Speaker
Description
In this State of the Art Address, I revisit and extend the conceptual boundaries of two core mixed methods transformation techniques: qualitizing and quantitizing. In so doing, I spotlight the expanded methodological and philosophical dimensions that elevate their application in contemporary mixed methods research. The first third of the presentation is dedicated to qualitizing, defined as the transformation of quantitative data into qualitative form that can be analyzed qualitatively. I will outline how qualitizing has evolved to include five major elements: (1) it can yield numerous representations (e.g., narratives, profiles), (2) it can stem from either quantitative or qualitative data, (3) it may involve either qualitative or quantitative analyses, (4) it can be applied as a single or multiple analyses, and (5) it can produce a fully integrated analysis. Special emphasis will be placed on narrative profile formation—such as modal, average, holistic, comparative, and normative profiles—which allows for rich, contextualized interpretations of numerical data.
In the second third of the address, I will introduce the DIME-Driven Model of Quantitizing, which encompasses four core classes of Level 1 quantitizing:
• Descriptive-Based Quantitizing transforms qualitative data into quantitative metrics to summarize patterns using measures such as mean, standard deviation, percentiles, and skewness.
• Inferential-Based Quantitizing involves converting qualitative data into formats suitable for statistical inference, including tests such as analysis of variance (ANOVA), regression, and structural equation modeling.
• Measurement-Based Quantitizing refers to the transformation of qualitative insights into quantifiable constructs for instrument development and validation, often using techniques such as Rasch modeling and Item Response Theory (IRT).
• Exploratory-Based Quantitizing converts qualitative data into numerical formats to explore underlying patterns, relationships, or structures through methods such as factor analysis, cluster analysis, and correspondence analysis.
In this presentation, I will also introduce for the first time a novel concept, which I call Transformatizing. Transformatizing refers to the integrated process of applying both qualitizing and quantitizing techniques within a single analytical framework fully to harness and to interweave the strengths of qualitative and quantitative data transformations. It represents a dynamic, bidirectional approach wherein data are fluidly transformed across paradigms to achieve comprehensive, meta-integrative insights. Major components of transformatizing are QuanQualitizing and QualQuantitizing—both of which will be defined.
To concretize these ideas, I will present a real example from the published literature that illustrates both QuanQualitizing and QualQuantitizing in action.
Throughout the session, I will illustrate these expanded definitions with practical examples from diverse research contexts. Attendees will leave with a clearer understanding of how thoughtfully transforming data across traditions/paradigms not only enriches methodological rigor, but also facilitates deeper, more meaningful meta-inferences. I invite colleagues to consider how these advanced transformation techniques can further democratize evidence, foster integration, and propel mixed methods research into new frontiers.
