Stop making sense: the trials and tribulations of qualitative data analysis

Abstract

An account is given of a research project with particular attention to the place of data analysis and interpretation. It shines some light into what often remains the black box of qualitative research practice, the mysterious process of generating a research narrative based on the data. The paper provides other apprentice researchers with an insight into what the data analytical experience can be like, warts and all. It raises issues to do with qualitative data analysis, the use of CAQDAS, grounded theory, the relationship between the narratives of the researcher and the researched, and about the training of qualitative researchers.

Qualitative data analysis: conceptual and practical considerations

“…Many other qualitative researchers advise interweaving data collection and analysis from the beginning. 3,4,[8][9][10][11] I contend that we need to treat data analysis as part of the research design, the literature review, the…”

“…This question often offers to the researchers to some "surprising, unusual, or conceptually interesting" results (p. 153). 17 Often, new and experienced researchers feel unskilled or clumsy at the beginning of the coding process 10,14,18 and may not feel confident about looking for meanings and naming the codes. However, confidence will gradually increase with continued efforts at data analysis.…”

Exploring the relationship between population mobility and HIV risk: Evidence from Tanzania

“…The second phase produced a series of detailed process maps and translated transcripts, which were analysed along with the in-depth interview transcripts and debrief notes using Nvivo v9. However, emerging themes had been discussed throughout the project, blurring the lines between data collection and analysis (Schiellerup, 2008). Ethical approval was granted by the ethics committees of SOAS, the…”

Human rights researchers’ data analysis and management practices

“…Prior studies about the data analysis practices in academic communities acknowledged several important issues in researchers' data analysis processes, such as the need of using mixed analytical approaches (Schiellerup, 2008) and transcribing, coding and interpreting the collected data (Merritt & Labbo, 2004;Whiffin, Bailey, Ellis-Hill, & Jarrett,, 2014), the preparatory work for archiving and sharing data (Cheshire, 2009), and the use of analytical aids like ATLAs/ti (Schiellerup, 2008) and NVivo (Welsh, 2002). The number of researchers involved/collaborated in the data analysis processes also affects the data analysis practices (Sweeney, Greenwood, Williams, Wykes, & Rose, 2013).…”