Life With and Without Coding: Two Methods for Early-Stage Data Analysis in Qualitative Research Aiming at Causal Explanations
Qualitative research aimed at "mechanismic" explanations poses specific challenges to qualitative data analysis because it must integrate existing theory with patterns identified in the data. We explore the utilization of two methods—coding and qualitative content analysis—for the first steps in the data analysis process, namely "cleaning" and organizing qualitative data. Both methods produce an information base that is structured by categories and can be used in the subsequent search for patterns in the data and integration of these patterns into a systematic, theoretically embedded explanation. Used as a stand-alone method outside the grounded theory approach, coding leads to an indexed text, i.e. both the original text and the index (the system of codes describing the content of text segments) are subjected to further analysis. Qualitative content analysis extracts the relevant information, i.e. separates it from the original text, and processes only this information. We suggest that qualitative content analysis has advantages compared to coding whenever the research question is embedded in prior theory and can be answered without processing knowledge about the form of statements and their position in the text, which usually is the case in the search for "mechanismic" explanations. Coding outperforms qualitative content analysis in research that needs this information in later stages of the analysis, e.g. the exploration of meaning or the study of the construction of narratives.
Copyright (c) 2013 Jochen Gläser, Grit Laudel
This work is licensed under a Creative Commons Attribution 4.0 International License.