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Understanding divergence of quantitative and qualitative data (or results) in mixed methods studies.

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Title: Understanding divergence of quantitative and qualitative data (or results) in mixed methods studies.
Authors: Pluye P (AUTHOR), Grad RM (AUTHOR), Levine A (AUTHOR), Nicolau B (AUTHOR)
Source: International Journal of Multiple Research Approaches. 2009, Vol. 3 Issue 1, p58-72. 15p.
Subject Terms: *Research methodology, *Qualitative research, Quantitative research, Family medicine, Empirical research
Abstract: In mixed methods studies, novice researchers need to know that qualitative and quantitative data or results sometimes diverge. However, few studies focus on this aspect of mixed methods research. The present paper aims to review the literature on divergence of qualitative and quantitative evidence, and describe examples. The prior literature reveals four strategies for taking divergence into account: reconciliation, initiation, bracketing and exclusion. Nine examples derived from empirical studies were found, and they are described. Then, a detailed example is given of how divergence was identified and explored in a pilot study of the implementation of one electronic knowledge resource on handheld computer in an academic family medicine clinic. Finally, this worked example is described in the context of a teaching exercise for novice researchers. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Multiple Research Approaches is the property of Dialectical Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
URL: 43539497
Database: Education Research Complete
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  Data: Understanding divergence of quantitative and qualitative data (or results) in mixed methods studies.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Multiple+Research+Approaches%22">International Journal of Multiple Research Approaches</searchLink>. 2009, Vol. 3 Issue 1, p58-72. 15p.
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  Data: *<searchLink fieldCode="DE" term="%22Research+methodology%22">Research methodology</searchLink><br />*<searchLink fieldCode="DE" term="%22Qualitative+research%22">Qualitative research</searchLink><br /><searchLink fieldCode="DE" term="%22Quantitative+research%22">Quantitative research</searchLink><br /><searchLink fieldCode="DE" term="%22Family+medicine%22">Family medicine</searchLink><br /><searchLink fieldCode="DE" term="%22Empirical+research%22">Empirical research</searchLink>
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  Data: In mixed methods studies, novice researchers need to know that qualitative and quantitative data or results sometimes diverge. However, few studies focus on this aspect of mixed methods research. The present paper aims to review the literature on divergence of qualitative and quantitative evidence, and describe examples. The prior literature reveals four strategies for taking divergence into account: reconciliation, initiation, bracketing and exclusion. Nine examples derived from empirical studies were found, and they are described. Then, a detailed example is given of how divergence was identified and explored in a pilot study of the implementation of one electronic knowledge resource on handheld computer in an academic family medicine clinic. Finally, this worked example is described in the context of a teaching exercise for novice researchers. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Multiple Research Approaches is the property of Dialectical Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.5172/mra.455.3.1.58
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      – SubjectFull: Quantitative research
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