On qualitizing revisited

Area of Science:

• Mixed Methods Research
• Qualitative Data Analysis
• Research Methodology

Background:

• The initial definition of qualitizing, converting quantitative data to qualitative form, offered a valuable contribution to mixed methods research.
• However, this original definition presented a reductionist perspective, neglecting the interpretive and integrative aspects of the process.
• This work builds upon existing meta-frameworks to address these limitations and expand the conceptualization of qualitizing.

Purpose of the Study:

• To reconceptualize qualitizing as a recursive, meaning-centered, and meta-integrative process.
• To reframe qualitizing not as mechanical data conversion but as a meta-transformative process of interpretive synthesis.
• To introduce a novel framework for operationalizing this expanded understanding of qualitizing.

Main Methods:

• Reconceptualization of qualitizing grounded in Critical Dialectical Pluralism (CDP) 2.0.
• Introduction of a 43-profile Narrative Profiling Taxonomy for transforming diverse data types into interpretive narratives.
• Integration of arts-based extensions to highlight analytic, affective, and creative dimensions of research integration.

Main Results:

• Qualitizing is redefined as a multidimensional system of meaning-making, emphasizing interpretive synthesis over data conversion.
• The Narrative Profiling Taxonomy operationalizes the transformation of quantitative, qualitative, or mixed data into integrated interpretive narratives.
• This approach facilitates meta-inferences that transcend traditional analytic boundaries by humanizing numerical structures through narrative representation.

Conclusions:

• Qualitizing is advanced as a foundational methodological and philosophical basis for research integration.
• The reconceptualized approach transforms data into meaning-centered understanding through integrative and interpretive processes.
• This revised perspective enriches mixed methods research by embracing the full potential of data synthesis and interpretation.