The final, formatted version of the article will be published soon.
METHODS article
Front. Psychol.
Sec. Quantitative Psychology and Measurement
Volume 15 - 2024 |
doi: 10.3389/fpsyg.2024.1421525
This article is part of the Research Topic Best Practice Approaches for Mixed Methods Research in Psychological Science - Volume II View all 17 articles
On Quantitizing Revisited
Provisionally accepted- Faculty of Education, University of Johannesburg, Johannesburg, South Africa
This article builds on the groundbreaking highly cited work of Sandelowski et al. (2009) by critically reevaluating the process of quantitizing-transforming qualitative data into quantitative forms-a technique that has surprisingly not proliferated in academic research, presumably due to a shortage of methodological exploration in this area. This article responds to this shortfall by proposing a comprehensive meta-framework using the 5W1H approach, which outlines why, when, what, where, how, where, and who should engage in quantitizing, thereby integrating several frameworks and models across both mixed and multiple methods research. Central to this framework is the DIME-Driven Model of Quantitizing, which categorizes quantitizing into Descriptive, Inferential, Measurement, and Exploratory types, each enhancing the utility and precision of quantitizing. This innovative model supports the article's broader advocacy for quantitizing as a crucial methodological tool across diverse research traditions. This article explores the application and value of quantitizing across qualitative, quantitative, and mixed methods research traditions, demonstrating its broad relevance and transformative potential. It discusses the variable adoption of quantitizing based on differing philosophical perspectives related to ontology, epistemology, axiology, and methodology. Despite these differences, few research philosophies completely reject quantitizing. The article advocates for a balanced use of quantitizing to complement qualitative analyses and to enhance research clarity and applicability without compromising the richness of qualitative data. It serves as a comprehensive resource for understanding the complexities and utility of quantitizing, aiming to inspire researchers to consider this approach to enrich their analytical tools and enhance the depth and applicability of their research findings.
Keywords: quantitizing, DIME-driven model of quantitizing, mixed methods research, 1 + 1 = 1 integration approach, Qualitative data, data transformation, Inter-respondent matrix, quantitative analysis
Received: 30 Apr 2024; Accepted: 25 Nov 2024.
Copyright: © 2024 Onwuegbuzie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Anthony J Onwuegbuzie, Faculty of Education, University of Johannesburg, Johannesburg, 2006, South Africa
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.