Going the Distance in Caqdas_2002

8/10/2019 Going the Distance in Caqdas_2002

1/15

Going the distance: closeness in

qualitative data analysis software

LINDA S. GILBERT

(Received 3 October 2001; accepted 18 March 2002)

This study about researchers transitions to using qualitative data analysis (QDA) softwareidentified three stages of closeness to the data: the tactile-digital divide; the coding trap;

and the metacognitive shift. The tactile-digital divide involves adapting to working onscreen instead of paper, an initially distancing process. As users gain comfort with thesoftware, they experience the coding trapan issue of too much closeness to the data.Users warned that there was a tendency to become bogged down in coding, and developedstrategies to provide analytical distance. The metacognitive shift involves learning to thinkabout software processes with the same level of reflectivity that should accompanyqualitative research processes in general, including developing strategies for errorrecognition. These transitions invite reflections on the nature of cognitive tools andexpertise with them, which lead to implications for evaluating research and consideringtrustworthiness.

Introduction

Concerns about losing closeness to the data pervade conversations aboutqualitative data analysis (QDA) software (Agar 1991, Mangabeira 1996,Weitzman and Miles 1995). In their 1998 report on focus groups conductedwith QDA users, Fielding and Lee (1998) attempted to examine theseconcerns in more detail, and began to differentiate between aspects of

closeness.One form of closeness involved a living knowledge of the content:being able to recover the sights, sounds, and experiences of being in thefield (Fielding and Lee 1998: 74). However, they observed, sometimes,when researchers refer to being close to their data, what they have in mindare the tactile and perceptual aspects associated with data handling(Fielding and Lee 1998: 75). Closeness was essentially equated withpositive sensations associated with handling fieldnotes, and distanceequated with the discomfort associated with the limitations of early

software screens. Thus, the term closeness to data conflates two differentconstructs: knowledge of content and pleasure in handling data.

INT. J. SOCIAL RESEARCH METHODOLOGY, 2002, VOL. 5, NO. 3, 215 228


2/15

of closeness as full knowledge of the content and full access to the dataitself. She further differentiates between closeness to the original data andcloseness to contextual information about those data. According to Richards,access to the original text is strongly supported by most QDA software,

includingNUD*IST. However, such access represents an unrecognized skewin methods: previously, most qualitative methodologies emphasize[d] thecreation of interpretive records from which to work (p. 322).

Richards also makes a critical observation: as much as qualitativeresearchers openly value closeness to the data, they also need distance fromthe data. Qualitative research requires an in-out process: researchers haveto achieve and manage both ways of zooming in and ways of achieving awide-angle view (Richards 1998: 324). She argues that closeness promotesfamiliarity and appreciation for subtle differences, but distance allows

abstraction and synthesis.Distance issues were also apparent in a recent study I conducted on

qualitative researchers who transitioned from manual practices to usingNUD*IST (Gilbert 1999). The purpose of the study was to describe howindividual qualitative researchers perceive that their research proceduresand perspectives have been influenced by the adoption of computer-assisted qualitative data analysis software (QDA software). The primarysource of data was in-depth interviews with qualitative researchers who hadexperience conducting qualitative analyses both manually and withNUD*IST software. (NUD*IST was selected because of its long history ofdevelopment and its wide distribution. Participants in this study were usingversions 3 and 4 of NUD*IST.) Participants compared their two workingmethods and reflected on the process of transition between them. The datawere analysed through individual case profiles and cross-case comparisons,both informed by phenomenological perspectives. The study was furtherinformed by activity theory, a socio-cultural theoretical perspective thatregards individuals and tools as mutually influencing one another.

In that study, I saw three levels of distance issues, which seemed tosurface somewhat sequentially as a user became familiar with the program.The first related to the perceptual aspects of handling data, as noted byFielding and Lee (Fielding and Lee 1998). The second related to closenessto the data, in terms of knowledge and access, similar to that discussed byRichards (Richards 1998). However, a third aspect of distance hasdeveloped in conjunction with the use of QDA programs. This form ofdistance relates to understanding and monitoring operations on the dataperformed with the assistance of these programs, and requires users to

extend their metacognitive awareness to software processes as well as theirown cognitive processes. In this paper, I will discuss the first two stages of

216 LINDA S. GILBERT


3/15

learn NUD*IST (or other programs) in part because they find this stage toouncomfortable. A number of the participants in this study comparedlearning to work with data on screen to learning to write on the computer:initially, they found the process cumbersome, but gradually adjusted.

Several of the participants described how they learned to work on thecomputer. Betty coded on the hard copy at first, then entered the codinginto NUD*IST in a second step; later, as she became more comfortable withthe program, she read from the hard copy but coded directly on screen.Both Frances and Diane recalled that they didnt feel close to their data atfirst, then got used to working with data in the program. Francesacknowledged that when she analysed her early feelings, she realized thatshe simply felt a greater sense of ownership about the data when it was onpaper. I, Ive sort of had to think through what it is about this closeness to

the data, the ownership of the, of the data is about. . . At this point, shesaid I can feel just as close to the data on the screen, as I can, um, on thepaper, it doesnt, you know, it doesnt have the same distancing effect onme. But it did for a little while. Some of that was due to the differencebetween pages that could be spread out, and scrolling back through text.Now, she actually finds it easier to link data on the computer.

For users who persevere, the tactile-digital divide seems a temporaryperiod of discomfort, followed by a synthesis of paper and computermethods. Most participants eventually established a combination ofworking on paper and working on the screen that they found comfortable,mingling paper-based and program-based coding. Alice said there cometimes when I have to print it out...I cant work solely in the computer.Diane also printed out sections to code on paper, though she seemed to feelthat she was unusual in doing so: And I know other people do everythingon the screen, but I, I dont. Based on her experience as a consultant andtrainer, one participant theorized that coding on paper and then enteringthe codes was a transitional practice that faded as users became

accustomed to working on screen, but the practice seemed persistentamong the participants in this study. This finding corresponds to Fieldingand Lees (1998) observation that most of their participants continued toexamine their data on hard copy as well as on screen.

I have two theories concerning the tactile-digital divide. First, it may beexacerbated by the mechanics of handling the text. NUD*IST 4, like otherQDA programs of its generation, required that the data be formatted inplain text (ASCII). This format does not permit the use of bold, italics,colour, or even variations in font or text size. Researchers already

accustomed to using such text characteristics to enhance meaning (forexample, indicating changes in the speakers intonation) may find it

217GOING THE DISTANCE


4/15

capabilities. Chris, who considered coding on paper a transitionalstrategy, herself used some non-computer analytical strategies. Dianeliked to print because for some things, I want to put them together in waysthat I cant put them together, the way NUD*IST gives them to me. In my

own experience, I worked on paper at times because NUD*IST does notsupport concept maps for data exploration, and I did not own the concept-mapping software it exported to. However, Frances observed that her teammembers labouriously created data representations that NUD*IST couldhave generated directly from the coding, because they were unaware of itscapabilities to create tables and matrixes. Thus, the temporary discomfortof working on-screen could easily be confused with real or perceivedlimitations of the software, prompting some users to conclude I just cantwork that way before theyve given themselves sufficient time to adapt.

Since my study involved users who did learn to work on screen, both thesetheories are preliminary.

Stage 2: the coding trap

The second aspect of distance from the data is the coding trap.Surprisingly, the issue involves too much closeness to the data, not toomuch distance. It seems to become noticeable after the user has developedsome facility with the basics of the program, after overcoming the tactile-digital divide.

As Fielding and Lee (1998) noted, though non-users express concernabout a distancing effect, QDA users actually feel that the software makesthem more knowledgeable about their data. This general feeling ofcloseness was confirmed in this study: participants felt NUD*ISTs abilityto retrieve both data and context was invaluable, and fostered familiaritywith the data. In fact, capabilities like jump to source were seen as

promoting a level of closeness difficult to achieve manually. (Jump tosource allows the user to view a coded excerpt within its original context.)Even newer users like Betty felt that non-users worries about distancefrom the data were completely unjustified. She found closeness to the datato be an advantage of the software: the beauty of it, I think, is that it letsyou be closer... have access to where your findings came from.

However, some participants specifically identified the need to balancecloseness to and distance from data. Alice acknowledged you can getclose to the data, and NUD*IST gives you many, many, many ways to do

that, but getting away from it (pause) to abstract up (pause) I have to print.Chris observed I know people talk about being close to the data, um



5/15

strongly related concern was that coding could become mechanical orunthinking. According to Chris, if youre just in the browser and yourecoding....I think its very easy to lose sight of where youre going, whatyoure analysing. Others offered similar warnings.

Another related topic involved the search tools. As several participantsobserved, the index search tools in NUD*IST depend upon the underlyingcodes. If the user wants to take advantage of these advanced features, fine-grained coding is necessary. Since NUD*IST manages multiple codes, allpotentially relevant distinctions can be tracked. From using NUD*ISTmyself, I can see that it would be easy to fall into the unconsciousassumption that because NUD*IST can manage complex coding systemsand can track demographics and other information that might be relevant,the user shouldtrack all the information available.

Extensive coding can also provide an excuse to delay other steps of theanalysis process. Gwen commented that one could go on doing somethingwith NUD*IST to avoid making decisions about what your datas saying.Individuals who are uncertain about the processes of qualitative researchmay be especially prone to such a problem. Alternatively, the programinterface itself may somehow over-emphasize the coding process: Diane, ahighly-experienced qualitative researcher, initially fell into the coding trapdespite her well-established manual coding methods. Of course, these twotheories are not mutually exclusive.

For the most part, participants became highly aware of the tendency toget sucked in to coding. Though they did not necessarily define the codingtrap as an aspect of too much closeness to the data, the strategies theydeveloped to mitigate the problem generally provided analytical distance.Gwen simply noticed that NUD*IST created an expectation of thoroughcoding, and deliberately chose not to code at that level. Other strategiesincluded: (1) alternating working on the computer with working on paper;(2) reflecting on the index tree categories; (3) writing memos; (4)

maintaining a focus on the research questions; (5) coding systematicallyfor specific research-related themes; (6) using other software programs withdifferent strengths and weaknesses; and (7) using complementary manualmethods. Thus, the majority of the participants in this study found ways tocompensate for the coding trap, just as they managed to negotiate thetactile-digital divide.

Though coding trap may be a manifestation of too much closenessthat is particularly apparent in computer-assisted analysis, it highlights ageneral issue that pervades qualitative research. General texts on analysis

address the importance of distance as well as closeness. One advises ...keepdrawing back in order to think about the total picture. Descend into detail,



6/15

closeness is so much more achievable, the need for a broader, balancingperspective becomes more apparent.

Stage 3: the metacognitive shift

The final stage of distance lies at a different level from the first two. Itinvolves learning to think about software processes with the same degree ofreflectivity that should accompany qualitative research processes ingeneral. In this stage, metacognitive processes, which are concerned withthe regulation of thinking and learning, extend to include software use.Though beginners often displayed aspects of this form of awareness, itseemed to develop in conjunction with expertise with the program.

Self-monitoring is one aspect of metacognition: thinking about how andwhy one works in a particular way. For example, when users consciouslyrecognized the coding trap and developed strategies for dealing with it, theydisplayed a metacognitive awareness of their working patterns. Experi-enced users developed a highly reflective attitude toward software use thatthey strove to impart to novices.

Error recognition demonstrates another aspect of metacognition. Theusers with the greatest level of expertise were also most aware of thepotential to make mistakes with NUD*IST. Frances described thepossibility of setting up a complex search without realizing that this isnot what I really want to ask. Chris ran checks to make sure that the codesin her command files had been properly applied. These experienced usersactively looked for indications that their intentions and results corre-spondedanalytical feedback that assured them that they had accom-plished their goals.

These participants obviously sought to avoid constructing a searchincorrectly, or unwittingly miscoding data. I have characterized these types

of potential errors as unmindful transformations: that is, doing somethingwith the data, but being unaware that the results were not what wasintended. Though these users developed strategies to confirm their results,inexperienced or unaware users could conceivably make mistakes theynever even recognized as such, distancing them from their data in aprofound way. In processes such as applying codes with a command file,small errors can have broad effects. I consider unmindful transformationsto be simultaneously a consequence of NUD*ISTs power, and the mostworrisome aspect of distance from the data.

A personal experience reinforced my own awareness of unmindfultransformations. I used a command file in NUD*IST to apply automatic



7/15

the twin questions what exactly am I trying to do? and did that do what Iwanted? bracketed my every use of the program.

Though I see unmindful transformations as the most critical form ofdistance from data, I do not see this problem as unique to either NUD*IST

or to qualitative data analysis programs in general. Users of most complexcomputer programs need to be alert to subtle signals in order to detectproblems. Results that violate some expected condition are one commontype of indicator. For example, SPSS users who notice that the average of agroup of numbers exceeds the maximum value (as in a class average of 112for a test that has only 100 points) can be fairly sure that some of the datahas been entered incorrectly. On a reassuring note, since qualitative dataanalysis requires intimate knowledge of the data, a QDA user would havemore opportunities to find anomalies than users of many other complex

programs.Chris (and other advanced users) developed specific strategies for

verifying that results were in line with expectations. These strategies maybe identified and taught to newer users. However, the strategies themselvesare less important than their underlying motivation: the awareness thatcomplex software like NUD*IST needs to be monitored.

Another way in which metacognition was demonstrated was inidentifying trade-offs: consciously assessing desirable and undesirableeffects of different ways of working with the software. Examples includeone participants speculation about whether using NUD*IST earlier in herproject might have focused the data collection morean advantageat thecost of allowing the earlier interviews to be too significanta disadvantage.As she put it, a bit of win-lose in that. Betty, a relative novice, showed thiskind of reflectivity when talking about coding online or on paper. Sheobserved that the advantages of seeing the coding structure might be offsetby the tendency to fit codes into existing strategies, limiting thedevelopment of categories too quickly.

Reflective users also monitored how the use of a program affected theirworking methods over time, sometimes in subtle ways. Participants whohad used NUD*IST over an extended period usually adapted their workingmethods and use of the software to accommodate one another. Forexample, Chris identified changes in her initial working processes that sheascribed to the capabilities of QDA software. She now begins by codinglarge chunks, organizing her data so that she can reflect on it and developmore subtle categories. In contrast, she had previously begun her manualanalysis by assigning fine codes, from which she built up her larger

categories. Gwen recognized the issue of setting boundaries so that the userdoesnt end up doing something with NUD*IST to avoid having to make a



8/15

Diane had initial problems that revealed preconceptions she had aboutNUD*IST: I thought that...I really had to have all this tree all figured outbefore I started...I thought NUD*IST was imposing much more on my(pause) way of thinking than it actually ended up imposing. A user with

less manual experience might never have questioned that initial assump-tion, and would have been more influenced by their incorrect under-standing of the softwares requirements.

Users who fail to reflect on their use of software are at risk for becomingdistant from their data-handling through unmindful transformations ofthe data or through unconscious trade-offs in their working methods. Toavoid this aspect of distance, metacognitive awareness needs to be fosteredin users of NUD*IST and similar cognitive tools.

On the other hand, users who learned to use the software effectively

found that they not only felt close to their data, but that they felt close totheir software. One participant referred to being one with the machine,and several described how different programs fit with their thinking. Atthis level, the program operates as a functional organ, extending thecapabilities of the user (Kaptelinin 1996). Several users felt strongly thatusing NUD*ISTincreased the validity of their work, because they could useit to explore their data more thoughtfully and to check for errors at levelsthat would be impossible manually.

The participants in this study consistently used the metaphor ofNUD*IST as a tool, a metaphor also found in literature surrounding QDAprograms and computers in general (Tesch 1990, Jonassen and Reeves1996, Hannafin 1999). This metaphor merits further examination withrespect to its implications for developing metacognitive awareness.

Exploring the tool metaphor

Tools extend and qualitatively change human capabilities. Some activities,like sawing, cannot be performed without tools; others can. Even activitiesthat can be performed without tools can become transformed with theiruse. Transportation tools provide an obvious example: people have alwaysbeen able to walk, and thus could travel prior to the invention of thecarriage, the locomotive, the automobile, or the airplane. Yet each newtransportation tool changed the scope of travel, simultaneously trans-forming the landscape and setting new expectations. Though walking hasnot been abandoned, it is generally used for shorter distances or for

pleasure, not as a serious form of regular transportationat least indeveloped countries. The effects of these changes have been far-reaching.



9/15

that usually are built into the tools by their developers. The goalsachieved by people equipped with a tool are often influenced by the toolsgoal, and the final results differs from both goals, being a compromisebetween them (Kaptelinin 1996: 53). Designers speak of affordances of

the tool, which invite certain kinds of interaction (Norman 1988, Carroll1997). Experienced users in this study seemed more aware of the toolsgoals than beginners. Novices tended to assume that the tools were neutral,whereas experienced users were aware of influences, but expected a goodresearcher to be able to control them.

As previously stated, the participants consistently used the metaphor ofQDA software as a tool. Their examplesfile cabinets, calendars,checkbooks, scissors and gluedemonstrate a general tendency that Ihave noticed: when the word tool is used in conjunction with software,

small tools seem to come to mind first. More general metaphors includehammers, saws, and other woodworking equipmenthand tools that theuser holds and guides. However, not all tools are so small and easilycontrolled, and the more power that tools have, the more damage they cando if misused. A table saw can cut more wood than a handsaw (and do sowith great accuracy), but fingers are also at risk.

A table saw represents a mid-sized tool: no longer a hand tool, but stillon human scale. At the next level are bulldozers and cranes: huge tools thatsurround the operator, and that can wreak havoc if misused. In theseexamples, each tool is not only larger than the last: it is also more powerful,requires more skill and safety training on the part of the operator, and cando more damage if misdirected. (Note that this categorization does notimply that powerful tools are only appropriate for larger projectstablesaws can be used for very fine cutsmerely that their effects are broader.)

In thinking of cognitive tools such as QDA programs, we need toconsider the scale of the tool in terms of its power. This form of scale affectsboth the tools utility in the hands of a skilled user and its danger in the

hands of the unskilled. Most of the advanced users of NUD*IST in thisstudy approached the program with confidence tempered by respect, whichsuggests a relatively high level of power.

Tools vary not only in terms of power, but in their range of functions.Some tools have only one or two functions, which are readily apparent: ahammer is for pounding, a saw for cutting. Other tools are more multi-purpose. The wider the scope of the tool, the less obvious any one use tendsto be. A table saw that converts to a router offers flexibilityif the userknows what a router does, and how to configure the saw to provide that

function. Thus, one aspect of tool use involves identifying what functionsare available, when to use themand when to choose a different tool



10/15

created an explicit invitation to code in a non-hierarchical manner, thoughsome experienced users had recognized that possibility prior to theirintroduction.

As mentioned earlier, the skill of the user is another aspect of tool use.

Development of skill usually requires practice with a specific tool. A mastercarpenter can do more with a lathe than a new user, because he or sheknows how to work effectively with it. Similarly, some NUD*IST userscreated very sophisticated command files and searches as their under-standing of those tools developed. Such development of skill also requiresongoing learning. The users who were least satisfied with their use of theprogram were those who had reached a plateau in terms of their skill level,and didnt have time to explore the program further.

Finallyand perhaps most importantlythe goals of the user affect

tool use. One reason master carpenters achieve more than the average useris simply that they understand what good work looks like. Experiencedresearchers, like those in this study, are more likely to have pre-existinggoals based in qualitative research standards, and to be able to set new goalsusing opportunities offered by the software. The participants generallyrealized that their previous experience aided them in this respect. Severalwere very worried about novice users who lacked previous researchexperience because they dont know how to approach it. Such novice usersclearly are at a disadvantage in terms of establishing high-level goals, andmay be especially prone to being led by the softwareor by their ownmisunderstandings of what theyre doing.

In relation to computer-based learning environments, Hannafin (1999)offers a preliminary typology of functions related to cognitive tools: seeking,collecting, organizing, generating, processing, and communicating. Notethat features and functions are not necessarily congruent: one feature maybe employed for various functions, and different features can serve the samefunction. For example, in NUD*IST, the search function may be used for

exploring themes (seeking), or for collecting and organizing relatedinstances. Conversely, multiple features can be used for organizingcommand files, searches, cut-and-paste within the tree index.

Users must determine appropriate configurations and uses of toolsbased on their goals, recognizing that some functions will be superfluous inany given situation. Hannafins preliminary typology may help usersarticulate their immediate goals more clearly. However, high-level goalarticulation must be the function of discussion within the qualitativecommunity. Software developers and users of QDA programs have

complained that the programs have been condemned for flaws that moreproperly apply to the methods themselves, and called for a more vigorous



11/15

are resource recognition (awareness of the tools capabilities), ability to usethose resources (skill), and goal identification. All three areas are fosteredwithin a reflective attitude.

No particular sequence is attached to these three requirements. Goalsoften drive tool selection and skill development, but sometimes thecapabilities of the tool suggest new goals, or new ways of meeting existinggoals. The level of natural alignment between user goals and identifiableresources may define the fit with your thinking that participants describedas perceiving in different programs.

Tool advocates frequently stress that tools merely carry out peoplesintentions. This view is most clearly typified in its extreme by slogans suchas guns dont kill people, people kill people. However, a tool generally

does not correct for misdirected intentions or incompetent use. The morecomplex and powerful the tool, the more the skill and intent of the userbecome an issue. These considerations have implications for thetrustworthiness of qualitative research.

Issues of trustworthiness

In qualitative research, the researcher has historically been considered the

primary tool for research (Merriam 1998). In some texts, the implicationseems almost that using tools taints the research in some way. However,

Figure 1. Expertise with cognitive tools.



12/15

In this study, some participants evaluated other researchers credibilityin part on how well they understood the software they were using. Even theusers who felt that their own work was actually more valid with thesoftware were unwilling to extend that assumption to other research that

used the same software, unless they had some evidence of researchercompetence with the software.Participants in this study were also concerned about people attempting

complex studies with insufficient skillsboth technical and conceptualbecause their expectations of the software were unrealistic. For example,they observed that some researchers trained in quantitative methodslearned the software without first familiarizing themselves with qualitativeconcepts, transferring their quantitative standards (for example, aboutsample sizes) to their qualitative projects.

Thus, goal orientation and ability to use resources (see figure 1) seemto be aspects of expertise especially related to trustworthinessessentially,are they doing the right thing? and are they doing that thing right?(Resource recognition appears to be less of an issue, though it certainlyaffects working efficiency.) Advanced participants insisted that researchconducted with software should be held to the same standards ofmethodological description required of manual studies: clear goal articula-tion, supported by a description of methods used, the justification for thosechoices, and explicit links between the data and the findings. This suggeststhe importance of methodological description may be increased rather thandiminished by the use of softwareand analyzed with program x does notconstitute a description.

In addition to the repeated concerns about novice users who didntunderstand qualitative research, participants believed that researchevaluators (editors, grant providers, and so on) should guard againstassumptions that software use automatically confers credibilityanassumption that they felt non-users or novices were especially prone to

make. They also suggested that misconceptions of non-users and new usersin positions of power (for example, grant providers) were a cause forconcern. Such misconceptions can create unrealistic or unreasonableexpectations that may deeply influence the practice of qualitative research.

Conclusions

This paper outlines three levels of distance highlighted when working

with qualitative data programs. First, the tactile-digital divide describesthe initial shift between working with paper-based materials and electronic



13/15

Third, the metacognitive shift addresses the issue of distance from themechanics and implications of data handling methods. Skilled usersdisplayed a strong metacognitive awareness of their own program use,and seemed to feel the software extended their own thinking. However,

their very awareness highlighted the potential for errors caused byunthinking or inept application of program functions. Such errors are aconcern for new or inexperienced users of the software, especially thosethat lack prior experience that would assist them in goal orientation.

Reflections on the tool metaphor were then used to highlight aspectsof expertise with cognitive tools such as QDA software: goal articulation,resource recognition, and ability to use resources. The final sectioncontained a brief discussion on issues of trustworthiness associated with theuse of tools in qualitative research. Experienced users were most aware of

potential influences of the software. They expected a reflective user to beable to control those influences, and were concerned over unthinkingapplication of software functions. These findings have implications bothfor helping new users and for evaluating research conducted with QDAsoftware: researchers at all levels must guard against the assumption thatlearning qualitative software is equivalent to learning qualitative research.

At all three levels, the balance between closeness and distance isinstructive. The tactile-digital divide forces consideration of what doescloseness to data mean? The coding trap highlights the value of analyticaldistance to balance closeness to data, and the value of purposefully movingbetween closeness and distance. The metacognitive shift achievescloseness to the powerful ways that software can transform data, but itachieves that closeness through reflective self-monitoringa form ofdistance, in that researchers step back to look at processes and decisions.As QDA software makes aspects of closeness more manageable, itchallenges us to re-consider where and when closeness is of value, andwhere and when we need a little distance to provide perspective.

References

Agar, M. (1991) The right brain strikes back. In N.G. Fielding and R.M. Lee (eds) Using Computers in

Qualitative Research(London: Sage Publications), pp. 181 194.

Bogdan, R.C. and Biklen, S.K. (1992)Qualitative Research for Education: An Introduction to Theory and

Methods, second edition (Boston: Allyn and Bacon).

Carroll, J.M. (1997) Human-computer interaction: psychology as a science of design. International

Journal of Human-Computer Studies, 4 6, 501 522.

Fielding, N. and Lee, R. (1996) Diffusion of a methodological innovation: CAQDAS in the UK.

Current Sociology, 44, 242 258.

Fielding, N.G. and Lee, R.M. (1998) Computer Analysis and Qualitative Research (London: Sage).



14/15

Kaptelinin, V. (1996) Computer mediated activity: Functional organs in social and developmental

contexts. In B.A. Nardi (ed.) Context and Consciousness: Activity Theory and Human-Computer

Interaction (Cambridge, MA: MIT Press), pp. 45 68.

Kelle, U. (1997) Theory building in qualitative research and computer programs for the management of

textual data.Sociological Research Online,2,5http://www.socreasonline.org.uk.socresonline/s/s/

1.html4.Kuutti, K. (1996) Activity theory as a potential framework f or human-computer interaction research. In

B.A. Nardi (ed.) Context and Consciousness: Activity Theory and Human-Computer Interaction

(Cambridge, MA: MIT Press), pp. 17 44.

Lofland, J. and Lofland, L.H. (1995) Analysing Social Settings: A Guide to Qualitative Observation and

Analysis, third edition (San Francisco: Wadsworth Publishing Co).

Mangabeira, W. (1996) CAQDAS and its diffusion across four countries: national specificities and

common themes. Current Sociology, 44, 191 205.

Merriam, S.B. (1998) Qualitative Research and Case Study Applications in Education, second edition

(San Francisco: Jossey-Bass).

Norman, D. (1988) The Design of Everyday Things (New York, NY: Currency Doubleday).Norman, D.A. (1993)Things that Make us Smart: Defending Human Attributes in the Age of the Machine

(Reading, Mass: Addison-Wesley Publishing Company).

Richards, L. (1998) Closeness to data: the changing goals of qualitative data handling. Qualitative

Health Research, 8, 319 328.

Richards, L. (1999) Data alive! The thinking behind NVivo. Qualitative Health Research, 9(3), 412

428.

Seidel, J. (1991). Method and madness in the application of computer technology to qualitative data

analysis. In N.G. Fielding and R.M. Lee (eds)Using Computers in Qualitative Research(London:

Sage Publications), pp. 107 116.

Tesch, R. (1990) Qualitative Research: Analysis Types and Software Tools (London: Falmer Press).

Weitzman, E.A. and Miles, M.B. (1995) Computer Programs for Qualitative Data Analysis: A Software

Sourcebook(Newbury Park: Sage Publications).



15/15

Documents

Going the Distance in Caqdas_2002