| Abstract | Over the past decade there has been a rapid growth in the development and use of computer programs to assist in the analysis of qualitative data. Computer programs can be used by qualitative researchers to perform a variety of analytical tasks, from simple editing functions through to complex theory development (Fielding, 1998; Gibbs, 2002; Miles & Huberman, 1994; Weitzman & Miles, 1995). Given this popularity and the diverse usage of computer programs, it is helpful to conceptualise programs as belonging to one of five broad categories based on analytical function. These categories listed in increasing levels of sophistication are text retrievers, textbase managers, code-and-retrieve programs, code-based theory-builders, and conceptual network-builders1. Thus each of these programs differ in the amount of analytical assistance it renders to the researcher.
At the lowest level of analytical assistance sits text retrievers. These programs search and retrieve words, phrases, and other string characters that have been determined by the researcher to be of analytic interest, be this inductively or deductively determined. To illustrate using my research interests in gender and discourse, I could search and retrieve all instances of the word feminine across each interview text file. In most text retrieval programs the raw data (e.g., interview file) is not entered into the program but stands outside the program where it is searched by the program. Textbase managers organise the raw data within the textbase manager program. That is, these programs store raw data and can systematically sort the database into meaningful subsets for comparison and contrast by the researcher. For instance, I could search and retrieve all instances of the word feminine across male athlete responses and across female athlete responses. Code-and-retrieve programs are an advance on the previous two categories in that they enable the researcher to retrieve and code the data. That is, lines, sentences or paragraphs can be coded on the basis of keywords. For example, I could search and retrieve all instances of the keywords feminine, girly girl, girl stuff, wearing make-up, and label all located items with the code feminine. The previous two program categories did not allow coding to be conducted at the same time as searching and retrieving nor did they allow for coding to be stored within the program itself.
Code-based theory-builders not only retrieve-and-code but also assist the researcher to develop and test theory. Here categories can be developed from the assigned codes, memo’s can be written and linked to these codes and categories, and hypotheses that have been induced from the data can be formulated and tested. For example, I could search and retrieve all instances for keywords feminine, girly girl, girl stuff, wearing make-up, and label all located items with the code feminine. In subsequent analysis the code feminine could be grouped with the code masculine into the category “gender stereotypes”. Further analysis of the gender stereotypes category could result in this being subsumed under the higher order theme “gender”. The development and defining qualities of the two codes and the two categories would be reported in a memo within the program that is attached to the category “gender stereotypes”. Indeed memos can be written for and stored for each search and each code. From this I may then hypothesis that the data appears to suggest that male athletes construct gender stereotypes differently than female athletes. This hypothesis could be tested via a search-retrieve-and-comparison of male and female responses. The last category is conceptual network-builders. These programs use semantically meaningful networks to build and test theory. Further, the researcher’s thinking and conceptualisation of the data can be represented graphically in these programs.
The aim of this chapter is to demonstrate the logic underpinning one particular code-based theory-builder, NUD*IST 6 which is produced by QSR International Pty Ltd. NUD*IST stands for Non-numerical Unstructured Data Indexing Searching and Theorising. The choice to discuss NUD*IST 6 is driven by my familiarity with the program. I used NUD*IST 6 in my Doctoral dissertation and extracts from this research will be used to illustrate points made during this chapter. The chapter will begin with a brief discussion regarding computer-aided analysis in qualitative research. A brief outline of what this chapter will not cover will then be presented. The main body of the chapter illustrates the logic and flexibility of the NUD*IST 6 program by contrasting the two interlocking data systems associated with the program, the document system and the index system. The chapter concludes with some limitations of the NUD*IST 6 program. |
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| Public Notes | Deposited with permission of publisher. The APA reference citation for this chapter is:
Lamont-Mills, A. (2004). Computer-aided qualitative research: A NUD*IST 6 approach. In H. Haag (Ed.), Research methodology for sport and exercise science: a comprehensive introduction for study and research (pp.288-298). Reinhein, Germany: Hofmann. Print copy on order for USQ Library 11/1/2007. |
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