| Title | Multi-Resolution Design: Using Qualitative and Quantitative Analyses to Recursively Zoom in and out of the Same Dataset |
|---|---|
| Authors | Gillespie A. , Glaveanu V. , de Saint-Laurent C. , Zittoun T. , BERNAL MARCOS, MARCOS JOSÉ |
| External publication | No |
| Means | J. Mix Methods Res. |
| Scope | Article |
| Nature | Científica |
| JCR Quartile | 1 |
| SJR Quartile | 1 |
| Web | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204523119&doi=10.1177%2f15586898241284696&partnerID=40&md5=18ae54543ba3bc26dcf03639dc8f80b5 |
| Publication date | 01/01/2024 |
| ISI | 001316187200001 |
| Scopus Id | 2-s2.0-85204523119 |
| DOI | 10.1177/15586898241284696 |
| Abstract | A recent challenge is how to mix qualitative interpretation with computational techniques to analyze big qualitative data. To this end, we propose “multi-resolution design” for mixed method analysis of the same data: qualitative analysis zooms-in to provide in-depth contextual insight and quantitative analysis zooms-out to provide measures, associations, and statistical models. The raw qualitative data is transformed between excerpts, counts, and measures; with each having unique gains and losses. Multi-resolution designs entail transforming the data back-and-forth between these data types, recursively quantitizing and qualitizing the data. Two empirical studies illustrate how multi-resolution design can support abductive inference and increase validity. This contributes to mixed methods literature a conceptualization of how mixed analysis of the same big qualitative dataset can create tightly integrated synergies. © The Author(s) 2024. |
| Keywords | data transformation; mixed analysis; multi-resolution design; qualitizing; quantitizing |
| Universidad Loyola members |