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Qualitative Sociology, Vol. 24, No. 3, 2001
Integrating Technology to Improve the Efficiency of Qualitative Data Analysis—A Note on Methods Calvin Smith and Patricia M. Short
Qualitative data analysis (QDA) is often a time-consuming and laborious process usually involving the management of large quantities of textual data. Recently developed computer programs offer great advances in the efficiency of the processes of QDA. In this paper we report on an innovative use of a combination of extant computer software technologies to further enhance and simplify QDA. Used in appropriate circumstances, we believe that this innovation greatly enhances the speed with which theoretical and descriptive ideas can be abstracted from rich, complex, and chaotic qualitative data. KEY WORDS: qualitative data analysis; computers.
INTRODUCTION Technological advances have led to enhancements in the efficiency of qualitative data analysis (QDA). Computer programs such as The Ethnograph and NUD? IST have made it possible for qualitative data analysts to manage large volumes of textual data. Such programs offer an immense improvement in the efficiency and ease with which QDA can be done and they continue to be improved in scope and function. Yet, until very recently, preparing documents for analysis in these programs involved transcription to text files. Notwithstanding recent advances in software design, qualitative data analysis can still be very timeconsuming and expensive; interviews or field notes still are usually transcribed and then “coded” in order to reduce data to an organised and coherent collection of ideas. Correspondence should be directed to Calvin Smith, Teaching and Educational Development Institute, University of Queensland, Queensland, Australia, 4072, and Patricia M. Short, Department of Sociology, Anthropology and Archaeology, University of Queensland, Queensland, Australia, 4072; e-mail:
[email protected]. 401 ° C
2001 Human Sciences Press, Inc.
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We offer in this paper an advance in efficiency that does not involve verbatim transcription of recorded data and draws on a combination of extant technologies to facilitate faster, less expensive QDA. We acknowledge that recent developments in QDA software enable researchers to code directly from audio or sound files (e.g., C-I-SAID) or to create “proxy” documents linked to (nontranscribed) sources in different formats—media files, photographic images, etc. (e.g., the latest QSR product, NVivo. Still, while C-I-SAID, for example, provides a sophisticated method for directly linking coding charts and video/audio sources, the lexical coding system provided for appears somewhat limited as a tool for genuine inductive analysis. The method we describe will be more useful for researchers who are interested in progressively building, changing, and documenting a coding system. Also, our method will assist those using earlier or similar versions of the QSR software (NUD? IST IV and earlier) and who wish to maximize and balance efficiency, affordability, convenience, and rigor in qualitative inquiry, especially in contexts where rapid assessment and analysis is necessary. The essence of the innovation we describe here is the production and coding of a simplified text file that represents the sequence and length of segments of recorded data (on audio- or videotape or digital recording device) passing through a playback machine. We call this text file the “counter-run” file. It is the counter-run file that is introduced into a QDA program such as NUD? IST or The Ethnograph rather than a full transcription of recorded data. Theoretical ideas are catalogued against the counter-run text that serves as an index representing the location of the data giving rise to those ideas in the original data record. This approach to QDA recommends itself highly for the routine work of QDA. It facilitates efficient analysis of qualitative data while still enabling the analyst to refer to the detail of the raw data at all stages of analysis. It is especially suitable where the goals of analysis are analytic induction, grounded theory building, and so on (Glaser and Strauss 1967; Lofland 1971; Strauss 1987; Strauss and Corbin 1990), especially where coarse-grained coding is all that is required or is a useful preliminary (e.g., Short 1996). In contexts where rapid assessment of a field or issue is necessary (e.g., for social impact assessment, action research, or social planning contexts) or research is strictly time-limited (e.g., for undergraduate or some shortterm postgraduate study programs), this technique is most appropriate. It affords speedy analysis, rigorous documentation of analytic procedures to demonstrate validity and verify findings, and swift retrieval of data for transcription, reporting and publication. It should be noted, however, that the approach to QDA described here will not always be appropriate. For instance, it does reduce the efficiency with which an analyst can search for related “strings” of text in order to conduct detailed analysis of the nuances of expression and meaning of particular ideas. And it would not be appropriate for conversation analysts or linguists who must record
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and document every minute semantic and syntactic detail in order to do their work. This said, we describe the technique in some detail below and then make further comment on some aspects of method. THE SIMPLE VERSION—TAPE-RECORDED DATA Most tape machines have a counter that ticks over as the tape plays through the machine. The innovation proposed here simply involves setting up a counterrun file that represents chunks of time (and therefore of the talk or images that transpired within them) as ranges of digits that appear in the counter window of the tape or video (e.g., 0–10, 11–20, etc.). Since it is a text file, it is the counter-run file that is read into programs such as The Ethnograph or NUD? IST and gets coded with theoretical ideas that come to mind as one listens to the original data source. The codings are mapped against the text units of the counter-run. The text units are the digits representing the number in the window of the tape counter and therefore representing various points along the tape. This allows researchers to code as they listen to the recorded data without first transcribing it, effecting a vast saving of time, effort, and cost. The same method can be applied, of course, to video data where the video machine has a built-in tape counter. Table I illustrates the method with the third column showing the text of the counter-run file. By conducting a preliminary test run, the analyst can determine the appropriate scale for the counter-run to accommodate speed of speech or the through-flow of useful, theoretically relevant bits of information. If the scale is over-coarse, too many ideas may be coded onto one band on the counter-run scale, and retrieving exemplary segments for later analysis and reporting will be less reliable and convenient. If it is too fine, there will be unnecessary “gaps” in the coding of the counter-run. During coding, the analyst can edit the counter-run text to include notes on the segment or snippets of text to signify, at a glance, the flow of the text or particular expressions/images that might be returned to for further analysis. It is possible to produce an abstract of the interview in this way, and this may provide some additional advantages over full transcription or no transcription for both researchers and participants.1 1 For
instance, Duncan (1997) suggests that an abstract and audiotape copy of interviews may allow more effective review of interviews by participants because the abstract is a smaller, more manageable document providing a succinct guide or index to the content of a taped interview, thus allowing participants to focus more carefully on the parts of the interview that are most important for them to review.
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Smith and Short Table I. Example of Coding Against Counter-Run
Transcribed Text (taken from an interview with a househusband—see Smith 1998) Q: Do you have an arrangement with U, your wife, where she pays you an amount each week or anything like that which is just yours to do with whatever you want to? A: No. We talked about something like that in the initial stages but we never really followed it through. Q: Why did you decide not to go that road and what road did you go down? A: Well I think it was a decision by default rather than a conscious decision—we tended to just have a joint account and as I was saying to you before I always tended to do the shopping, I still do the shopping so you just draw it out. So there’s money there—I mean we sat down and budgeted: there’s x-amount for you to spend each week—and you just don’t pay any attention to that (laughter). I guess we’ve never really had to worry about counting every penny (mm hmm), things like that so we’ve been fortunate that way—so long as there’s enough there to meet our bills and commitments we don’t have much problem.
Tape counter
Counter-run
Coding
189 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225
189
Financial arrangements
201
Planning the transition
206
211
Responsibility for shopping; power over expenditure
216
Budgeting; standard of living
221
INCORPORATING OTHER TECHNOLOGIES IN THE INNOVATION In order to make counter-run files, one can simply type and save a “template” file that can be reproduced for each transcript being analyzed, incorporated into, and coded in programs such as NUD? IST or The Ethnograph. Alternatively, one can utilize a feature of spreadsheets that makes the construction of a counter-run file very simple and efficient. Counter-run files to suit any scaling of a counter-run can easily be made this way. The way to do this is quite simple. In a Microsoft Excel spreadsheet (version 97 or later): 1. Put in two numbers, one under the other, in a column (say 0 and 10); 2. highlight both; 3. pointing at the bottom right-hand corner of the highlighted block, mouseclick and drag down the column (while holding the mouse button down).
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The package will fill in the cells, incrementing each by the difference between the values in the first two that you typed in. That is, if these numbers are “0” and “10,” the program will fill the next cells with “20,” “30,” “40,” etc., incrementing each time by 10. By doing this, the column can be copied and pasted directly into a word processor file or text editor (including the text editor in NUD? IST ) or, more easily, in the latest version of NUD? IST, N5, imported directly from the clipboard. The document can then be prepared in the usual way for importing into a program such as The Ethnograph and NUD? IST for analysis. You could copy it straight into such a package, but you may like to prepare it for use first in a word processor or text editor. This is useful if you wish to add information such as interviewee’s pseudonym, details of the data source, a fieldwork date, and any technical information, such as tape speed2 at the head of the document.
MORE SOPHISTICATED VERSIONS—DIGITAL DATA STORAGE Storing the data digitally and accessing it via software such as Sound Edit Pro (Macintosh) or Sound Forge (PC) for audio, or Avid Cinema for video, is now possible and is navigationally more reliable than using analog tape counters. Digital representations of the data also allow for the calibration of the “counter-run” to be further fine-tuned. To use digitized sound software, you may need to first convert the audio or video data into digital format; this may require special connectors and software to manage the connection between a video or tape machine and the computer. You also need considerable storage capacity to store these forms of data digitally. The chief benefit is the capacity to navigate easily around the data source by pointing and dragging the computer’s cursor or mouse pointer over a graphic representation of the data (e.g., an oscilloscopic trace of audio data). In deciding the scale of counter-run intervals in these applications, the issue of how much real time is represented by each point in the scale of the counter can be considered. In the case of the audio or video software, the amount of real time that is represented by each chunk of the counter usually can be varied. Sometimes it may be appropriate to use five-second intervals for each point on the counter scale, sometimes more, sometimes less. A very fast speaker, for instance, will conceivably need to be mapped against a fine time-scaling with fewer scalepoints in each range of the counter-run than might otherwise be used. The different combinations of the time-scale and counter-run intervals, and the characteristics of 2 You
may need to vary the speed at which the tape replays the recorded data (e.g., for slow or fast speakers or to increase audibility). If so, you would need to keep a record of the speed at which you replayed and coded each recording, so that you could return reliably to the same location on the tape from later transcription of a representative segment.
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Diagram 1. Flow Chart of Use of Different Technologies.
the data to which each seems to be best suited, are matters of personal preference and fitness for the task at hand. SUMMARY AND DISCUSSION Diagram 1 shows the steps involved in the process of integrating computerbased technologies to improve the efficiency of QDA. A Methodological Note We acknowledge that there is a degree of debate in qualitative analysis literature about the advantages and disadvantages of transcription and nontranscription methods of interpretation and analysis (DeVault 1990; Duncan 1997).
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Some analysts may prefer to work (to code) from a transcript of the data because it imparts a certain intensity to the work and closeness to the data. We take the view that the coding process is made more meaningful and accurate because multiple “listenings” and “viewings” of the data bring the researcher even closer to it than a transcript does. Listening to and viewing the data allows us to retain paralinguistic cues (body language and the like) that supplement the verbal “message.” Using the counter-run method described here maintains a direct link with the interview as “an interaction grounded in talk” (DeVault 1990) and facilitates the analyst’s interpreting the meaning-making aural dimensions of talk such as silences (Duncan 1997; Opie 1995; Poland and Pederson 1998), timing and pacing, pitch, tone, and volume. Thus, our method also avoids some of the pitfalls of “editing” first-person narratives (especially nonstandard patterns of speech) at early stages of analysis (Duncan 1997; Blauner 1987). A textual representation of the verbal always elides these complex and rich cues and so transcripts of data are a poorer second cousin to the original than are audio and video recordings. Facilitating the reliable and accurate return to the segments of original data that gave rise to theoretical notions eases the researcher’s task of demonstrating to an audience of colleagues that what is proposed is a reasonable interpretation of the data. Segments of data to be reported can be transcribed verbatim and published in support of findings, in the usual manner. What has been avoided by adopting this approach is the transcription of the entire body of data, thus saving much time and labor and, therefore, cost.
REFERENCES Blauner, B. (1987). Problems of editing first-person sociology. Qualitative Sociology, 10, 46–64. DeVault, M. L. (1990). Talking and listening from women’s standpoint: Feminist strategies for interviewing and analysis. Social Problems, 37, 96–116. Duncan, J. (1997). To transcribe or not to transcribe? That is the question. Education Research and Perspectives, 24, 1–13. Glaser, B., & Strauss, A. (1967). The discovery of grounded theory. Chicago: Aldine Publishing Company. Lofland, J. (1971). Analyzing social settings. Belmont, CA: Wadsworth. Opie, A. (1995). Beyond good intentions: Support work with older people. Wellington, New Zealand: Institute of Policy Studies. Poland, B., & Pederson, A. (1998). Reading between the lines: Interpreting silences in qualitative research. Qualitative Inquiry, 4, 293–313. Short, P. M. (1996). No-one to turn to: Estrangement, need and kinship economies. Paper presented to the 5th Australian Families Research Conference, Brisbane, Queensland, November. Also at and on Australian Family and Society Abstracts (FAMILY) database. Smith, C. D. (1998). Men don’t do this kind of thing: A case study of the social isolation of househusbands. Men and Masculinities, 1, 138–172. Strauss, A. (1987). Qualitative analysis for social scientists. Cambridge: Cambridge University Press. Strauss, A., & Corbin, J. (1990). Basics of qualitative research: Grounded theory procedures and techniques. Newbury Park: Sage.
