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Related Concept Videos

Quantitative Analysis01:12

Quantitative Analysis

Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
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Quantitative Immunofluorescence Assay to Measure the Variation in Protein Levels at Centrosomes
09:39

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Published on: December 20, 2014

On Quantitizing.

Margarete Sandelowski1, Corrine I Voils, George Knafl

  • 1The University of North Carolina at Chapel Hill, School of Nursing.

Journal of Mixed Methods Research
|October 30, 2009
PubMed
Summary
This summary is machine-generated.

Quantitizing, the conversion of qualitative data to numerical form, is common in mixed methods research. This study examines the assumptions and challenges of this process, highlighting its value.

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Area of Science:

  • Mixed Methods Research
  • Qualitative Data Analysis
  • Quantitative Data Transformation

Background:

  • Quantitizing qualitative data is a frequent practice in mixed methods research.
  • The underlying assumptions, judgments, and compromises of quantitizing are often overlooked.
  • Critical examination of quantitizing is necessary to understand its impact on research inquiry.

Purpose of the Study:

  • To critically analyze the foundational assumptions and inherent complexities of quantitizing qualitative data.
  • To evaluate whether the conversion of qualitative data into quantitative form genuinely advances research inquiry.
  • To explore the conceptual standpoints that elucidate the value of quantitizing.

Main Methods:

  • Conceptual analysis of quantitizing processes in mixed methods research.
  • Examination of assumptions regarding the distinct nature of qualitative and quantitative data.
  • Analysis of the decision-making involved in counting and data conversion.

Main Results:

  • Quantitizing involves unexamined assumptions about data types, process directionality, and counting ambiguity.
  • Decisions about what and how to count are critical judgments in quantitizing.
  • Balancing numerical precision with narrative complexity represents a key compromise in data conversion.

Conclusions:

  • The process of quantitizing qualitative data is complex and requires careful consideration of assumptions and compromises.
  • Standpoints such as "conditional complementarity," "critical remediation," and "analytic alternation" offer frameworks for understanding the value of quantitizing.
  • Further research is needed to fully appreciate the implications of quantitizing for advancing mixed methods inquiry.