Integrating visual and verbal data
Anna Juliane Heinrich · transcript Verlag eBooks · 2024
Researchers in the interdisciplinary field of qualitative spatial research are concerned with understanding spaces in terms of their multifaceted nature, complexity, simultaneity, ascribed meanings, and transformation.In order to grasp the constitution of spaces, researchers make use of a nearly infinite spectrum of data: They analyze scientific, literary, and journalistic publications, interview transcripts and field notes, tweets and chat histories, plans and maps, sketches and photographs, videos and films, everyday objects and other artifacts, and much more.This plurality of data is not only characteristic for qualitative spatial research as a whole but also applies to individual research projects.As a rule, it is often necessary to use diverse data to achieve a certain research objective.If not only diverse data but also diverse types of data are used within a research project, this is especially challenging as it results in methodological and practical requirements.Surely the most prominent discourse relates to the combination of qualitative and quantitative data in mixed methods designs. 1 In contrast, the combination of distinct qualitative data has rarely been discussed and there are virtually no recommendations on the topic (see Cronin et al. 2008: 576; exceptions: Moran-Ellis 2006;Cronin 2008).There are even fewer references in the literature to the specifics of integrating visual and verbal data (see Emmel/Clark 2011: n.p.; exception: Forum: Qualitative Social Research, special issue 2/2008).At first glance, this is astonishing for two reasons: Firstly, visual and verbal data are regularly combined in the research practice of many disciplines, a practice that is becoming more and more common.Therefore, data integration is a highly topical issue for research practice.Secondly, it would seem obvious that the various characteristics of verbal data, such as interview transcripts, and visual data, such as photographs, could at least potentially contain relevant implications for the research process: for example, with regard to the selection of analysis methods or the acquisition of analysis software.