Moving from Tension to Texture: The Paradigmatic Roots of Mixed Methods Research

Moving from Tension to Texture: The Paradigmatic Roots of Mixed Methods Research

Preston B. Cosgrove (Cardinal Stritch University, USA) and Peter M. Jonas (Cardinal Stritch University, USA)
Copyright: © 2016 |Pages: 11
DOI: 10.4018/978-1-5225-0007-0.ch002
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Much like a jigsaw puzzle box top guides one in how to connect the pieces, an individual's research paradigm operates as a conscious or subconscious influence in conducting a research project. This chapter starts by making the argument for the critical role of research paradigms before moving into a thorough investigation of the paradigmatic origins of the qualitative-quantitative “debate.” While mixed-methods research is often seen as the mediator in the dispute, the authors then articulate four broad ways in which mixed methods research addresses the paradigm divide at the heart of qualitative and quantitative research. The result is paradigmatically complex, but offers researchers flexibility as they seek to address their research question.
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Tension: The Qualitative-Quantitative Debate And The Paradigm Divide

\ten(t)-shən\ noun ~ a state of latent hostility or opposition between individuals or groups

An examination of the tension inherent in the qualitative and quantitative debate first requires an understanding of the two terms in question. Both forms of research follow from an empirical process involving the standard collection, analysis, and interpretation of data (Leedy & Ormrod, 2010). But to describe their distinctions as a simple comparison between words (qualitative) and numbers (quantitative) is to over-generalize their fundamentally different ways of approaching the research process. Qualitative research involves emergent and inductive research designs, processes, and analyses that focus on the interpretation and meaning of phenomenon and/or participant experience in a natural setting. Conversely, quantitative research is a deductive and theory-driven approach focusing on strict measurement and control of variables within large samples and using analysis to identify statistical links among the data (Creswell, 2014; Ponterotto, 2005).

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