By Jason Seawright
Reflecting the emerging approval for examine that mixes qualitative and quantitative social technology, Multi-Method Social technology offers the 1st systematic consultant to designing multi-method study. It argues that tools will be productively mixed utilizing the framework of integrative multi-method learn, with one process used to hold out a last causal inference, and techniques from different traditions used to check the main assumptions considering that causal inference. In making this argument, Jason Seawright considers a variety of statistical instruments together with regression, matching, and normal experiments. The e-book additionally discusses qualitative instruments together with technique tracing, using causal method observations, and comparative case research learn. alongside the way in which, the textual content develops over a dozen multi-method designs to check key assumptions approximately social technology causation.
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Extra resources for Multi-Method Social Science: Combining Qualitative and Quantitative Tools
1 Identifying a Case’s Causal Counterfactuals causally relevant change between the time of the first and the second decision is the treatment variable. If other variables are changing as well, then the second decision does not measure Yi,t , reflecting instead some other causal quantity. A classic and highly useful, if probably incomplete, inventory of problematic varieties of changes between the first and second measurement in such case-study designs is given by Campbell and Stanley (1963; see also Campbell and Ross 1968).
Because the equation is simultaneously dealing with each level of treatment, successful inference will require that a single set of included W variables block off statistical relationships between each treatment level indicator and the error term. Returning to the setup regarding the effects of warnings, tickets, or arrests at police stops, for example, it is necessary that the W variables include all immediate causes of getting arrested, of getting a ticket, of getting a formal warning, and of getting an informal warning; this is obviously even harder than including all of the immediate causes of any pair of these treatments.
On the other hand, if the children are vaccinated in one scenario but not the other, then the announcement affects the vaccination decision. This example presents the key features of the potential outcomes theory of causation (Neyman 1923/1990; Rubin 1974), a framework for thinking about causation that – I will argue below – can be used as the basis for a common language that captures core concerns of both qualitative and quantitative causal thinking. Let us briefly introduce a formal language for representing the components of this framework.