Using Qualitative Data Analysis Software: Addressing the Debates

Qualitative Data Analysis Software Journal of Management Systems, Vol. XVI, No. 4, 2004, Copyright 2004, Maximilian Press, Publisher 33 ARTICLE Qualitative Data Analysis Software: Addressing the Debates Raewyn Bassett Dalhousie University, Canada ____________________________________________________________________________________________________ ! ABSTRACT Computerized data analysis presents exciting and innovative opportunities. More efficient data management, reduced paper volume, easier and more accurate investigation of data in depth and more refined, replicable and sophisticated analyses have legitimized qualitative research for many researchers. Others, however, point with pessimism to the tyranny that accompanies such benefits. Larger data sets, infinite coding and the ability of both to drive the analysis, as well as the nudging of qualitative research closer to the quantitative paradigm are feared outcomes by some researchers of the use of qualitative data analysis software. Both the optimism and the pessimism are explored by examining the advantages and disadvantages of qualitative computing, and the implications of these for qualitative methodology. ____________________________________________________________________________________________________ ! INTRODUCTION Word processing and the use of microcomputers are taken-for-granted components of research today. Yet many qualitative researchers have considerable reservations about the use of computer programs in the analysis of their data (Drisko, 1998; Este, Sieppert & Barsky, 1998; Maclaran & Catterall, 2002; Wall, 1995). A relative newcomer to the field of qualitative research, computerized data analysis presents exciting and innovative opportunities. More efficient data management, time management, reduced paper volume and shuffling of paper, easier and more accurate investigation of data in-depth, system closure, and more refined, replicable and sophisticated analyses have legitimized qualitative research for many researchers (Dolan & Ayland, 2001; Richard & Richards, 1991; St John & Johnson, 2000; Weitzman, 1999). The 1980s was the development decade for qualitative data analysis software, with the first programs for widespread public use available in the early 1990s. Early publications focused on descriptions of the software and, often written by the developer, included the rationale for unique structural features (Hesse-Biber, Dupuis, & Scott Kinder, 1991; Muhr, 1991; Richard & Richards, 1991). Not until the late 1990s did the literature take a critical turn, comparing one or more software (e.g. Barry, 1998; Drisko, 1998; Lewins, 1996; Lewis, 2002; Mangabeira, 1995; Walker, 1993; Walsh & Lavalli, 1997) or computerized versus traditional methods of qualitative analysis (Dolan & Ayland, 2001; Webb, 1999), and discussing advantages and disadvantages of qualitative computing (Buston, 1997; St John & Johnson, 2000; Tallerico, 1992; Wolfe, Gephart, & Johnson, 1993). What delayed the debate? Was the software so seductive that its effect on research was seldom questioned? Or was there such a paucity of literature on how to proceed with qualitative analysis beyond the collection of data and that having found a method in the software, researchers were loathe to question it? Whatever the reasons, there remains, as yet, little discussion of the impact of qualitative data analysis software on the qualitative research paradigm. Methodological development is undoubtedly taking place alongside the development of the software itself (Hinchliffe, Crang, Reimer, & Hudson, 1997; Richards & Richards, 1991). Implicit in the development of qualitative data analysis software has been the premise that the methodological integrity of qualitative research can be retained with the use of computer-assisted data analysis (Drass, 1980). In this paper, the literature on qualitative computing will be reviewed. Following a description of types of software, both the advantages and disadvantages of computer use for qualitative research will be outlined. Rather than dwelling upon one or other side of the arguments for and against its use, a middle ground will be presented that seeks not to immobilize the potential user through skepticism or discouragement nor lead them seductively to overestimate the power of qualitative computer use. Implications of the benefits and pitfalls of qualitative data analysis software for methodological development will be considered. Journal of Management Systems, Vol. 16, Number 4, 2004 34 Raewyn Bassett ! QUALITATIVE DATA ANALYSIS PROGRAMS In fields as diverse as market research, the social, human and health sciences, and library, administration and management sciences, software for the analysis of qualitative data is increasingly being put to use. Facilitating the management and organization of data, qualitative software consists of five overlapping types. Text retrievers such as WordCruncher search, find and sort words and phrases. Textbase managers such as askSam and FolioVIEWS organize and manage text before searching and retrieving data. Memos and comments can be attached to text, and hyperlinks between text segments made. Code-andretrieve programs such as Code-A-Text, Kwalitan, and Transana assist the researcher with splicing data into segments or chunks, attaching labels to data, and finding and displaying requested instances of data. Memoing and hyperlinking may also be included. Code-based theory-builders, for example, HyperRESEARCH, and NUD*IST, allow connections between codes beyond the capabilities of the code-and-retrieve programs, and provide sophisticated memoing abilities. Classifying and categorizing of data, and formulating and testing of propositions are facilitated. Conceptual network-builders such as ATLAS/ti and NVIVO add to code-based theory-builders the development and testing of theory, and include the facility to graphically map conceptual networks (Miles & Weitzman, 1996; Rodgers, 1995). Researchers’ decisions about the software are shaped by the availability of support, colleagues’ use of software, perceived user-friendliness of the software, and the advantages and disadvantages software use provides. ____________________________________________________________________________________________________ ! THE ADVANTAGES OF QUALITATIVE DATA ANALYSIS SOFTWARE Analysis of data requires a set of mechanical operations. Traditionally performed by hand in qualitative research, these tedious, time-consuming and mundane tasks are executed more easily and efficiently using the computer. Numerous researchers have attested to the benefits of qualitative computing (Buston, 1997; Drass, 1980; Lee & Fielding, 1991; Richards & Richards, 1991; St John & Johnson, 2000; Weitzman, 1999; Wolfe, Gephart, & Johnson, 1993). Data in written form can be speedily recorded and rapidly retrieved (Richard & Richards, 1991; Weitzman, 1999). Similarities, differences and relationships between text passages can be identified and systematically coded and retrieved (Kelle, 1997; Wolfe, Gephart, & Johnson, 1993). Coding schemes can be easily and quickly changed, at whim. Ideas can be inserted as they occur, and new coding and new codes created at will. The raw data remains close by, at hand, immediately available for investigation (Drass, 1980; Lee & Fielding, 1991; Richards & Richards, 1991; St John & Johnson, 2000; Tallerico, 1992; Tesch, 1991). A greater volume of data can be collected since the ease and efficiency of computerized search-and-retrieval enables huge quantities of material to be thoroughly interrogated (Dolan & Ayland, 2001; St John & Johnson, 2000). No longer constrained by the capacity of the researcher to retain information and ideas, infinite amounts of data produce unlimited possibilities for analysis (Richard & Richards, 1991). Less paper makes the analytical process significantly less cumbersome and less tedious (Lee & Fielding, 1991; Richards & Richards, 1991). Further, its not merely a question of being able to collect and store more data, but an enhanced ability to sort, sift, and think through the patterns to be found there (Richards & Richards, 1987; Tallerico, 1992; Tesch, 1989). Not only are the clerical-type tasks rapidly dealt with, but the quality of data analysis is enriched. The computer’s ability to handle large volumes of data more easily and efficiently means that data is less likely to be disregarded, or worse, lost. The small but insignificant pieces of information buried within the larger mass of material are more effortlessly located; the deviant case more easily found. Researchers are encouraged to ‘play’ with their data, to enjoy the analytic stages, rather than approaching this process as one that is tedious and boring (Lee & Fielding, 1991; Richards & Richards, 1991; St John & Johnson, 2000; Tesch, 1989). The research routine, too, is altered. The savings in time also amount to savings in energy for qualitative researchers. Intellectual energy can now be directed towards the analysis rather than tiringly spent on the mechanical tasks of the research process (Conrad & Reinharz, 1984; St John & Johnson, 2000; Tesch, 1989). Coding can now begin as soon as the first data is collected, then also providing the opportunity for a more thorough reflection of the data collection process, with consequent redirection if necessary. Coding becomes a process that goes the length of the project, rather than merely being one stage of Journal of Management Systems, Vol. 16, Number 4, 2004 Qualitative Data Analysis Software 35 the research. It becomes much easier to multi-task, entering data, coding, testing assumptions, for example, almost simultaneously, rather than pursuing these in linear fashion as traditional qualitative methods encourage (Richards & Richards, 1991; Tesch, 1989). Not least is the destigmatization and legitimation of qualitative research with computerized qualitative data analysis more readily accepted and used. Qualitative research has tended to be defined in relation to quantitative research. That is, the “‘non-quantitative’ handling of ‘unstructured’ data” (Richards & Richards, 1991:39). Negatively defined, of low status, and with a reputation for untrustworthy results supported by anecdotes, computerized qualitative data analysis has legitimized qualitative research by providing the rigour and transparency that qualitative method ostensibly lacked (Kelle, 1997; Richards & Richards, 1991; Tesch, 1989; Webb, 1999; Weitzman, 1999; Welsh, 2002). ____________________________________________________________________________________________________ ! THE DISADVANTAGES OF QUALITATIVE DATA ANALYSIS SOFTWARE The speed and efficiency with which clerical tasks can be undertaken by computer, the propensity for larger data sets, and the flexibility of coding schemes provide not only benefits but further implications as well. A revered, if not reified, aspect of qualitative research is ‘closeness to data’. Many qualitative researchers value close involvement and interaction with the data, and fear the loss of this relationship with computerized qualitative data analysis. The tactile, handling of data has been extolled by many, often linked to research insights and creativity (Webb, 1999). In contrast, quantitative researchers have been perceived by qualitative researchers as being distanced from the data, engaged with computerized analysis of huge data sets (Buston, 1997; Hinchliffe et al, 1997; Mangabeira, 1995; Ragin & Becker, 1989; Seidel, 1991; St John & Johnson, 2000; Tesch, 1989). Qualitative research, traditionally, has tended to value smaller data sets, trading breadth for depth, the latter one of the prized characteristics of qualitative research. The inherent difficulty, however, in dealing in-depth with huge quantities of data has constrained traditional qualitative analysis. At the same time, there has been the criticism by the quantitative research community that no basis exists, namely quantity, for staking qualitative research claims. The results of qualitative research have been, quite simply, labeled untrustworthy (Tesch, 1989). The ability to now efficiently and speedily search, retrieve and analyze every bit of data, no matter how large the data set, by computer, is enticing. The concern lies where a large data set is selected merely because the availability of the technology makes it more feasible to do so. In addition, volume of data may well drive the analysis, with important and interesting insights missed. Instead of analyzing identified phenomena, for example, the analysis may be reduced to the counting of occurrences. Attention and energy may well be spread out over large numbers of instances with little analytical effort put into any one instance. The ease with which computer technology allows the researcher to use voluminous data may well lead to a trade-off of resolution for scope (Mangabeira, 1995; Seidel, 1991). Commodification of qualitative research may occur. The speed and efficiency of computerized qualitative data analysis can all too easily result in a lack of knowledge and understanding as to how the final results were achieved. Seduced by the convenience and credibility of the program’s rendering of sense, attention is diverted from the logic and research design issues that actually govern the adequacy of analyses (Lee & Fielding, 1991). An added outcome is ‘quick and dirty’ research characterized, in addition, by preemptive data reduction and quasi-quantitative analysis posing as qualitative (Richards & Richards, 1991; St John & Johnson, 2000). Perceived by many researchers as merely a tool (Drisko, 1998; Smith & Hesse-Biber, 1996), qualitative data analysis software may come to define method (Agar, 1991; Levin, 1986). The unstructured abyss that often follows data collection, the sense of not knowing what to do next, and the desire for an ordered process may lead to the grasping of a software program that then becomes the method (Hinchliffe et al, 1997). The problem, it has been suggested, is that the use of software presupposes a way of doing research (Agar, 1991). Journal of Management Systems, Vol. 16, Number 4, 2004 36 Raewyn Bassett Further, the structure of individual software programs may influence research results. Where software defines method, the goals of the study may be completely missed. In addition, studies comparing software programs using the same data have found a qualitative difference in results for each progam (Walker, 1993). This points to the importance of knowing the developer’s assumptions about research that are built into the program (Mangabeira, 1995), as well as the implications of using one program only for analysis of research data. A further criticism, and one linked to the developer’s assumptions about qualitative research, has been an increasing tendency towards homogeneity in what has been a methodologically diverse field. While a few programs have moved beyond the requirement for text in ASCII format, most qualitative data analysis software continue to have this requirement, in addition to expecting the data to be easily divided into single files, and drive the research process towards code-an-retrieve at the expense of any other approach. Rather than a creative approach to analysis, researchers are led to methodically code every chunk of text. Rather than thinking about the data-emergent ideas, creativity is immersed in a process in which coding remains a task to be done, and a way of controlling messy data (Coffey, Holbrook & Atkinson, 1996; Hinchliffe et al, 1997; Richards, 1996; St John & Johnson, 2000; Webb, 1999). Linked here is the central role of grounded theory as a premise of some of the qualitative data analysis programs. The choice of methods available, for example, coding, have been argued to prescribe choice of method. Homogeneity in the software can coerce qualitative research in restricted directions (Coffey, Holbrook & Atkinson, 1996; Crang, Hudson, Reimer, & Hinchliffe, 1997; Hinchliffe et al, 1997; Lonkila, 1995; Ragin & Becker, 1989; Richards & Richards, 1994). Closing of the quantitative-qualitative gap had also been viewed as problematic. There has been renewed insistence in the past decade or so that techniques for sorting and analyzing qualitative data be rigorous. The introduction of computers has coincided with an increased call in qualitative research for developing and justifying rigorous methods of data processing. Absurd as is the notion of the absence of rigour in qualitative research, nonetheless this supposed lack has burdened the paradigm with low status and a reputation for untrustworthy results. Rigour in qualitative research has pushed towards quantification and scale. Coincidentally, qualitative data analysis software is ideally placed to serve just this goal, and can be expected to reinforce any trend towards dealing with “soft data” rigorously (Richards & Richards, 1991). Computer techniques have provided the opportunity to quantify a wide range of characteristics of textual data. The trend in the past two decades or so has been towards harder analysis of soft data. The computer offers a “Trojan horse for the infiltration of the narrowest goals of quantitative sociology” (Richards & Richards, 1991: 40). Those goals include the positivist dream of converting raw phenomena into data that can be treated scientifically. Objective data, susceptible to measurement and quantification, and able to be generalized beyond the sample analyzed, are required (Richards & Richards, 1991). Qualitative software itself, not merely its use, is instrumental in bridging the gap between quantitative and qualitative methods. Some qualitative data analysis programs include quasi-statistics, and many provide the facility to export or import data to and from a statistical package such as SPSS. Certainly there is a ‘blurring’ between the two methods (Bassett, Cox & Rauch, 1995), with the microcomputer providing “a common technical ground for the meeting of qualitative and quantitative researchers” (Smith & Hesse-Biber, 1996: 428). ____________________________________________________________________________________________________ ! THE POTENTIAL OF QUALITATIVE DATA ANALYSIS SOFTWARE Are the disadvantages of qualitative data analysis software sufficient to deter researchers from qualitative computing? Some qualitative researchers maintain that too much is made of the disadvantages, and that these so-called constraints can actually work to expand a researcher’s thinking about their data (Kelle, 1997; Lee & Fielding, 1996). First, replacing the ‘tactile’ with the ‘digital’ may not be quite the loss envisioned by some qualitative researchers. The creativity that potentially comes with ‘feeling’ the data as we shuffle through mounds of paper may itself be mythical, or at the very least, prevent other forms of creativity. For those wedded to the need to feel the data on paper, however, this need not be precluded by importing the data into the computer. A hard copy is always possible, and many qualitative researchers work in both mediums, with data on screen and on paper. Digitalization of the data, however, may lead us to ‘see’ in other Journal of Management Systems, Vol. 16, Number 4, 2004 Qualitative Data Analysis Software 37 ways, encouraging further creativity (Hinchliffe et al, 1997). It has been suggested that the gap between quantitative and qualitative research is mythical (Richards & Richards, 1991). Some researchers, both quantitative and qualitative, welcome the convergence, perceiving the possibility for quantitative researchers to attend to more diversity in their analyses, and for qualitative researchers to compare and contrast more thoroughly across cases (Kuckartz, 1995; Ragin, 1995; Ragin & Becker, 1989). Computational techniques could lead qualitative research away from its traditional base towards the stereotypically quantitative. On the other hand, qualitative computing “may push qualitative research towards far more subtle, varied, powerful, and rigorous ways of doing what the method has always attempted to do” (Richards & Richards, 1991:53). Grounded theory has been a predominant assumption embedded in some qualitative data analysis software. Early articles written by developers indicate that for some of them, this has been so (Hesse-Biber, Dupuis, Scott Kinder, 1991; Muhr, 1991; Richard & Richards, 1991). This does not apply to all qualitative data analysis software developers, however. Qualitative computing is a large and growing field, and it is important to understand the assumptions embedded in the software (Buston, 1997; Lee & Fielding, 1996; Mangabeira, 1995). Knowing something of the developer’s ideas about qualitative research is important, regardless of whether these are based in grounded theory or another qualitative method.(Kelle, 1997). While many software programs invite coding and retrieval as a first step in analysis, code and retrieve programs are not all the same. Many diverge in analysis functions after this first step (Webb, 1999). The software, NUD*IST for example, connects coding in hierarchical categories enabling the analyst to explore relations between codes and categories. NUD*IST also has links with two other qualitative software, Decision Explorer and Inspiration (both concept mapping programs), allowing the researcher to export their coding system to another format for further consideration and visualization. Another software, ATLAS/ti, enables both hierarchical and non-hierarchical semantic networks to be developed. HyperRESEARCH, makes possible the identification of associations between codes with the use of hypothesis testing. Both ATLAS/ti and NVIVO software facilitate hyperlinking between video, audio, text and graphic segments. Still other software employ further strategies that encourage visioning the data anew in ways difficult to achieve in the traditional manual method of doing qualitative research (Kelle, 1997). The combination of text, audio and visual data in research, and the hyperlinking of these sources take qualitative methodology to realms as yet little explored. It is a mistake to assume that a new technology will be used simply to extend rather than alter an existing practice. New technologies designed to streamline or otherwise enhance the conduct of familiar social routines may end up so reorganizing them they become new events (Snyder, 1997). The development and use of software undoubtedly changes the methodology of qualitative research (Richards & Richards, 1994). ____________________________________________________________________________________________________ ! CONCLUSION What seems evident in the discussion about the advantages and disadvantages of computer use in qualitative research is the need for awareness and reflexivity on the part of the researcher of the capabilities of the software, and what that means for the analysis of their research project (Murphy & Pardeck, 1991; Tallerico, 1992). Researchers need not be ensnared by the software program. The researcher can have control over their use of the software, abandoning it if it does not meet their requirements (Buston, 1997). Now, however, researchers can design projects that would have been unthinkable without qualitative data analysis software. The code-and-retrieve model embedded in many qualitative data analysis programs was the cut-and-paste of traditional qualitative research, a method that even then had serious problems (Richards, 1996). Qualitative software allows the ‘constant interrogation’ of themes, both emerging and developing, enhancing theory construction and theory testing. And the confidence and thoroughness with which claims can be made and validated far exceeds that of traditional qualitative research (Richards & Richards, 1994). The advantages of no longer losing data, of being able to retain and manipulate ideas instead of being limited by the capacity of the human brain, and of being able to take advantage of new visual approaches to the whole Journal of Management Systems, Vol. 16, Number 4, 2004 38 Raewyn Bassett research project, facilitate innovative and creative ways to think about the data while contributing to the development of qualitative methodology. ____________________________________________________________________________________________________ ! REFERENCES Agar, M. (1991). The Right Brain Strikes Back. In N.G.Fielding & R.M. Lee (Eds.) 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Walsh, B., & Lavalli, T. (1996). Beyond beancounting: qualitative research software for business. Microtimes, 162. Webb, C. (1999). Analyzing qualitative data: computerized and other approaches. Journal of Advanced Nursing, 20, 2, 323330. Weitzman, E.A. (1999). Analyzing qualitative data with computer software. Health Services Research: Part 2, 34, 5, 12411263. Welsh, E. (2002). Dealing with Data: Using NVivo in the Qualitative Data Analysis Process. Forum: Qualitative Social Research, 3, 2. Wolfe, R.A., Gephart, R.P., & Johnson, T.E. (1993). Computer-facilitated qualitative data analysis: Potential contributions to management research. Journal of Management, 19, 3, 637-660. ____________________________________________________________________________________________________ ! ABOUT THE AUTHOR Raewyn Bassett has a doctorate in Sociology from the University of British Columbia, Vancouver, Canada. She currently teaches on-line graduate courses at the School of Occupational Therapy, Dalhousie University, Halifax, Nova Scotia, and free lances as a research consultant. She has extensive experience of a number of qualitative data analysis software. Working with the software in social demonstration research, clinical trials, evaluation research and micro-ethnographies have provided her with a laboratory-of-sorts for observing and experiencing the interplay of the advantages and disadvantages of qualitative computing. Journal of Management Systems, Vol. 16, Number 4, 2004 View publication stats