By Barry Cooper, Martyn Hammersley, Judith Glaesser, Roger Gomm
This booklet demanding situations the divide among qualitative and quantitative methods that's now institutionalized inside social technology. instead of suggesting the 'mixing' of tools, not easy the Qualitative-Quantitative Divide presents a radical interrogation of the arguments and practices attribute of either side of the divide, targeting how good they handle the typical difficulties that each one social learn faces, quite as regards causal research. The authors determine a few basic weaknesses in either quantitative and qualitative ways, and discover even if case-focused research - for example, within the kind of Qualitative Comparative research, Analytic Induction, Grounded Theorising, or Cluster research - can bridge the space among the 2 facets.
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Additional info for Challenging the Qualitative-Quantitative Divide: Explorations in Case-focused Causal Analysis
Indd 32 11/24/2011 1:34:18 PM What’s Wrong with Quantitative Research? 33 The problem of measurement Measurement is a central requirement in most forms of natural science, and it is also essential for much of the statistical analysis that is used by quantitative social scientists. The term ‘measurement’ can be interpreted in different ways. One common, rather broad, definition is that it is the rule-guided allocation of objects to mutually exclusive categories or to points on an ordinal, interval or ratio scale – in such a way as to capture difference or variation in a property possessed by that type of object.
Here, though, I want to focus on the other side of the issue: namely, some well-known obstacles to the claims of quantitative research to produce sound generalizations from samples to populations. First of all, it is necessary to draw a distinction between empirical generalizations about finite populations and the testing of theoretical propositions, which are intended to apply to all cases that would fall within the (necessarily open-ended) population marked out by the conditions specified by an explanatory theory.
However, generally speaking in social science what we find is not an all-or-nothing, or even a very large, average difference in outcome between those cases where the hypothetical causal factor is operating and those where it is not. Instead, the differences discovered are usually relatively small. , rather than signifying a causal process of the kind suspected. Significance testing is often used to assess the likelihood of this. However, this is not justified. Above all, what significance testing cannot tell us is whether any difference in outcome found in some set of cases would be found in all cases where the factor hypothesized as the cause is present.