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164 MAKING SENSE OF QUALITATIVE DATA

Suggestions for Further Reading

Denzin, N. K. (1992). S)?lIbo/ic i//tcraoionism and cultura/ studics. Oxford, UK: Blackwell.

AII imponallt cxamplc oJ contcntporary illterpretaríoll and theory ill a tradiríoll thatIras informtd nwd/ ethnographic rescarch.

Geertz, C. (1973). Thc interpretation oJ cuiturcs. New York: Basic Books,Geertz, C. (1983). Loca/ kllowlcdgc: Futther eHa)'s ill intcrptctivc anthropology. Nc'" York:

Basic Books. .

Two vo/umes in wlrich Gecrtz explores and documC1Jrs thc illlerprcril'e 1I'0rk of alltlrro·p%gical reasoning.

Gubrium, J. (1988). Alla/yzillg fidd rcality. Newbury Park, eA: Sage.

Gives cxccllent insights into proecsStl oJ [ormal anolysis from qua/itatil'C ficldwork,based 011 rhc author's cxrcnsívc fie/d rcsearch in organizational scttings.

Harnrnersley, M. (J 989). The dilernma oJ qua/itative lIIethod: Hcrbcrt Blunicr alla tlu:Chieago tradition. Londan: Routledge and Keg an Paul.

Examincs sornc of rhe rccurrcnt cpisrelll%gica/ problems and thcorcticat rOOIl oJctll/lography Olla interprctive sociology.

Stanley, L., & 'Vise, S. (1993). Breakill!: 011' ngaill: Femi"i.<' o"r%!:)' and rpiHem%g)'.London: Routledge and Kegan Paul.

Exp/icir/)' /illb a [cminist pcrspcctivc ",it// intcrprctivc approaclles to the sociotogvo]cvcryday /iJe.

Strauss. A. L. (1995). Notes on the naturc and developrnent of gcneral theories. Qlla/irari\'e1I1ljui')', 1(1),7·18.

A valuabie discussion oJ rhe rc1lltioll51ripbC(wcell tlicotics oJ di!Jere"t 50rr5 olld grouJ].dedrhco71=illg. .

Complementar)! Strategies ofComputer-Aidrrl Analysis

Using Cornputers

Many of the analytic strategies we have described can be supportedthrough the use of computer software. The fact that we have not com-mented specifically on such software does not reflect a Luddite rejectionof information technology on our parto On the contr arv, we believe thatcomputing can be a key featuie of contemporary qualitative data analy-siso V/e are. convinced, however, that it is important to identify therelevant analytic strategies before lurning to the cornputer for analyticsupport. It is important to recogniz e th at contempor ary software can beused to help implement and develo p al! the analytic apprcaches th at wehave outlined.

The structure of this chapter r eflects the developmenl of the bOOK asa whole. \Ve will discuss the use of computers lo create and stor equalitative data and for the general management ofthe resear ch process.\Ve wil! describe the use of software in the various tasks associated withcoding data and the use of codes lo retrieve and sort data. Although

165 \

166 MAKING SENSE OF QUALlTATIVE DATA 167

software is now being used most commonly for the cocling and retrieval. of data segrnents, that is by no means its only applicatíon to qualitativedata analysis. Appropriate software also can help us to examine textualand sernantic featur es of our data, aiding in the construction of vocabu-laries, foIk taxonornies, and narrative form and content. V·le also look atways in which cornputers can help U5 to visualize and display our ideasand analyses. This leads us to consider how computer applications canhelp in the intellectual tasks of developing theoretical ideas.

In the course of this chapter, then, we comment on the use ofcomputers in the development of stralegic approaches lo qualitative data.analysis. We write of specific software to illustrate the more generalissues. It is not our intent to provide a systematic and comprehensivereview of the entire field, still less to comment on all the availablesoftware. Fortunately, ther e are other sources to which the reader can bereferred (e.g., Tesch, 1990; Weítzman & Miles. 1995). We will refer toother publications that deal in' greater depth with specific applications.(See al so our suggestions for further reading at the end of the chapter.)

\Ve reiterate that no single software package can be made to perforrnqualitative data analysis in and ofitself. The appropriate use of softwaredepends on appreciation of the kind of data being analyzed and of theanalytic purchase the researcher wants to obtain on those data. It is, webelieve, vitally important that researchers recognj,ze the diversiry of

. approaches that can be facilitated "a cornputer-aided qualitative dataanalysis. As Lonkila (1995) suggests, ther e is a danger in the current useof software-e-reflecting in part the r elative prominence of sorne pfÓ-grarns, rather than an intrinsic bias in the technology-that reflects aconvergence toward one dominant analytic mode. Such convergence isnot necessary, and it is certainly not necessary lo endorse a versión oftechnologícal determinism in order to exploit the value of contemporarycomputing strategies. Indeed, blind faith in the technolcgy undoubtedlywould restrict data analysis and methodological reflection.

Sorne indicatíon of the functíons and variery of applications of softwareis índícated bythe list provided in Miles and Huberman (1994, p. 44):

1. Making notes in th e field2. Writing up or transcribing fieldnores3. Editing: correcting, exrending, or revising fieldnotes

Str at egies of Ccrnputer-Aided Analysis

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4. Ca~ing: ~lta:hing key words or tags to segrnents af text and making thernavailable far inspection

5. Storage: keeping text in an arganized database

6. Search and retrieval: locating relevant segrnents of text and rnakinc thernavailable for inspection o

7. Data "linking": connecting relcvant data segrnents and forrqing categories,clusrers, or nerworks of informatian

8. Mernoing: writing reflective comrnemaries on sorne aspect of the data as abasis for deeper analysis

9. Content analysis: counting fr equencies, sequences, or locarions of wor ds andphr ases

10. Data display: placing selected or r educe d data in a condensed, organizedformat, such as a matrix or nerwork, for inspecrion

11. CancJusian drawing and verification: aiding the analyst lo inierpr et dis-played data and to test or canfirm findings

12. TheolJ: building: developing systernatic, conceptually cohererit explanationsof findings: tesring hypotheses .

13. Graphicm~ppin¡f. crcat ing diagrarns iha: depict findings or theories14. Preparing interim and final rcports

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In the rcmainder of this chapier , we will touch on most ofthese. \'Vewill not do so in a totally cornprehensive fashion. To do 50 wculddemand yet another .volurne, and there are other sources that pr ovidemuch more extensive accounts than this one chaprer (see the sugge stionsfor further reading at the end ofthe chapter). ~

In constructing the rernainder of this chapter, we have followed th egeneral logíc of the book as a whole. \Ve depart from our previouspracrice of illustrating particular methodological stralegies and tacticswith our own data. \'\'e rnake cross-references back lo the relevantchapters where we did so when we discuss particular methodologicaltasks. To provide detailed ernpirical exarnples in sufficient detail todernonstrate all rhe various software applications would demand a muchlonge~ tr eatrnent than is possible her e. It is, for exarnple, worth drawingatterition to the length of a work such as Weitzman and ¡'viiles (1995),which pr ovides a systernatic overview of the software wirh no attempt towork through analyses in detail and runs to more than 300 double-columned, large-forrnat pages. Moreover, the exercise of analvz inc thesam e data vía complementar)' programs already has becn ~ubli~hed(V/eaver & Atkinson, 1994, 1996).

168 MAKING SENSE OF QUALITATIVE DATA

Creating and Managing Data

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Most of the data used in qualitative research are textual, derived fromfieldnores, transcribed interviews, transcriptions of naturally occurringaction, documents, and the like. The first task for which the computer isperfectly suited, is the preparation of such textual data. It was not Jongago that field researchers reJied on handwrirten notes or rypewrirtenmaterials, By contrast, it is now routine in academic life to use wordprocessors to create and store files oftextual materials.lt therefore makessense for researchers to exploit the taken-for-granted technology andbase much of their work on text files created with word processors. As'Weitzman and Miles (1995, p, 11) say ofword processors:

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They go on to note that the porential of word processors for analyticpurposes is restricted, and it is riecessary to go frorn thc word proccssorto more specialized prograrñs iJ! order ro dpJore those fijes in moreproductive ways. This is not 3 view iliat is universally endorsed. Someresearchers have come to an informed decision that the facilities andfunctions of the most advanced conternporary word processors can carryout most of the analytic tasks that the practical researcher needs. Thisisa conclusion reached b), StanJey and Temple (1996). They tried out anurnber of specialized programs for the analysis of qualitative data sets,including materials from the Mass Observation archive, and cornpar edthem with Word for \Vindows. (Mass Observation was an exiensiveproject in the United Kingdom in which large nurnbers of people keptdiaries ~nd created other kinds of data.) Stanley and Temple (1996,p. 167) exarnined The Ethnograph, NUD.IST, askS.1\J\1, ETHNO, andInfoSelect as examples of specialized software and concluded that

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Str ate gies of Computer-Aided Analys is 169

~ good wordprocessing package \ViIIbe sufficient 10 the analysi s required, or.if n~[, rhe resca rcher would be best advised lO use a dedicated package forspecific research tasks. The seduction of the dedicaied packages is thatbeca use they have capacities, these are then used !O rhe limits possible.

-. This is an extrernely sensible piece of general advice in that anyresearcher should use to their full extent whatever r esources are availablebefore seeking out more specialized, esoteric research tools. Many prac-titioners would dissent from Stanley and Temple. As we will illustrate inthe remainder of this chapter, there is a range of sofrwarc available thatallows one to use textual and other materials in useful ways, It is valuableto keep in mind, however, that such software facilitates complementaryanalytic strategies. It is vital to identify ones analytic goals and interestsand to use computer software accordingly. There is no one softwarepackage that will do the analysis in itself.

The analytic software that deals with textual rn aterials rncstly importsdata that are created using a standard word processor. It therefore makessense to translate textual materials, such as fieldnotes or interview tran-scripts, into text files. Tliere are, moreover, practical management tasksthat can be accomplished in the normal desktop computing envir on-mento Data fijes can be sror ed, copied, shared, and transferred ra pidlyand efficieritly on disk. Normal routines of file management can be usedto organize data sets into directories (e.g., for different rescarch sites,fieldwork periods, sarnples, etc.). Fisher (1995) points out that man)'standard features of personal computers can be used to advantage in thegeneral organization and managemenl of qualirarive research. In thisrespect, qualitative rescarch is no different from an)' other kind ofscholarly inquiry. :;

Fisher pr ovides a useful review of how desktcp computing can pro-mote research management. Relevant tasks include maintenance ofdatabases relating to research sites, informanrs, samples, research con-tacts, or gatekeepers, and 50 on; electronic communication as a rneansof collaboration with research team mernbers and orhers: managementof references and bibliographies; preparation of materials for distribu-tion and publication; and preparation of materials for presentatíon tolive audiences. Th e use of such resources for the rnanaaern ent of re-search, maintenance of files and recor ds, and production of researchourput is now part of the eraft knowledge of most researchers and can

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170 MAKING SENSE OF QUALlTATIVE DATA

be used to great advantage in parallel with software that is more specifi-cally tied to the work of qualitative data analysis. As Miles and H uberman(1994, p. 45) remind us, the management of qualitative data involvesmore than the preparation of raw text from fieldnotes or transcripts.Attention must be given to formatting those materials, such as nurnber-ing lines, paragraphs, and pages; cross-referencing and indexing; includ-ing contextual and other headings; and including surnmaries or abstractsoflonger data documents. These are all significant tasks in data organi-zation and management. They are al! necessary preliminaries to detailedanalysis, and many are part of broader sets of analytic procedures. AlIcan be enhanced or aided by the use of cornputer software.

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Coding and Retríeving Data

Following the overall structure of the previous chapters, we turn tothe use of computer software for coding qualitative, textual data. Gen-erically, such software irnplernents what is known as a code-and-r ctricvestrategy. At its simplest, this approach recapitulates the tasks of manualcoding and searching. On the whole, however, the software allows-cvcnencourages-the analyst to do more with that strategfthan manualtechniques would support.

Code-and-retrieve programs are designed to allow the analyst to marksegments of data by attaching code words to those segments, and thento search the data, retrieving and collecting all segments identified by thesarne code or by some combination of code words. As \Veitzman andMiles (1995, p. 17) surnmarize this software strategy, the progr;:¡ms "takeover the kinds of marking up, cutting, sorting, reorganizing, and collect-ing tasks qualitative researchers used to do with scissors and paper andnote cards."

It is arguable that, in terms of general methodology as opposed topractical data management, the code-and-r etrieve strategy offers nogreat conceptual advance over manual data sorting. Many programs usethat strategy as their major mode of data handling. They include TheEthnograph, QUALPRO, Kwalitan, Martin, HyperQual2, NUD.lST, andATLAS/ti. Those programs do more than coding alone, and there areothers (some ofwhich we mention in a later section) that are based onsome notion of coding as well but add further analytic functions (see

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Weitzman & Miles, 1995, pp. 148-203)0 The Ethnograph and NUD.JSTare probably the most widely used and best known ofthe programs thatare predicated on a coding strategyo That does not mean necessarily thatthey are the best-indeed, there is no single best software-but it reflectsin part wh en they entered the rnarket and the general level of acquain-tance ofthe research community.

The computer-based approachcs of this sort depend on procedurcsfor coding the text (such as interview transcripts, fieldnotes, transcribedrecordings, documents). This rneans marking the text in order to tagparticular chunks or segments of that ten. Code words are attached todiscrete stretches of data. The purpose ofthe software is, at root, twofold.First, it facilitates the attachment of those codes to the strips of data.Second, it allows the researcher lo retrieve all instances in the data thatshare acode. Such code-and-retrieve approaches are exernplified inprograms such as The Ethnograph, one ofthe most widely disseminatedand used applications.

The underlying logic of coding and searching for coded segmentsdiffers little, if at all, from that of manual techniques. Ther e is no greatconceptual advance over the indexing oftypcd or evcn manuscript notesand transcripts or of marking thern physically with code words, coloredinks, and the like. In practice, though, the cornputer can offer rnanyadvantages. Th e specd and comprehensiveness of searches is an un-doubted bcnefit: The cornputer does not search the data filcs until itcomes up with the first exarnple that will "do" to illustrate an argument,nor will it stop after it has found only one or a few apposite quotes orvignertcs. As we indicate later in the chapter, the capacity for compre-hensive scarching is often a valuable part of hypothesis testing in thecourse of data analysis. The software has additional merits that definitelymark advanc-s on the practical value of manual coding and searching:It can cope with multiple and overlapping codes, and it can conductmultiple searches, using more than one code word sirnultaneously.Software such as The Ethnograph allows the analyst to combine codewor ds in order to facilitate cornplex searches. In other words, the analystcan use the operators AND, OR, and N'OT to combine code wor ds incomplex searches. The software can han dle ver)' large numbers of cod-ings. In purely mechanical ter ms, therefore, the computer can helpimplernent more compr ehensive and more cornplex searching tasks thancan be performed by manual techniques.

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172 MAKING SENSE OF QUALlTATIVE DATA

Many software packages allow the researcher to do more than code dataSoftware such as The Ethn ograph , Kwalitan, and ~UD .IST permits the userto do things such as attaching analytic or other mernoranda to specificpoints in the text, The aim is ,to incorpórate rnany of the key tasks ofgrounded theory strategies. (as discussed in Chapter 6) within the sO,ftwareapplications. Throughout the process of analyzing data and refle~mg onfieldwork, the researcher needs to maintain analytic, rnethcdological, andother memoranda. There is, therefore, every advantage in incorporatingsuch memo writing within the use of computing software.

There is, therefore, a close relationship berween the processes ofcoding and the use of computers. It should be evident that coding datafor use with computing programs and the retrieval of coded segments oftext is not; inour view, analysis, At root, it is a way of organizing data in

, order 'to' search thern. It is a useful part of the research process. Anyon enow embarking on a sustained piece of qualitative research shouldseriously consider the potential value of cornputer-aided storage andretrieval. Such astrategy of data organization should be thought aboutat an e~rly stage in the research planning,'not tacked on at the cnd ofthedata collection phase of fieldwork. Qualitative rcsearch is not enhancedif researchers decide they will take their data and "put it through thecomputer,".as if that substituted f~r t~ iniellectual work of analysis.

Language, Meaning, and Narrative

Conternporary uses of computer software do not Iollow pr ecisely theanalytic perspectives we outlined in Chapters 3 and 4. Nevertheless, thereare softwareapplications that can readily facilitate the detailed explora-tion of language, even of narrative structures. The former are mcstlygeneral programs for the analysis of text rather than specific ones toqualitative research, as are the ccde-and-retrieve applications pr eviouslyoutlined.

As we have indicated, it is one of the strengths of computer-assistedanalysis that it facilitates the rapid and comprehensive scrutiny oflargevolumes of textual data. Such searching capacities are characteristic ofprograms that can be used to investigate aspects oflanguage and :nean-ing. Generic software rypes have been developed and widely rrn ple-mented for the systematic searching of textual data sets. Th eir practica!

Strategies of Computer-Aided An alysis 173

uses are various, including indexing large volumes of documentarysources, searching them for particular terms (such as proper names), orlocating specific sequences of words and characters. Such programs,called text re tr ievers, are described thus by \'\Teitzman and Miles (J 995,p. 17):

They specialize in finding al! the instanccs of words, phrases (or othercharacter strings), and combinations ofthese you are inierested in locating,in one or several fijes. They can often do intcres¡ing cperations with whatthey find, like marking or sorring the found text int o ne w fijes. or Jinkingannotations and memos 10 the original data, or launching new processes orother software p~ckages lo work on the data.

It is often useful to use such software to searchextensively with in adata set of interview transcripts, fieldnotes, transcribed inter actioris, ordocumentar}' sour ces. A comparison with coding ma)' help lo point outthe value of this approach. V/hen we code data, we lag segrnents of datawith terms that r eprescnt analytic themes. \Ve ma)' find ourselves usingas code words sh6rthand terms that stand for brcad sociological oranthropological concepts. There is no necessJI)' relationship bctwcenthose an alytic concepts and the terrns used by the original social actors.In the anthr opology data, we rnight, for example, code man)' segmcntsofinterview data as being about iso/alion. That code might cover expres-sions of personal and intellectual isolaticn 3S pan of the cxp crience ofthe research studcnt. It goes withoyt sa)'ing that the interview respon-dents wil! not al! have described their own cxper iences and those ofothers only in terrns of th e wor d "isola tion." They wil! have used a widevariery of words and phrases to capture the range of cmotions andactions that rnay be captured by the ea tegory isotation (such as Joneliness.alorie, isolation, nobcdy to turn tolo Ther e are man)' good reaso n s loexplore the kinds of terms-the vocabularies and folk terms-thatinformants actually use. Unless one is going to treat every word as acodeword and undertake the laborious work of replicating them as codes ina program such as The Ethnograph, then a text retriever of sorne son canbe a vel)' useful tool,

11 is possible to construct a comprehensive thesaurus (a word list ordictionary) for the entire data ser, from which can be identifie d itcms ofvocabulary for further, more detailed, exarn ination. In addition. words

174 MAKING SENSE OF QUALITATIVE DATA

can be sorted into concordances, that is, retrievcd and displayed in theirimmediate coritext. \Vords can be counted and sorted into relativefrequencies. In thernselves, these rypes of textual searching are notconceptually powerful or illuminating. Thev can be used for elemental)'types of content analysis, and programs of this sort have been recorn-mended for such work. They are, however, more usefully thought of interms of potential aids in more imaginative and creative kinds of analvticactiviry, As Weaver and Atkinson (1994, p. 77) write about the generalapproach, these programs

enable researcherslO explore their data dir ectly, b), searching for le xi cal it ernsand analysing the lexical content of fieidnotes, interview transcripts, and an)'other documents of interest. By prompting the program to produce a 1'0:

cabulary list, we can examine the vocabularies displayed by respondents, andthercby gain insights into how peoplc articulare phcnomena, or throughIanguage make sense of their 'cveryday lives, Similarly, wc rnay find that acertain wor d dorninatcs int erviews with a ccrtain person, or certain socialencounters, whieh may he analytically significant. Also, ihese prograrnsenable us to conduet searches not only on pa r ticular wor ds. bu: nlso combi-natioris of wor ds. ln rhese searchcs, r esearchers can specify conditions fort ext to be r etrieved, regarding the proxirnity of one word !O anorher, by usinga variery of Boolean operators in a search string.

...In other words, cornplex searches can be performed (as in code-and-

rctrieve operations) by combining terms with combinations of AND,OR, and NOT (the "Boolcan opcrator s" rcferred to above) to deveiop,

for exarnple, syncnym lists.Programs often help in the exploration of texts by allowing "wild

card" characters. A search ma)' be undertakcn using the root of the wordplus the wild card character, thus capturing al! the Iorrns of the term inquestion. To give a concrete example, in searching the data about PhDstudents and their advisers, we might well fi~d oursclves seárching outall the instances where students had mentioned their supervisor, theprocess of supervisión, 01' related terrns, such as supervising. \-Ve couldfind every instance matching the sequcnce SUPERVIS*.. In the same way, we saw in Chapter 4 how we might start to buil'd up

an analysis of a domain such as thc fidd. Civen the irnportance oífieldwork to PhD research in anthropolcgy, we would expect to have acode for ir, using The Ethnograph or a similar programo That would not

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Strategics of Computer-Aided Anal)'sis 175

guarantee finding every reference to the field or its equivalerit. \Ve couldsearch the entire corpus of interviews for FIELD*, which would findevery instance of field, fieldwork, fieldnotes, and so on. A comprehensiveand systematic search of that sort could well be a valuable prelirninaryto the thorough examination of anthropologists' own folk terminolo'-gles. B)' examining the contcxts in which such terms are used, we wouldstart to examine the range of connotations associared with si.ch kcyterms, the kinds of imagery and rnetaphors associated with them andthe distribution of such terrns among the informants. Th~ capacit), ofsoftware to retrieve selected words or strings of words in context is a vitalfunction in performing such analytic work. Again, one must emphasizethat the real analytic work is created by the analyst, using (he outpul ofsuch ~ search as the raw materials. Software will not itself complete adornain analysis, but it can be an enorrnously useful tool in doing thegroundwork for such a task. Tesch (1990, p. 182) highlighted (he valueof such lexical searching:

Evcn researchcrs \~'ho norrnally deal wirh interpretational analysis, in whichthey handlc rncaningfu] chunks of iext raihcr than wcrds, could find sorneof these pr ograrns' options helpful. For instance, a resenr cher mav noticc thatJ cerra in concept is nlludcd 10 in his/hcr data. As a validitv check s/he couldcr eatc J list of synonyrns and phrascs th at capt ur e that conccpt, and explorewhether, and hOI\' frequently, it wa s dir ectly addr essed bv the participanis inhis/her research. ' - .

\Veaver and Atkinson (J 994) are in no doubt that text-r etr ieval, orlexical-searching, strategies are an important element in the rcsearchprocess. They sugges: that th~se are especially useful tools during early,exploraiory scrutiny ofthe data. They al so suggest thar the results of suchexaminar.ion of the data can yield unpredictable results when comparedwith coding strategies, because codings reflect the analvsi's own deci-sions, while lexical searching can be much more open ended. Text-r etrieva) strategies are not dependent 0:1 the kind oflabor-intensive workinvolved in adding codes, so they can be used for more speculative,freewheeling kinds of explor ation al lcw cost. There are m.an)' progr arnsthat perform elements of text retrieval and can be used prcductively forqualitative data anaiysis. \Veitzman and Miles (1995) review Meta-morph, Orbis, Sonar Professional, The Text Collector, \Vord Cruncher,

176 MAKING SE~SE OF QUALlTATIVE DATA

and Z)'INDEX. Weaver and Atkinson (1994) based their analysis on

FY13000 Plus, an older programoThere are very few programs that wilJ help direct1y with the analysis

of narrative structures. The software generally is more valuable for theorganization and r etrieval of content than the discovery of :orm orstructure. One should pay attention, however, to orie especially mterest-ing program, developed by Heise, called ETHNO. This program ~elpsin the conduct of narrative analysis. The actual use of the program 15notaJways easy to follow, but it holds out intriguing possibiJi~ies f~r theformal anaJyses of narrative. The analytic approach 15not identical tothat advocated by Labov or Cortazzi (see our discussion in Chapter 3).ETHNO is a particularly interesting program that differs i.n style andpurpose from many that are more familiar to the c.om,;nuDlt)' of quali-tative researchers. 1t is designed prirnarily to deal with verbally definedevents," including narratives (written or spoken). The analyst mustdefine the events to be analyzed from within the narrative. The softwareis then usedto construct a model of the relationship among those

narrated events.The analyst must first identify the events rnánually. They are then

entered into the cornputer. and the pr~ram generates a diagram of theevents and their arrangement. The analysis is based on four principIes:(a) events have prerequisites, (b) an evcnt canno! occur until alJ of ~tsprerequisites have occurred, (e) the occurrence of a.n event uses. up IlS

prerequisites, and (d) an event is not repeated until the con.dlt10n,~tcreated are used up by sorne consequences (Corsaro &. Heise, 1990;

Mangabeira, 1995). The software uses these logi~al pr~nciples to con-struct the diagram of events and their interrelatlOnsh1ps. The analystenters the first narrated event on ser een, then enters subsequent eventsin turno At each new eve nt, ETHNO requires the user to respond tosimple prompts in order to specify the logical relationship betwe:n thetwo events. The researcher goes on entering events and respondmg tothe program's prompts. The program thus progressively builds up adiagram of the relationships implied within the narrat1v~ of events. Asthe structure is built up, the software draws on pr eviously enteredinformatíon and infers relationships that alr eady have been established.

Heise (1988) demonstrated the use of ETHNO in an analysis of thestory of Little Red Riding Hood. It clearly can be used to constructmodels of a wide variery of narratives. Companson of the models of

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Stratcgics of Computcr-Aided Anal)'sis 177

different narratives may reveal not only different events or narrativeelernents but also different relationships among the constituent parts.ETHNO establishes, as we have indicated, JogicaJ strucrures from nar-rated events and is not used to construct formal structur es such as thosewe described in Chapter 3. Nevenheless, in conjunction with those kindsof analytic models, ETHNO can be a useful tool for the repr esentaticnof narrative structures and for analysis of logical properties of thenarrated reconstructions of evenrs. For that reason, the analyst mayconstruct his or her own reconstruction of events (such as riruals) anduse ETHNO to tease out and compare structures of sequences of actionthat have been observed (cf. Corsaro &. Heise, 1990).

ETHNO also has functions relevant to domain analysis throughgeneral sernantics. It permits the analyst (or indeed, anindividual in-forrnant) to construct taxonornies of terrns through a series of structuredquestions. This can be an extrernely useful heuristic device. lf the re-searcher chooses to use the program to undertake a domain analysis,then ETHNO can help lo make explicit the knowledge that is beingrepresented and the relationships among the elements in the domain ortaxonorny. It is by no means necessar)' to subscribe to a strong versiónof semantic or semiotic analysis in arder to recognize the general heu-ristic value of such an approach.

Th eory Building an d Hypothesis Testing

The kind of representation that is generated b), ETHNO's taxonomicanalysis is facilitated, on 3 different scale, by several code-based pro-grams, among others. Programs such as NUD.IST and ATLAS/ti areexplicitly designed to encourage the researcher lo do more than urider-take the coding and fragmentation of the data. They encourage theanalyst lo build up systernatic relationships among the code categories.They are often referred to as having theory-building functions, althoughone should be careful not to irnply that "theory" can be constructedmechanistically through th e aggregation of cedes or ca tegories. Togetherwith other, similar, programs (such as Kwalitan), software such asl\'UD.IST is claimed lo support the gener aticn of grounded theory, AsRichar ds and Richards (1990, pp. 9-10) argue.

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178 MAKING SENSE OF QUALlTATIVE DATA

NUD.IST supports "grounded theory" research ... a rnethod that has li~1eto do with coding and retrieval of text segmenrs, bur a lot to do wirh catchingand interrogating meanings ernergent from data. "Coding" in that m~thodrefers to a very different process from the labelling of lines oftex.t for r etrieval.Rather it is about construction and exploration of new categories and pointsof view in the data, link.ing these lo rext.

At the heart of the theory-building procederes in NUD.IST is the factthat ail codes are arranged into hierarchically structured trees, In con-trast to the sirnplest systerns of coding, therefore, NUD.IST arrangescodes in relation to one another, with orders of generality or specificity.In working with the data, adding or rnodifying codes and coding schernes,

.one is therefore simultaneously rnodifying the structure of interrelatedcodeso The product of coding (in NUD.lST terminology, index.ing) thedata is not sirnply a rnechanism for searching and retrieving chunks ofdata; it is also the conceptual framework indicated by the index systernitself. The arr angernents of c~des into hierarchical relationships is notautomatic:The analyst must initially specify the relationship with othercodeso

It is this approach to the conceptual structure implicit in manyanalvses, and rendered explicit in NUD.IST, that makes this softwareattr;ctive to rnany actual or potential users. lt is not, however, the onlysoftware product that encourages the explicaiion of S"uch conceptuallinks and structures. ATLAS/ti and HyperRESEARCH, among others,support similar functions. ATLAS/ti, for exarnple, ~a~ a. numbe.~ offunctions that eneourage the analyst to create explicit links amongelements, such as codeso These and other anal ytic links (such as thoselinking passages in the original data) can be displayed graphically inATLAS/ti. Using the nerwork' editor, these graphieally represcnted rela-tionships can be modified on screen. The analyst can use the editingfunction as a heuristic device in exploring relationships among eatego-ries or conccpts; they can, for exarnple, be arranged into hierarehicalrelationships. In addition to having powerful and revealing ways ofsearehing text, therefore, ATLAS/ti is an especially useful tool for theanalyst who wishes to explícate and to visualize emergent patterns ofconcepts and the links among thern, In effect, as we indicate below, rt

can be argued that the ordcred set of codes and the relationships amongthem constitutes the researcher 's knowledge base. In other words, the

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Strategies of Computer-Aided Analysis 179

emergent framework of concepts and ideas is an ordered set of rela tiori-ships that parallels thea::latabase ofthe original texts.

It is, ofcourse, not neeessar)' to base the representation of conceptsand relationships on coding strategies. There are several generie pro-grarns that help one to develop and display conceptual schernes, sernan-tic networks, and the like. In other words, one can take the ideas derivedfrom other kinds of strategies-such as possible elernents for a doma inanal)'sis-and use'network-mapping software to build and display thosesemantic relationships. V·le have not used an)' of the relevant programsat Cardiff, and we do not cornment on th em any further here. Severalare described in detaibyWeitzman and Miles (1995, pp. 266-309), whoalso surnrnarize their general characteristics:

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You can se e your variables shown as nodes (typically rect3ngles or cllipses),linked lo other riodes by lines or arrows repr~senting specified rcbtionships(such as "bclorigs lo," "lcads to," "is a kind of"). The networks are not justcasually hand dr awn, but are real "sernantic nctworks" that develop frornyour data and your concepls (usually higher leve] codes), and ihc rcla tion-ships you see among thern. (Weitzman & Miles, 1995, p. 18)

Weitzman and Miles themselves review Inspiration, MECA, MetaDesign, and SemNet as repr esentatives of this software type. Such soft-ware clearly helps in th e developrn ent ofidcas,establish ing links betweenideas, mapping out possible organizing thernes, and so on. One of themajor strengths of software of this type is the strongl)' visual char acterof their representations. It is abundantly clear that qualitat ive dataanalysts often think most productively about their data through gr<Jphicdisplays of various sorts. In the.study or the serninar room, cne can drawon the ehalkboard or doodle on scrap paper to create conceptual linksand parterns. The eomputer now helps do sirnilar ly cr eativc work withOUT data and OUT ideas.

There is no doubt that the strategic use of software can help in thecrueial interaetions berween ideas and data. The irnportant issue here isnot simply the generation of analytic categories and concepts. There isusually a need lo systernatically check those ideas against the data. Theshuttling process berwee n ideas and data means that erriergent coricepts .and hypotheses must alwa)'s be checked, modified, abandoned. or de-vcloped. Forrnally, one ma)' choose 10 think of such a pr ocess as onc of

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180 MAKING SENSE OF QUALlTATIVE DATA

hypothesis exarnination and verification (Kelle, Sibert, Shelly, Hesse-Biber, & Huber, 1995). As those authors put it,

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11isin this area of qualitative hypolhesis examination and refinernent whcreresearchers can draw the greatest beriefits from computer aided nletllods forthe coding and retrieval of textual data.lf qualitative data were not or ganizedand structured, the search for evidence or counter-evidence would be apractically insurrnountable task: every time a rcsearcher c.xamined a certainhypothesis he or she would have to re-read severa] hundred, or severa]thousand transcript pages. This would make it very difficult to withstand theremptation lo "validare" theoretical concepts with some hastily gatheredquotations, thereby neglecting negative evidence contained elsewhcre in thedata. On the contrary, the use of storage-and-retrieval methods can go a longway towards helping lo avoid those dangers that are always prevalent inqualitative analysis due to the evcr-pres~nt data overload. (p. 107)

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Here it is claimed that the· capacities of compulcr software to helporganize and interrogate a large corpus of data exhausnvely may con-tribute directly lo the comprehensive tcsting and modificalion of hy-potheses that are grounded in those data.

Some uses of qualitarive analysis software may go further towar dhypothesis generation and testing. Sibert and Shclly (1995), for exarnple,describe the use of logic programming for the developrnent and testing ofhypotheses. Such programming involves use of computational methodsto produce formal propositions about the researcher's knowledge base(asrepresented in the codingscheme). In a similar vein, Hesse-Biber.oridDupuis (1995) commend the use of computer-aided techniques to testhypotheses. They describe an automaiic h)'pothcsis tcstet based on theidentification of co-occurring codeso A search procedure using Boolcanoperators (AND, OR, and NOT) perrnits the researcher to build upcomplex and precise searches of the data.

. Hesse-Biber and Dupuis base their discussion on their own developmentofthe program HyperRESEARCH, although the functions they describe areb)' no means confined to thatsoftware alone. The h)'pothesis-testing ele-ment of the software, in addition to more basic code-and-retrieve aspects,1S claimed to reside in the ability of the researcher to construct "if ... then"propositions and to test them by exploring the overlapping, nesting, orproximity of codes in the data. It must be born in mind-as the authorsthernselves acknowledge-that such an approach rests on the assumption

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Str ate gies of Computer-Aided Analysis 181

that the co-occurr ence or proximity of codes in the data set can be usedto infer relationships of analytic significance.. Several programs allow the researcher to examine co-occurrences of

codes, and the general strategy mar often prove to be a valuable heuristicdevice. lt is far less certain that it can be described strictly as a general.means for hypothesis testing. Given the inherently unpredictable struc-~ure of qualitative data, co-cccurrence or proximity does not necessarilyirnply an analytically significant relationship among categories. It is asshaky an assumption as one that assurnes greater significance of corn-monly occurring codeso Analytic significance is not guaranteed by fre-quency, nor is a relationship guaranteed by proxirniry. Nevenheless, ageneral heuristic value may be found for such methods for checking outideas and data, as part of the constant interplay between the two as thercsearch prccess unfolds. ' .

Hyp ertext and Hypermedia

We have tried to indicate in this chapter that computer programs canhelp the qualitative analyst to undertake important tasks of data man-agernent, retrieval, and analysis, Some of those computing strategiesessentially recapitulate the logic and the procedures of manual datahandling, as in th e simples! code-and-retrieve functions. Other strateciesbuild on those procedures but include functions that could not~ beaccommodated under 'manual methóds, such as use of Boolean opera-tors in multiple-code searches. Others r eadily exploit features of generalsoftware that are less dependent on manual procedures of data soningandmore directly dependent on the special characteristics of computersoftware.

V/e turn now lo onestrategy of analysis and representation that is,arguably, the most thoroughly grounded in conternpor ary informationtechnology. We venture to suggest that it provides the basis for ananalytic and representational str ategy that promises to make th e most ofcontemporary inforrnation technology. It is potentially most faithful tothe·representational flexibility and diversiry that best capture contcm-porary qualitative research (Weaver & Atkinson, 1994).

Hypertext is not an especially new idea, but it may preve to be onewhose time has come. In essence, its underlying ideas are fairly simple.

182 MAKING SENSE OF QUALITATIVE DATA

They are predicated on the view that the reader's relationship with agiven text (such as a literal"}' work or a work of reference) should notnecessarily be restructured to the linear reading of that text in. a prede-termined sequence. The hypertext approach is nonlinear, akin to brows-ing and foUowing up cross-references. Hypertext software allows a readerto follow, and indeed to create, diverse pathways through a collection oftextual materials. Hypertext applicatiOns thus support a much moreinteractive relationship between the text and its readers. Readers, in asense, become authors of their own reading; they are not sirnply thepassive recipients of a determínate textual formo

This approach has exciting possibilities for qualitative researchers.Many people working with qualiiative data-whether they use field-notes, interviews, oral history, or documentary sources-feel frustrated

. by the necessiry of irnposing a single linear order on those materials. ltis, after all, part of the raison 'd'étre of ethnographic and similar ap-proaches that the anthropologist, sociologist, historian, psychologist, orwhoever recognizes the complexity of social interrelatedness. \Ve recog-nize the overdeter~ination of culture, in that ther e are multiple, denselycoded influences among and between different dornains and institu-tions. It is part of the attraction of hypertext solutions that a sense ofdense interconnectedness is preservcd-enhanced, even.oLwhile linear-ity is discarded.

De)' (1995) commends the use of hypertext links, not least in theinterests of reducing the fragmentation of qualitative data (cf. Atkins;?J1>1992a). De)' also suggests that "technology has been used to enháncerather than transform traditional methods" (1995, p. 69). His ownprogram, Hypersoft, was developed to facilitate the establishment of"hyperlinks" between segments of data texto Dey (1995, p. 75) argues that"beca use we can link text segrnents, we can analyse data in ways notpreviously practicable. We can retain information about narrative andprocess." In other words, it is not necessary to recapitulate the physicaldisaggregation of the data that is characteristic of cut-and-paste ap-proaches and elementar¡' code-and-retrieve methods. De)' does not goon to draw out the fuJl potential of a true hypertext environm ent: 'Hedoes not discuss or ilJustrate the use of such a straregy for the ",riting andrepresentation of qualitative analysis more generalJy. \Ve suggest thatone of the most exciting possibilities of hyper text softwa re lies pr eciselyin its capacities to support novel forms of representation.

Strategies of Computer-Aided Analysis 183

The basic implementation of a hypertext application is fairly simple.lt is based on the idea of the "button," which marks a point in the text(or other data) at which various functions can be pcrformed. A linkbutton allows the user to go to another point in the data to make a suitablecross-reference or to pick up another instance of "th e sarne" occurrenceor a conceptually related instance. Such links join "nodes." The analystcan create dense webs or rietworks of such links, which can then be"navigatcd" in various cxploraticns of Ú1Cda la. '

By contrast, an cxpansian button allows the analyst to attach addi-tional text to the node. Activating a buttori can reveal, for exarnple, ananalytic memorandum about an actor or a given incident, or an expla-nation of a particular item of situated vocabulary. In working with hisor her corpus of data, therefore, the analyst can use hypertext applica-tions to create complicated linkages among differcnt items and canattach all sorts of explanations and other helpful materials to thern. Inaddition lo explanatory material or memos, we might also attach addi-tional rnaterials, such as carecr details of individual respondents, theirfamily tr ces, or dctails about their dornestic lives,

In principle, the range of such information that can be linked tospecific buttons IS limitless. If we were using such an approach with ouranthropology data, for exarnple, our procedures might include th e fol-lowing operatioris. \Ve would not have sepárate phases of data storage,retrieval, analysis, and writing up. Instcad, we would prepare the datasets of interviews, documents, and other representations together withOUT sociological cornrncntary in such a way as lo allow the rcader 10

navigate her or his unique pa thwavs through thern al!. Thc reader(working at a multimedia workstation rather than reading a book orjournal) th er efore would notbe constrained to inspcct only those ex-tracts from th e data that we had chosen for illustrative purposes. Byactivating links across al! the data files (created by us or by the reader, orboth), readers becorne analysts in thcir 0\';;' right. Furtherrnore, the fullpossibilities of hypertext and hyperrnedia mean that it is not too fancifulto think in the ncar future of hypertexr-based ethnograpbies in whichthe reader can activa te sound and visual playbacks, so that originaldocuments can be inspected in the context of the ethnographic analysis.One ma)' thus go beyond the purcly textual by wor king in a h)'pCrlllcdíaenvironrnent, so that stil! and rnoving irnages, sound, and other repr e-sentations can be included. When an inforrnant talks about ficld research,

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184 MAKING SENSE OF QUALITATIVE DATA

we might click a link button or an expansión button to bring in photo-graphs or mov:ing images of the field settings thernselves, together withth e voice of the informant describing his or her experiences. In the courseof descriptions of academic departments, we could draw in graphicrepresentations of their physical Iayout, lists of faculry members, gene-alogies of their leading lights over the generations, refer ences to andextracts from key publications by our inforrnants, and so on. Thesewould all in practice create ethical problems, but we are conccrned herewith the analytic and representationaf possibilities that are on offer, nottheir precise appli~ation. .

The work of analysis with a hypertext approach need never be com-plete. The analyst can go on creating links and adding inforrnarionindefinitely. There will always be practical limitations, of course, andthere are also limits placed by human cognitive capacities. Ther e is, forexarnple, the widely recognized possibility of becoming lost in "hyper-space" if the whole thing becomes too complicated and the user cannotget back lo where he or she starred, or cannot navigate lo whcre she orhe actually wants lo be. It is appar ent, however, that once the relevantIinkagcs and cxpansioris have béen s~t up, with ap~roFriate iniroductorymaterial and cornmentary, the resulting hypertext is a form of analysis.lt is not necessary to recast the whole tfung in lo a conventional, linear,printed text, Hypertext applicarions are authoring systems, often usedto develop and deliver instructional materials. The construction of ahypertext based on systematically ordered and suitably edited qualitativ; .data thus collapses the processes of analysis and writing, '

In other words, looked al from the other cnd of the process, a r eaderwould not settle down with a book as "the ethnography" or "the history"bUI instead would interact with data and arialytic cornmentary in aflexible andint~ractive wa)'. Reading through introductor)' and explana-tory text, for' exarnple, the reader-by clicking on a button-couldchoose tó examine relevant data in sorne detail, go to other exarnples ofthe same phenomenon, or examine extracts from relevant literatur e (afar more user-friendly process than the average literature reviewl). Heor she thus picks a path through a variegated collection of texts andeross-referenees. It is for this and similar reasoris that it is sometimesclaimed that hypertext approaches work with a postmodernist approachto tcxts, It is certainly the case that hypertext helps to preserve a scnse ofco mplcxity, intertextualit)', and nonlinearity.

Str are gies of Compulcr-Aided Analysis 185

In Chapter 5, we wrote briefly about the relcvance of visual repre-sentations. In this chapter, we have referred to the use of computersoftware to generate visual displays as part of the heuristic explorarionof data and in the dcveloprnem oftheoretical ideas. Hypertext/h)'perme-dia provides one powerful ccntext for wor king with visual data gcnerally.\'lie have suggested tha! although man)' qualitative r esearcher s thinkvisually, they pa)' insufficienr artenticn to visual materials as part of theirdata collection, analysis, and writing. Hyperrnedia software ClD pr ovidean especially useful \\'ay to link textual and visual rnarerials, in flexibleways, in the analysis and representation of social worlds, actors, andcultures .

It is possible, for exarnple, to eonstruct a hyperrnedia environment inwhich links are established not only berween segments of text but alsoberween text and data of many other sorts, incJuding visual materials,such as video images and still photographs. The hypcrrriedia approachmeans that sueh visual materials are not ineorporated merely as illustra-tions to the main body of text; they are given full weight as pan of thework of representation. The reader of the hyperrnedia eth nography, forexarnp!e, could readily gain access to a large number of visual rnaterialsin a flexible manner. Corisequently, the author/analyst can freely incor-porate detailed commentary on such material s, together with rhe imagesthernselves, into the ethncgraphy. Hypcrrnedia accounts of our anthr o-pologists might, hypothetically, draw not only on volurnes of textualdata, such as interview transcripts, but also on visual eviderice taken fromthe anthropologists' own fieldwork arid their academic departments(always subject ro access and ethical considerations). Inforrnants' ac-counts of fieldwork experience cculd thus incorporate visual testirnony.

Likewise, our invesligationsof material artifacts as memorabilia andself-presentatiorial devices (a possibiliry referred to in Chapter 5) couldincorporate visual representations ofthose artifacts dir ectly, Our under-standing 0[, for exarnple, aceounts and metaphors of "the field" thuscould be par alleled and developed by a corresponding analysis of tho seconcrete representations of the fields in question, \Ve could thus com-pare directly the spoken accounts of fieldwork (always in the past or inth e future, always elsewhere, always priva te) with the visual icons anomemorabilia of field trips (pr esent in time and space, bringing oth ercultures "heme" lo the academic setting, which is in turn rcnderedexotic). \Ve could thus explore and re-present directly t o ihe reader th e

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186 MAKING SENSE OF QUALlTATIVE DATA

multiple connotations of such concrete artifacts and their symbolicfunctions in the creation of a disciplinar)' and departmental collectiveidentity. Iconography can thus be incorporated into textual rnaterials(and vice versa) in a densely interlinked patterning of data and analysis.

The implernentation of such an approach to analysis and dissernina-tion is not easy. Data need to be edited-in order to preserve confiden-tiality and to render them comprehensible, for example. The nodes needto be identified and the relevant links and expansions put in place.

, Additional material must be entered into the database (and there maybe copyright problems if extracts from the relevant literature are to beincluded). The opportunities are potentially wide, however, especiallywhen we enter the world not only of hypertext but also ofhypermedia.

. It is possible to incorporate not only textual. rnaterials but also informa-, tion in other media. As we have suggested, the ethnographer m ay look

forward to a time when a reader can choose to hear extracts from interviews 'or other spokeri data, or to find video images when an expansión buttonis clicked on, or to have access to a wide arra)' of graphic images. Giventhe extent to which ethncgraphy proceeds ihrough the concrete exarnpleand the actual typeícf Atkinson, 1990; Edmondson, 1984) and to whichit is grounded in the vivid reconstruction of everyday life, there is everylikelihood of advances in ethncgraphic representation coming thoughsuch information technology. The ethnography itself mi~ht be publishcdin hard copy, as a conventional book, but theremight be another

, "ethnography," consisting of an array of information stored in diffe;:.entmedia, accessed via a computer and a CD-ROM through which theprofessional social scientist, the student, or the la)' reader could navigatepathways and pick up informa tion appropriate to respective inter estsand levels of sophistication.

The systerns and software alJ exist now, and qualitative researc~ers arestarting to exploit them. Predictions in this are a are often doomed tofailure, and one would be foolish to try to second guess how and to whatextent possibilities wiU be exploiied. Ther e is no need to assurne that al!future ethnographers will becorne (or need to become) totally cornrnit-ted enthusiasts of information technology, any more than one needpredict that fate for al! literary critics, even though the same opportuni-ties exist in the humanities. 11will be a tragic irony, however, if ethnog-raphyends up in the hands ofthe culturally and computationally illiter-

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ate because the rnajoriry of its practitioners insist on print technologyfor the presentation of their work.

Conclusion

In the course of this chapter, we introduced a number of computingstratcgies that paralJel and complement the more general analytic ap-proaches already outlined, There is no implication her~that the quali-tative researcher is bound to use an)' of the cornputer-aided strategies towhich w~ refer. The. important issue is that noneof the computerprograms will perform automatic data analysis. They all depend onresearchers dcfining for themselves what arialytic issues are to be ex-plored, wha t id~as are important, and whatmodes of representa tion aremost appropriare.

There is, as we have stressed rcpeatedly, a variery of analytic ap-proaches that 2Te perfectly proper. Many of them can be aidcd withcomputer software, and the computer can often help us perform data-haridling tasks with speed and with cornprchensive thoroughness. Onthe other hand, it would be wrong for qualitative r esearchers to allowthe available software to dr ive thcir general research strategy. It is im-portant to guard against the develcprnent of a single ortho doxy predi-cated on the assumptions and procedures built into conternporary soft-ware applications.

\Ve have not attempted to provide a systernatic r eview of the aya ilablesoftware. Several of the volumes Iisted in the "Suggestions for FurtherReading" should be consulted for technical specifications, har dwar e

, -requirements, suppliers, priccs, and so on. Our intent has been to followthe general logic of the rest of this book to show how conternpor arysoftware can be used to help achieve various analytic and representationaltasks we have identified. Here, once more, we ernphasize that the re-scarcher should not try to seek out a single orthodoxy or think that therewill be a single "best" software package. Such a solution is not in thenature of qualitative research. The student or researcher therefore shouldnot regard this chapter or the other methcdological lirerature we referto as "corisumer reports" on the software. It is irnportant to bear in mindthat software pro vides tools that can be used in various \\';])'S, and invarious com binations, to realize a nurnber of different analytic stratcgies.