Qualitative Data Analysis
What to do with your data after data collection: transcription and analysis
Presumably, all qualitative research method teachers have experienced the following situation: A student comes to you and tells you that now all 12 interviews have been conducted, maybe they have been transcribed already and asks you: What should I do next? Steinard Kvale (1996) has devoted an article and later a book chapter to this with the title: “The 1,000 pages question.” When students ask this question, it is basically already too late, he writes. It is important to “…think about how the interviews are to be analyzed before they are conducted. The method of analysis decided on – or at least considered – will then direct the preparation of the interview guide, the interview process, and the transcription of the interviews.” (178)
It is important to point out that there are different ways of data transcription and you need to be aware of them to make a choice. You can opt to transcribe verbatim, word by word as it was spoken including all repetitions, half sentences, erm, mmh, pauses, etc. or to polish it turning the spoken language into grammatically correct sentences. When you first transcribe data, you will find out that we (you included) do not talk printed language. At first it feels awful. Is it really me, you may think, that asks the questions in such a way? Do I really talk like that?
A verbatim transcript has the advantage of best capturing the original interview situation. Often when we speak, we think about what we want to say next – while we talk. We need breaks to reflect on questions. Sometimes we have no answer yet and need to clarify our thoughts – even as we talk. The results are sentences that remain unfinished, sentences that flow into each other or overlap. This is important data and helps the analyst to interpret the content of what was said. When you however are only interested in facts and information, transferring spoken language into grammatically correct sentences may be an appropriate option as well. You need to take this decision in the context of your research question. Whether you need to transcribe all ‘mmhs’ and ‘aahs’ also depends on the intent of your qualitative research. There are very fine grained transcription systems like GAT (see below); you may also derive your own transcription notation from existing systems.
What is important is that you do not just transcribe your data, but have thought about what level of detail is necessary for the type of analysis you want to carry out. Below you see a number of examples how data can be transcribed:
Transcription system developed by Kallmeyer and Schütze (1976)
|Mhm||filling pauses, reception signal|
|(.)||falling pitch or intonation|
|(`)||raising pitch or intonation|
|(h)||inhibition / hemming and hamming|
|(leaves the room)||description of actions|
|&||Indicates that the enclosed speech was delivered more rapidly than usual for the speaker.|
|(..), (…)||Not understandable|
|(text?)||Not understandable, best guess|
Sign Up to our Newsletter
Jeffersonian transcript notation (Jefferson, 1984)
INT: >is it good<? (addressed to person setting up video camera) (0.2) PER: yep (set-up person leaves room) (0.3) INT: okay (1.1) INT: so em:: (0.7) INT: so you’ve (0.5) INT: got (0.2) INT: >the majority of the information anyway [I mean]< the ↑priority the most important thing really is that em
INT: obviously you can withdraw at any
INT: [time] (.) from this today
INT: apa::rt from when it goes (.) on to the
STU: [then it’s [too lat]e then yea] ha ha if your [in trouble you change your mind then ha ha]
INT: [yes] [ha-ha-ha exactly]
The same text transcribed verbatim looks like this:
INT: Ok, so erm, so you’ve got the majority of the information anyway, I mean primarily the most important thing really is that erm obviously you can withdraw at any time from the study, apart from when it goes on to the internet.
STU: It’s too late then yeah.
STU: You’re in trouble if you change your mind then [laughs].
INT: Exactly [laughs].
Transcription System GAT
01 S1: ja:; (.) die VIERzigergeneration so;=
02 =das=s: !WA:HN!sinnig viele die sich da ham [SCHEIden
03 S2: [ja;
04 S1: lasse[n.=
05 S2: [hm,
06 S1: =oder scheiden lassen ÜBERhaupt.
07 S2: hm,
09 S1: heutenoch-
11 s=is der UMbruch.
12 S2: n besonders GUtesbeispiel das warn mal unsere NACHbarn.
14 ähm (1.0)
15 DREISsigjahreverheiratet, (–)
16 das letzte kind (.) endlich aus m HAUS,
17 zumstuDIERN, (–)
The choice is yours, but you need to make one and this should also be explained in the methodology chapter of your research report. The same applies to the selection of the analysis procedures. It would go beyond the scope of this post to even briefly describe the various analysis choices you have. To get some ideas about the options available, take a look at Bernard and Ryan (2010). They describe a broad range of approaches like cultural domain analysis, KWIC analysis, semantic network analysis, discourse analysis, narrative analysis, grounded theory, content analysis, schema analysis, analytic induction, qualitative comparative analysis, and ethnographic decision models. The point I want to make here is that you need to think about transcription and analysis – thus, about what you want to do with your data – early on in the research process and not after all data are collected.
Bernard, Russel H. & Ryan, Gery W. (2010). Analysing Qualitative Data: Systematic Approaches.London: Sage.
Jefferson, G. (1984). Transcript Notation, in J. Heritage (eds), Structures of Social Interaction, New York: Cambridge University Press.
Kallmeyer, W. und Schütze, F. (1976). Konversationsanalyse. Studium Linguistik, 1, 1 – 28.
Kvale, Steinar (1996). The 1,000 page question. In: An Introduction to Qualitative Research Interviewing. Thousand Oak, CA: Sage, pp. 176 – 186.
See also: Dresing, Thorsten / Pehl, Thorsten: Praxisbuch Interview, Transkription&Analyse. Anleitungen und RegelsystemefürqualitativForschende; 5.Auflage (1. Auflage Juni 2011). Marburg, 2013.Quelle: www.audiotranskription.de/praxisbuch