Selecting Cases for Comparative Sequential Analysis: Novel Uses for Old Methods
Year: 2018
Type: preprint
Abstract: This chapter proposes novel approaches to comparative case study research by revisiting the most influential and widely-used means to conduct qualitative research involving two or more cases: Mill’s methods of agreement and difference. I argue that as traditionally used, Millian methods of case selection embrace a flawed treatment of cases as static units to be synchronically compared rather than as social processes unfolding over time. As a result, Millian methods risk prematurely rejecting and otherwise overlooking (1) ordered causal processes, (2) paced causal processes, and (3) equifinality, or the presence of multiple pathways that produce the same outcome. Taking a more constructive and prescriptive turn, the chapter provides a set of recommendations for ensuring the alignment of Millian methods of case selection with within-case sequential analysis. It begins by outlining how the deductive use of processualist theories can help reformulate Millian case selection designs to accommodate ordered and paced processes. More originally, the chapter concludes by proposing a new, alternative approach to comparative case study research – the method of inductive case selection. By making use of Millian methods to select cases for comparison after a causal process has been identified within a particular case, the method of inductive case selection enables researchers to assess (1) the generalizability of the causal sequences, (2) the logics of scope conditions on the causal argument, and (3) the presence of equifinal pathways to the same outcome. In so doing, the weaknesses of Millian approaches can be converted into strengths, aligning comparative case study research with the advances of processualist researchers.
Source: SSRN Electronic Journal
Author: Tommaso Pavone
Institution: University of Oslo
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2961697805