Wolf, Amanda, & Robin Peace (2018). Understanding Q methodology data abductively: An ideal social science institutions. SAGE Research Methods Dataset [online]. London: Sage. (doi: http://dx.doi.org/10.4135/9781526439253)
Abstract: Abductive inquiry, following the work of Charles Peirce and others, concerns the generation of new hypotheses directly from data. In Q methodology, data are in the form of Q sorts, which show how a person has engaged subjectively with a given topic. This dataset exemplar is provided by Dr Amanda Wolf from Victoria University of Wellington and Assoc Prof Robin Peace from Massey University and draws attention to the abductive nature of understanding factor-analysed Q methodology data. The dataset is based on the analysis of individual Q-sort data produced by PQMethod software. It shows a composite summary prepared by the researchers from computer output to assist their abductive inquiry. The research team sought to ascertain the preferences of New Zealand social scientists for an ‘institution’ that might best promote the interests of a community of social scientists. The dataset will be of most use to those interested in thoroughly grasping the qualitative possibilities opened up by the application of statistical techniques. This exemplar will help you understand the unique properties of Q data and how it can be understood abductively in Q methodology.
Amanda Wolf <firstname.lastname@example.org> is in the School of Government, Victoria University of Wellington, New Zealand, and is past editor of Operant Subjectivity; and Robin Peace <email@example.com> is in the School of People, Environment and Planning, Massey University, Palmerston North, New Zealand.
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