, matching, and normal experiments. The publication additionally discusses qualitative instruments together with technique tracing, using causal approach observations, and comparative case examine learn. alongside the best way, the textual content develops over a dozen multi-method designs to check key assumptions approximately social technological know-how causation.
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Additional resources for Multi-Method Social Science: Combining Qualitative and Quantitative Tools
Usually, neither of these special exceptions holds, and therefore the causal parameters of interest cannot be estimated using a bivariate regression. Of course, this will not come as much of a surprise to the reader; few social scientists put much causal stock in bivariate regressions. 2 is that the dependent variable and the first independent variable are the same. The W variables are added to the model in the hope that they will somehow break the statistical connections between Di and i,1 + i,2 .
Thus, a central argument of this book, developed primarily in the later chapters, is that multi-method designs can be far more valuable if the quantitative component of the design is not based primarily on regression analysis or related techniques. The near-total focus on combining regression and case studies in the multi-method literature to date seems to represent something of an error of emphasis. 4 Conclusions important to ask whether and how case-study research can contribute to strengthening causal understanding in the wake of regression analysis.
The respondent hears a question, goes through one or another process of interpretation, recall, judgment, and editing, and then responds (Tourangeau et al. 2000). The response, which becomes a survey measure, is thus a product of a causal interaction between the survey text and a social dynamic involving the interviewer and the respondent. An indicator is the central product of the interaction between social reality and the measurement instrument, but it need not be the only one. A resourceful researcher can capture other pieces of evidence related to the causal process by which measurement takes place, using those data as clues regarding possible sources of measurement error (Mahoney 2010: 125–28).