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How Can a Family Resemblances Approach Help to Typify Qualitative Research? Exploring the Complexity of Simplicity
Author(s) -
Stephen Buetow
Publication year - 2014
Publication title -
sage open
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.357
H-Index - 32
ISSN - 2158-2440
DOI - 10.1177/2158244014556604
Subject(s) - qualitative research , simplicity , set (abstract data type) , epistemology , computer science , meaning (existential) , data science , data collection , management science , qualitative property , sociology , social science , machine learning , philosophy , economics , programming language
The term qualitative research still gives meaning to a diversearray of approaches. Attributes typical of these approaches are easily oversimplified.Recognition that qualitative research requires no essential set of predefiningattributes can minimize this problem. This article suggests how to typify qualitativeresearch outputs on the basis of overlapping similarities in the uses of this research.It discusses these similarities within and across five domains of qualitativeresearch—philosophy and theory, purpose, approach to reasoning, data collection, and useof numbers. Across these domains, qualitative research is typified as research that,without taking a unified stance on epistemological issues, can produce rich data byselecting and engaging purposefully with small samples, and analyzing these data usingiterative processes of induction, abduction, and deduction. Within this network offamily resemblances, no attributes of qualitative research are individually necessary orsufficient. Together, they attend simply to the complexity of coinciding attributes ofthis research

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