The pains and pleasures of data analysis Part 1

Analysis finally makes clear to researchers what would have been most important thing to study, if only they had known beforehand

(Patton, 1990, p. 371).

data-scienceAs the hectic exam period has finally come to an end with students getting back into the study routine and tutors reaching the bottoms of the piles of essays and exam papers to grade (thankfully, in the School of Sociology we have now switched to an online marking system), I finally find myself face to face with my project again. It is right where I have left it – fieldwork complete, interviews transcribed, copious field notes and all other relevant data organised by case studies, some preliminary analysis attempted but never properly taken off the ground. It is time to admit that it is not only due to a jam-packed work schedule that I have been putting the data analysis off until this moment. The truth is that I genuinely dread confronting the overwhelming amount of data I’ve got. Some gentle pressure from my supervision team and a clear recognition of the fact that it is this data that holds the key to understanding what I am so eager to understand led me to a firm decision to finally embark on this daunting task. But before immersing myself into the data, I rolled up my sleeves for another job – I began to scan research methods textbooks and manuals in search for a clear, logical and feasible set of instructions to painlessly guide me through all the stages of the laborious analysis process. I quickly realized that if different methodologists agree on anything, it is that there are no fixed rules to guide qualitative data analysis for there are almost as many analysis strategies as there are analysts. Yet, getting my head around some key terminology and understanding essential procedures and steps of the analysis process certainly helped clear the way for me to starting making sense of my data.

One resource I found particularly useful – Johny Saldaña’s Coding Manual For Qualitative Researchers. This clear, concise and very practical textbook offers a highly accessible introduction into the data coding and provides novice analysts with a range of perspectives on codes and coding strategies. Getting the terminology right did not prove to be a trivial task – in fact, understanding the distinction between codes, categories and themes seems to be an important step towards progressive interpretation of data. It is not uncommon to see these terms being used interchangeably (as, for example, in this otherwise quite useful video material on data analysis), but I side with Saldaña in that it is helpful to distinguish between different components of data analysis in order to move logically and effectively from one stage to another, from fundamental ideas to the more advanced level of the conceptual.

Saldaña defines a code as “a word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data” (2012, p. 3). In other words, a code should reflect the intrinsic meaning of the chosen segment of the text. Identified codes are then scanned for patterns of regularity and similarity and grouped into categories which should effectively summarise and bring meaning to the data. The next step is to consolidate categories into themes which for Saldaña means “to transcend the “reality” of your data and progress toward the thematic, conceptual, and theoretical” (2012, p.11). Thus, Identity may be a theme and Ethnicity, Language, and Religion – its related categories (this is just one example from the manual; Saldaña provides many more illustrations of codes, categories and themes and explains how those can be applied to the data).

Understanding different elements of the analysis process has helped me to organize my thoughts, devise a practical strategy and set the mood for effectively approaching my data. Staying alert and sensitive to the words and phrases that will run through my mind as I will be reading the interview transcripts will help me find the right codes to capture the essence of my textual data. Being attentive to patterns of similarity and regularity among the codes will enable the emergence of categories. The final challenge then will be to merge categories into themes by reaching out beyond the level of explicit and discerning much more subtle conceptual processes captured in the data. It is this last task that seems (and something tells me will prove) to be most challenging but, as Saldaña points out, “coding is not a precise science; it’s primarily an interpretive act” (2012, p. 4). An interpretive act, it seems to me, calls for creative thinking, empathic attitude, flexible mind and vivid imagination. These ingredients, coupled, of course, with “long periods of hard work, deep thinking, and weight-lifting volumes of material (Patton, 2000, p. 371), sound like the right recipe for success which, I am hopeful, will bring me from the “moment of terror that there’s nothing there” to the “times of exhilaration from the clarity of discovering ultimate truth” (Patton, 1990, p. 371).

References

Patton, M. Q. (1990). Qualitative evaluation and research methods . SAGE Publications, inc.

Saldaña, J. (2012). The coding manual for qualitative researchers (No. 14). Sage.

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