A Novel Sequential Mixed-method Technique for Contrastive Analysis of Unscripted Qualitative Data

Abstract

Between-subject design surveys are a powerful means of gauging public opinion, but critics rightly charge that closed-ended questions only provide slices of insight into issues that are considerably more complex. Qualitative research enables richer accounts but inevitably includes coder bias and subjective interpretations. To mitigate these issues, we have developed a sequential mixed-methods approach in which content analysis is quantitized and then compared in a contrastive fashion to provide data that capitalize upon the features of qualitative research while reducing the impact of coder bias in analysis of the data. This article describes the method and demonstrates the advantages of the technique by providing an example of insights into public attitudes that have not been revealed using other methods.

Quantitizing and qualitizing (im-)possibilities in mixed methods research

“…Despite their raison d’être, not much has been done to look into the process of quantitizing qualitative data and qualitizing quantitative data in convergent parallel mixed methods designs. While some methodological attempts exist, mainly focusing on quantitizing (Cabrera and Reiner, 2016; Kerrigan, 2014; Leal et al, 2016; Sandelowski et al, 2009), none has so far subjected study participants to two paradigmatically different data collection tools and to switching tools over time in an equal-weight convergent parallel design. Empirically, this article deals with the conversion of qualitative data into quantitative data, and vice versa.…”

“…Defined as ‘the process of assigning numerical values (nominal or ordinal) to data conceived as not numerical’ (Sandelowski et al, 2009: 209–210), the process of transforming coded qualitative data into quantitative data (Driscoll et al, 2007: 20; Tashakkori and Teddlie, 1998: 126; Velzen, 2016: 3) or ‘transforming qualitative data into numerical format’ (Collingridge, 2013), quantitizing analyses narratives to explore the existence of any possible hypothesis or examine the salience of some discourses of interest and how they differentially occur in social actors’ narratives. Different approaches to quantitizing have been adopted, including content analysis of qualitative data and quantitizing of the qualitative codes (Cabrera and Reiner, 2016) into dichotomies. Besides dichotomization coded in terms of presence or absence, mention or non-mention of a concept or an issue under investigation in the qualitative data (coded as 1 vs 0), researchers may opt for capturing the various qualitative nuances on a scale to demonstrate that whatever is said qualitatively does not necessarily fall under a set of dichotomies but a continuum (ordinal, even scales).…”

“…Content analysis remains the springboard to sorting out and categorizing chunks of the qualitative narratives that need coding either by 1 or 0 or on a continuum ranging from 0 to any number, say 4. It is therefore a way of grouping respondents by a given category or other features of the responses (Cabrera and Reiner, 2016; Onwuegbuzie, 2003) or what Canoy (2015: 926) has compared to ‘freezing moving pictures in time’. The results of the data-quantitizing process is a set of data that can be combined or merged with the data originally conceived as quantitative to compare, contrast and locate within the research discourse.…”

“…Worth noting, however, is that quantitizing is not always possible. Quantitizing comes at a cost: dichotomization engenders loss of critical information and analytical power existing in raw qualitative data, undercuts the nuance and subtlety of particulars within given contexts of meaning and thus undermining the rationale for conducting qualitative research, sometimes trying to count the uncountable (Cabrera and Reiner, 2016: 6; Chang et al, 2009: 850; Sandelowski et al, 2009: 217). To some extent, quantitizing involves a process of getting rid of the uncountable or stripping qualitative data of their contextual and cultural meaning (Kerrigan, 2014: 351), losing the multidimensionality of qualitative data and insights through dichotomization (Driscoll et al, 2007), actually dropping anything that is not amenable to numbers.…”

Negative Perceptions of Jews in Turkish Politics: An Analysis of Parliamentary Debates, 1983-2016

“…Various studies underline the advantages of mixed-method content analysis (Cabrera & Reiner 2018;Chi 1997;Onwuegbuzue 2003). Of particular importance is the process of quantitizing, assigning numerical values to non-numerical data (Sandelowski et al 2009) which Jews were mentioned to analyse whether discussing threat-related topics trigger negative views.…”