Written by Robert Yin, this entry gives a clear overview of analytic generalisation from case studies, where it is appropriate, and how to effectively apply it.
"With both the case study and the laboratory experiment, the objective for generalizing the findings is the same: The findings or results from the single study are to follow a process of analytic generalization. Analytic generalization may be defined as a two-step process. The first involves a conceptual claim whereby investigators show how their case study findings bear upon a particular theory, theoretical construct, or theoretical (not just actual) sequence of events. The second involves applying the same theory to implicate other, similar situations where analogous events also might occur."
In terms of application of analytic generalisation, Yin suggests the following:
- A logical argument or theory should be made clear at the beginning of conducting case study research
- The argument should be grounded in a research literature rather than specific related to the case study
- Findings should demonstrate how the theory or argument was either challenged or supported the results
- If the findings support the theory, a logical and sound argument needs to be made by researchers to show how these findings can be generalised to similar situations
- Examining rival hypotheses, and actively collecting data to investigate rival hypotheses, will strengthen claims of analytical generalisation
- Analytic generalisations are stronger when more than one situation or case study shows results that support the theory
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Yin, R. (2010). 'Analytic Generalization.' In Albert J. Mills, G. Durepos, & E. Wiebe (Eds.), Encyclopedia of Case Study Research. (pp. 21-23). Thousand Oaks, CA: SAGE Publications, Inc.