bliss y ogborn 1979

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The Analysis of Qualitative DataJoan Bliss a , Jon Ogborn a & François Grize ba Chelsea College, University of London, UKb Université de Neuchâtel, Switzerland

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To cite this article: Joan Bliss, Jon Ogborn & François Grize (1979): The Analysis of QualitativeData, European Journal of Science Education, 1:4, 427-440

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EUR. J. SCI. EDUC., 1979, VOL. 1, NO. 4, 427-440

The Analysis of Qualitative Data

Joan Bliss and Jon Ogborn, Chelsea College, University of London,UK, in association with François Grize, Université de Neuchâtel,Switzerland

Foreword by Richard KempaThe recent shift in general educational research from the traditional psychometricapproach to a more qualitative approach involving techniques such as looselystructured interviews, the recording of linguistic interactions, etc., is beginning toattract the attention of the science-education researcher. The feature of theseapproaches is that they lead to 'soft' data the evaluation of which has in the past oftenbeen limited to the presentation of quotes and selected passages. In this paper, Bliss,Ogborn and Grize outline a method for the analysis of qualitative research datawhich is based on the principles of network analysis. The Editors hope that thearticle will be of much interest to science-education researchers.

R.F.K.

1. Introduction

One of the outstanding problems in educational research (indeed in socialresearch generally) is the difficulty of performing an adequate contentanalysis of qualitative data such as interview transcripts, free questionnaireresponses, observational material, or documentary evidence. We have begunto develop a method for handling such analyses, and this paper reports thepresent stage of development, current exploratory uses and possible futuredirections.

Applications already made include an analysis of over 100 unstructuredinterviews with physics undergraduates about their reactions to learning(Bliss and Ogborn 1977); an attempt to describe teacher's questions (Ogborn1977), and a scheme for categorizing some examination questions in physics(unpublished). We mention these uses to illustrate our view that the methodmay be of rather wide application and not be limited to only a few types ofmaterial.

The method, and our terminology, derive from systemic linguistics.Accordingly, in Section 2 we introduce terms and notation with (intention-ally naive) linguistic examples. In Section 3 we offer a miniature 'model' setof data to suggest how the terms and notation can be adapted for analysingeducational or other data, and sketch a tentative theoretical framework forthe description of such data. In Section 4 we give an account of past andpresent work using this or similar forms of analysis for a variety of purposes,and outline the computing system being developed to handle the informationgenerated. Finally, in Section 5 we briefly review current progress andproblems.

0140-5284/79/0104 0427 $02·00 © Taylor & Francis Ltd

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428 RESEARCH REPORTS

2. Systemic networks in linguistics

This section indicates the origin of the proposed method of analysis insystemic linguistics, and introduces terms and notation which we have takenover or adapted. Systemic linguistics originates largely with Halliday (Berry1975, 1977; Halliday 1973, 1975, 1978; Kress 1976), and has been used insociological studies (Turner 1972) and machine understanding of language(Winograd 1972). It has also influenced discourse analysis (Sinclair andCoulthard 1975, Coulthard 1977, Brazil 1975).

Systemic linguists are interested in the description and representation ofmeaning; of the semantic resources of language. It is for just this reason thattheir ideas may have value for those of us who wish to be able to say what aninterviewee meant, what an examination question is for, or what a teacherintends by a question. Since de Saussure, linguists have looked for meaningin the internal contrasts offered by language (as opposed to the relation ofwords and things); systemic linguists formalize this idea in the fundamentalnotion of system. By a system is meant a finite set of choices in an environmentwhich permits that range of choice. The available options are called terms andgive each other meaning by being just those contrasting choices which existin a context.

Starting a letter provides a commonsense example. To begin, Dear Johnhas (roughly) the meaning 'normal informality between acquaintances'because that option is one out of a fairly small set ranging perhaps fromDarling to Sirs and including Dear Jones, Dear Mr Jones, and Dear Sir. Eachmeans what it does by being the choice it is, by not being the choices it is not,and by belonging to an environment (starting a letter) in which those choicesare available or required. The terms in a representation of such choices wouldobviously include 'formality', 'informality', 'intimacy' etc.

Systemic linguists have developed this basic idea considerably, and haveintroduced a convenient and powerful formalism for representing it. Thesimplest case is that of exclusive choice: a linguistic example would be theoption open to the clause between statement and question. This isrepresented by terms written against a bar, as shown in figure 1.

I— DECLARATIVE

(Clause)

1— INTERROGATIVE

Figure 1. Bar to represent exclusive choice

Next we consider the notion of delicacy. Questions can be more finelydivided into YES/NO questions {Did he do it?) and WH-questions{When I Why I How I Where did he do it?). Equally, the choice between questionand statement can itself be seen as a finer choice amongst information-passing (INDICATIVE) clauses as contrasted with requesting or exhortingclauses (IMPERATIVE). Delicacy is represented by linking options in aleft-to-right tree, as in figure 2.

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THE ANALYSIS OF QUALITATIVE DATA 429

— DECLARATIVE

r- INDICATIVE

(Clause) —

r YES/NO Question

— INTERROGATIVE —

L- IMPERATIVE •— WH — Question

Figure 2. The notion of Delicacy

Binary choices are shown here only for simplicity: a system can have anyfinite number of terms.

The options above act on clauses. But language is organized into units ofmore than one size: in English, these units include words, groups (e.g. verbalor nominal groups), clauses, and clause-complexes. Such units form a rankscale. Different options operate at different ranks: plural/singular operates onwords, tense choice on verbal groups, and declarative/interrogative choice onclauses. There are also clause-co-ordination options which build units at therank of clause-complex {I found it difficult but/when I learnt it). We havefound it useful to think in terms of rank in devising suitable structures fordescribing data.

The next notion is that of simultaneity, or allowed free combinations ofchoices. For example, verbal groups in all kinds of indicative clauses can varyfreely in tense {He is doing it; He will do it; Will he do it? How was he doing it?etc. etc.), but imperatives do not vary in tense in the same way (one cannot tellsomeone to do something in the past!). The example introduces the furthernotation shown in figure 3.

r— NON-FINITE e.g. to go

Verbal Group —

L- FINITE

I— MAJOR

Clause —

•INDICATIVE —

PAST

PRESENT

FUTURE

r- DECLARATIVE

j - YES/NO Question

• IMPERATIVE <— INTERROGATIVE —

- W H - Question

I—MINOR

Figure 3. Simultaneity, conditional entry and recursion

The simultaneous options of tense and of types of indicative are shownby writing these systems against a right-facing bracket — {I. Such a bracket isanalogous to stating differing independent dimensions. It is an extremelypowerful notion, because it allows the linguist to say how a segment oflanguage means several different kinds of thing at one and the same time. It isprecisely this aspect of the description of qualitative data that it is mostdifficult to handle in a system of simple exclusive categories.

The example of tense further illustrates the useful notion of recursivechoice, indicated by the letter R written against the tense system (ournotation, notHalliday's). That is, in English, besides doing or did there can bewill have done, was being done, will have been doing, etc., in which tense choices

E.J.S.E. 2 F

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are repeated to build complex tenses. In analysing qualitative material onefrequently finds a need to allow for such repeated selection (e.g. 'the problemwas both difficult and long').

Finally, figure 3 illustrates the use of entry conditions. It shows two types:The left-facing bracket Z } shows that for the option indicative/imperative toexist, the clause must be major (e.g. he learnt the work, but not, 'till he knewit), and that the verbal group must be finite (go or went but not to go or going).Thus a network can represent necessary entry conditions to a system. Thesecond type is the left-facing bar]-, which in figure 3 shows that the system oftense is available either to (some) non-finite verbal groups (to go, to have gone,to be going to go) or to verbal groups in indicative clauses. J Thus a networkcan represent alternative entry conditions. Both types are useful in descript-ive analysis, the first particularly so.

We have now presented by examples the main components of networks.The examples are purely illustrative; they would not satisfy a linguist.But what is a network and what does it do? How does it relate to actual talk orwriting?

A network is a structure of possibilities, showing their dependence andindependence. It allows certain configurations of choices (e.g. FINITEVERB, MAJOR CLAUSE, IMPERATIVE) but not others (e.g. PASTIMPERATIVE). Each possible allowed configuration is termed a paradigmof that network. Clearly a quite compact network can represent a veryconsiderable number of paradigms. However, the paradigms are not yetactual language; they say only that certain choices exist, not how thosechoices appear in speaking or writing. To link the network to what is said orwritten we need the further notion of realization rules; the rules which sayhow the language encodes the choices. Obvious examples include adding 5 tomake words plural or suppressing the subject and transposing the object tomake clauses passive (he did the experiment, the experiment was done). Asexplained in the next section, we have modified the notion of realization ruleso as to connect networks showing structures of possible features of data withdescriptions of particular items of data.

We have now introduced the terms: system, delicacy, rank, simultaneity,recursion, entry conditions, paradigm, and realization rule. We have alsoillustrated the network notation using right- and left-facing bars andbrackets, summarized in figure 4. All are of course introduced here not for

If A, thenenter oneof B,C,D.

* If A, thenenter all the

systems B,C,D.

Enter D onlyif all ofA,B,C.

Enter D ifone or more

of A,B,C.

Figure 4. Network notation

% It is a fault in the network not to allow tense variation for minor clauses.

- - B - B A - A - -

A - - C A - • - C B - - D B - - D

- - D - D C - C - -Dow

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their linguistic interest, but for their value in describing educational or otherdata.

The whole line of thought can be summarized as follows. A network is astructured pattern of interdependent options, showing by its structure thepatterning of related descriptive features, and by the combinations offeatures it permits the particular complex groupings of features it accountsfor and labels with those features. Actual instances of different meaningseach correspond to just one configuration of choices out of the possibleconfigurations or paradigms. All that we ever hear, say, read, or write arerealizations of one paradigm at a time, but each instance gets its meaningbecause it exists as one possibility amongst a finite set of other (linked)possibilities. Different aspects of meaning are to be caught by describingsimultaneously existing structures of meaning.

Why trouble with all this? The reason is that the problem of describingqualitative data is, as we see it, very largely the problem of handling acomplex of descriptive features at very varied levels of generality; of seeing inwhat ways items of data are alike and are different. When one speaks, onemeans more than one thing at once, and those who give us educational datanormally take the same liberty.

3. Adaptation to non-linguistic analysis

This section illustrates, through a simple 'model' example, how we have beenled to adapt and modify the linguistic framework just described, for theanalysis of educational research data. It is essential to stress that the analysisis not itself linguistic, even when it deals with language data (such asinterview transcripts). The problem is to extract, codify, and represent non-linguistic information: thoughts, feelings, ideas, events etc. Data may be ofany qualitative kind, including drawings or pictures.

Suppose, then, that we are trying to analyse the following miniature'model' set of data, consisting of fragments of stories about learning.}: It isoffered purely for illustration: the real value of networks lies in their ability todeal with much larger and more complex bodies of information.

Model data

A ' . . .when I started reading Landau and Lifschitz' mechanics—Ifollowed the book—it is a beautiful classic book on mechanics—andthings came out so well I felt like kissing the book... '

B ' . . . I had a very good essay given back to me; that made me feel verygood, because I had spent a long time on i t . . . for the rest of that termI felt so good I could have tackled anything.. . '

C ' . . . in practicals we were supposed to be learning soldering... I gotstuck and the supervisor thought it was very funny that there I wasmaking a fool of myself...'

D ' . . . I feel bound to go through as many problem sheets as I can, but Ijust couldn't do it. I found that I couldn't keep up with it and I keptgetting very depressed and wouldn't speak to anybody .. .'

X Extracted and modified from interviews with physics students (Bliss and Ogborn 1977).2F2

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Model analysis

Any analysis must start from the point of the research. Suppose that it is torelate students' reactions to the circumstances in which they arose: then anaccount of the data would want to describe reactions, situations, and ifpossible circumstantial detail. For this reason, the network shown in figure 5begins at the left with these as independent descriptive dimensions.

Consider the four situations: reading, an essay, a practical class, anddoing problems. If the analyst thought that reactions might differ as betweenprivate, individual work and things done publicly in class (and if the datasupported the distinction) he might sketch the simple tree of more delicateoptions shown following SITUATIONS in figure 5, so grouping threesituations together and one apart. One would try to have, as the most delicateoptions, situations which were specific enough to relate directly to individualitems of data (e.g., READING) but general enough to apply to more thanone (e.g., probably not READING LANDAU). Decisions would have to bemade as to how delicately to distinguish situations, for example furtherdividing practical work into (say) techniques (as here), set experiments, andprojects. The decisions would depend on the variety offered by the data, andthe plausibility of the distinction mattering.

Consider now the reactions or feelings expressed. After many attempts(and with more evidence than is given here) one might describe the reactionsshown as, respectively:

A Elated, and 'beauty-of-ideas'.B Pleased, and 'able-to-cope'.C Felt foolish.D Depressed, and 'not-able-to-cope'.

As before, one would try to find descriptions narrow enough to be able to bematched against the data itself, but wide enough to work on several items.Also as before, one would try to organize these (and other) descriptions into anetwork of less delicate and perhaps simultaneous options. To do so wouldnaturally involve guesses about what kinds of difference might proveimportant and reliable as accounts of the data. Possibly the result might bethe part of the network following REACTIONS in figure 5. Then itwould be necessary to check that each of the described reactionscorresponded well enough to one network paradigm: 'elated' to positivesatisfaction; 'beauty-of ideas' to positive involvement, 'pleased' to positivesatisfaction, 'able-to-cope' and 'not-able-to-cope' to positive and negativecoping, etc. Doing so would immediately raise problems: for example, thenetwork equates 'elated' and 'pleased' as both positive satisfactions—is thatgood enough? One might be led to distinguish strong versus moderatereactions as a result. Similarly, is 'depressed' well represented as negativesatisfaction, as the opposite of 'pleased'? Might feeling able or not able tocope be better as feelings about the self instead of feelings within the self;indeed, can that distinction be maintained? As analysis proceeded, suchquestions would have to be resolved, judging always by how well thedescriptions assigned by the network fitted the interpretations one coulddecently give the data.

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Figure 5. Network to represent the 'model' data

Finally, figure 5 shows a fragment of network describing circumstantialdetail, dividing it into interpersonal interaction (as in C) and more individualprivate circumstances as in A, B and D, together with a device forrepresenting not trying and not succeeding as well as trying and succeeding.The whole network is clearly naive; at the same time it does perhaps capture agood deal of what one can see in the data. Its point here is to illustrate theprocess of network construction and its problems.

Coding

It is clear that the network describes, not the data, but an interpretation of thedata for a purpose. Its paradigms are what the analyst wants to extract. Forthis reason, we have found it useful to make one very significant modificationof the linguistic framework: to introduce the notion of an artificial codinglanguage, which functions as the language in which the analyst says what hesees.

The idea is simply to invent realization rules for the descriptive networkwhich generate, from the chosen options, a simple code which reads like adirect analysis of each item of data; which reads as a summary of what is

I— ESSAY

i- PRIVATE —READING

SITUATIONS I—PROBLEMS

L P U B L I C _r P R A C T I C A L

I— etc.

:

POSITIVE

NEGATIVE

< REACTIONS — '

EOF INVOLVEMENT r- CONTROL

WITHIN MYSELF —, L SATISFACTION

ABOUT MYSELF L COPING

i—DOING

r , N D I V . D U A L — ~ L NOT DOINGi—TRYING

CIRCUMSTANCES— L SUCCEEDING

V r B E LAUGHED ATU INTERPERSONAL-n

I—etc.

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there. But each term in the code is to be understood, not as having its normalrange of meaning, but as having a fixed and definite meaning assigned by thenetwork. For example, some simple rules might give as codes:

A ABOUT READING.I FELT ELATED AND BEAUTY-OF-IDEAS.HAVING SUCCEEDED.

B ABOUT ESSAY.I FELT PLEASED AND ABLE-TO-COPE.HAVING TRIED AND SUCCEEDED.

C ABOUT PRACTICAL-WORK.I FELT FOOLISH.HAVING BEEN-LAUGHED-AT.

D ABOUT PROBLEMS.I FELT DEPRESSED AND NOT-ABLE-TO-COPE.HAVING TRIED AND NOT SUCCEEDED.

Rules might include using ABOUT, I FELT, and HAVING to markrespectively situations, reactions and circumstances, and using NOT toindicate negative rather than positive options. Many items of code are justcopies of the most delicate option; in such cases the network automaticallyimplies their having the less delicate features it assigns them.

One value of coding like this is that the codes, which carry the analysis,are easily read and compared with interpretations of the data. Here, forexample, one might note that D does not encode the feeling of 'being boundto do as many as possible', which might lead to augmenting the network by,and adding to the code, features to do with compulsion or freedom. Further,using the code, one can look to see how naturally the form chosen for thenetwork expresses an interpretation of the data; is this data well representedby a 'situation-reaction-circumstance' format?

The codes, of course, retain implicitly all the distinctions written intothe network whose options they realize. Thus it is possible to count andcorrelate features at any level of delicacy—normally there will be a greatmany. It is this dual property of being at once a description language which israther close to the data, and a formal language with fixed and definite termsand meanings, which makes such codes and their associated networks usefulin handling large bodies of complex and subtle qualitative data. In a sense,the code is what the analyst might write if he were asked simply to summarizewhat he saw in a given item of data, but written in a special language,developed from the data itself in a systematic way, designed to say as muchbut no more than is wanted. The network ensures that the codes have just theimplications and 'hidden' meanings required, and provides the means ofextracting them again when necessary.

Theory of the analysis

In the linguistic case, it is language itself which is the data. A systemiclinguist tries to describe how aspects of it can be accounted for as realizationsof options in a network he proposes. The network is his theory of language.In our case, the data is further filtered through the interests, preconceptions,

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Realization

( ö ) N E T W 0 R K ^ _ ^ ^ LG E

rules |

FERCEPTJCTNSJI Realization I I Description ' I

{b) NETWORK _ _ ^ _ - ; - - LE _ ^ _ | DATA |

| rules I | I I i

I I

Figure 6. Network, language and data (a) linguistics (6) our analysis

and perceptions of the analyst. Those perceptions he tries to codify and fix inan artificial description language. Figure 6 shows the difference between thetwo cases. No system of analysis can avoid the fact that the relation betweenperceptions of data and the analysis (whether as categories or as here) isproblematic. What the present system aims to do is to make such intuitiverelations more explicit, more accessible to discussion, and less fluid in theirapplication.

—A

— B

—C

— etc.

Figure 7. Categories as a system

It is worth noting that the scheme can be seen as a generalization oforthodox analysis by categories. In the simplest case of mutally exclusivecategories A, B, C, etc., the network is as in figure 7 and the descriptions arejust the category names, 'realized' by quoting. Categories in a hierarchy (e.g.Bloom et al. 1956), or with sub-categories, are just tree networks. Theessential innovation taken from linguistics is to provide for descriptions ofseveral simultaneous aspects in one code.

4. Applications and developments

Socio-semantic networks

The use of networks to represent educational or sociological data is not new.Turner (1972) has used systemic networks to describe structural differencesin the ways parents of different kinds of families control their children, aspart of a study of socialization (Bernstein 1972). Another suggestiveapplication is that of Mohan (1969) to the language and situation in a cardgame, with reference to the nature of rules and instructions.

One of us (J.O.) has attempted to use systemic networks to categorizedifferences between teachers' questions, using data from recorded university

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tutorial classes in physics (Ogborn 1977). The networks represent styles ofquestion, types of content, types of role, and types of interconnectionbetween questions. In similar vein is a tentative study of teacher-pupilinteractions, using published classroom transactions (Monk 1977).

In all these applications, there is nothing corresponding to the artificialcoding language introduced in Section 3. The networks function as aclassificatory device, each paradigm being a structured complex of classifi-catory features.

Analysis of interview data

As part of the Higher Education Learning Project (Physics) (see Black andOgborn 1977) we, with a team of university teachers, interviewed 115 physicsundergraduates in 10 universities (Bliss and Ogborn 1977). The studentswere asked to talk about times when they had felt especially 'good' and 'bad'about their learning. The interviews produced some 300 stories for analysis:a formidable bulk of complex material, and a situation only too familiar to allthose who have conducted interviews on any scale. Further, the teachersinvolved showed a healthy practical interest in matters of detail and were notinclined to accept any too general or global set of categories to describe thematerial. They felt that it mattered if an incident involved an unexpectedword of praise, for example, and we were inclined to agree that 'motivation' isbest understood as related to the particular character of events rather than toany general 'level of interest' or of involvement.

We also—as so often—found that simple categories failed because everyitem needed to belong at once to overlapping categories along severaldimensions. We turned to systemic networks as offering the possibility ofaccommodating complex interrelated classifications. In addition, partlybecause of the bulk of the data, we developed the notion of coding aspreviously described.

A typical story, encoding some two to five pages of transcript, might looklike this:

STORY CONCERNS PARTICULAR INDIVIDUALLEARNINGT H A T IS WRITING ESSAYWHEN (I FELT PLEASURE AND PLEASED-WITH-MYSELFAND I-HAVE-DONE-IT)

BECAUSE (I DID A LOT OF PREPARATION)SO (I UNDERSTOOD IDEAS)

ALSO (TEACHER PRAISED ME)ALSO (TEACHER GAVE ME GOOD MARK)ALSO (I DID WELL WHICH IS UNUSUAL)

ALSO WHEN (I FELT IT-WAS-MY-WORK)BECAUSE (I WAS WORKING INDEPENDENTLY)

We gave the codes a rank structure consisting of a story made up of clauseslinked by relating terms (WHEN, BECAUSE etc.). The first clause had todescribe the situation, the next the reaction, and further clauses the reasonsfor the reaction. Dependency between clauses was indicated by indentation.

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In this way the networks allowed the code to express the content andstructure of the stories, all to be further analysed in terms of frequencies.

At the rank of clause, each had to have a subject or topic (in first place)and a comment (in TEACHER PRAISED ME, the topic is the teacher, and'praised me' is the comment). A very large network stored all the types ofcomments we found we needed, grouped by the network under classifyingfeatures such as actions or appearances, cognitive or affective, personal orsituational, interpersonal or individual, etc. etc. The network grouped itemsunder clusters of such features, keeping apart things to do with the teacher,the student, or the situation as topic, for example.

This meant two things. First, each story had a code close in form andexpression to the story itself, making it easier to check for adequacy, while atthe same time being assigned by the network a cluster of features at all levelsof delicacy. Second, the existence for each story of this cluster of networkfeatures made it possible to count and correlate features at various levels ofgenerality. We could ask, without having lost the particularity of individualstories, how many concerned (for example) interpersonal interactions with ateacher, or the student's feelings about himself and the amount of praise hegot or his perception of how well he worked. In the event, limitations of timerestricted the number of issues we could look at. Typical outcomes were therelatively high frequency in 'bad' stories of reactions of withdrawal (notablyin lectures), and the relatively high intensity of involvement felt in individualwork as well as in projects. We felt a need, however, for a better way of doingall the counting than by hand.

A systemic network computing system

Recently, F. Grize has begun work with us on developing a computingsystem to accept an arbitrary network specified by the user; to accept, checkand 'understand' codes representing data, written as realizations of networkoptions; and to answer questions about numbers of items with arbitraryconfigurations of network features. At present, the system is still underdevelopment. It allows only the crudest realizations (copying most delicateterms); conditional entry is developed but not yet implemented fully. Itemscan be listed, counted or cross-tabulated.

The computing system is told the network for given data by a fairlyobvious translation of network symbols into a symbolic language. Figure 8gives some examples. The system is written in PASCAL and runsinteractively at a terminal.

Problem-solving protocols

A group at Chelsea College Centre for Science Education is interested instudents' problem-solving processes. This work is at an early stage, but theidea of analysis using networks has shown some promise. In one case,F. Mujib has developed a network to describe how the student's attentionshifts from one aspect of the problem to another, how the type of approachvaries in different parts of a problem, and the type of cognitive operationsused. H. Elliot has a prototype network to describe the range and interconnec-tion of knowledge potentially implicated in the solution of a particular

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- B F B I A = BAR(B C).

- A - A = BAR(BC). A - B = BAR(EF).

-c L-c

— B L r_ A - A=BRA(BC). A

-cA = BRA(BAR(B C) BAR(D El)

Figure 8. Computer translations of network symbols

chemistry problem. One aim of the group is to develop further the idea ofusing networks to represent knowledge, especially partial knowledge.

Examination questions

Related to the problem of describing knowledge is that of describing testitems or examination questions. Existing category systems (Bloom et al.1956) together with lists of syllabus topics are much used, but we hope thatthe systemic network representation will offer the chance of representingmore detail without losing higher classificatory features, and withoutdeciding once and for all the level of delicacy at which to analyse. Theparticular material being investigated is, at present, some hundreds ofmultiple choice items for the Nuffield Advanced Physics examination, andnetworks describing the items are being explored for their potential inrecording and extracting information about this question bank.

5. Conclusions

We do not wish to claim too much for the ideas sketched in this progressreport. In particular, the preliminary stage of many of the applications makesit hard to evaluate the real promise of the approach. At the same time, we feelthat there is here an important problem, namely the handling in educationaland other research of complex qualitative material, where the difficulty is toretain its detail and subtlety without losing control and, at the same time, tobe able to look at it from several general points of view without deforming theindividuality of particular pieces of data. Just because these problems exist, itis not easy to deal with the important but 'soft' data one often has, and suchdata often fails to carry the weight it might otherwise have had. Researchersare reduced to quotation, which if selective is in danger of bias, and ifcomprehensive leads to enormous and indigestible reports. New approachesto evaluation, which seek to mitigate the limitations of 'hard' test data, haveall too often been dogged by such methodological impasses.

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THE ANALYSIS OF QUALITATIVE DATA 439,

Many problems remain to be resolved or clarified. One is the problem ofestablishing criteria for 'good' network features and structures. Where theaspect studied is well understood, as for example the scientific knowledgeimplied in a question, the understood logic of the material is a great help. Inhandling the interview material, we found that it was the harder to establishmeaningful features and structures the less well we understood the situationsto be described; fairly hard for (say) students' feelings despite some insightsfrom psychology, and very hard for the complex situations of laboratory andclassroom where, as the network revealed, our ideas tended to be superficial.Thus the existence of some background theory, and the knowledge andunderstanding of the analyst, turn out to be very important.

Another problem is that of establishing ways of testing the reliability ofcodes and their associated features. Here the method seems to have someadvantages over simpler category systems, in that it is hard to get agreementwhen one category must contain much data, but easier when, as here, thecode reflects rather closely detailed perceptions of the data. One analyst maynot agree with another, but each knows more about where the disagreementlies. Further, since the network assigns to codes features at successively lessand less delicacy, it is often possible to trace, when there is disagreement, thelevel of delicacy at which one can find agreement. That is, analysts may notagree about the exact descriptive term chosen, but they soon find that all theterms they favour have some higher level set of features in common. Lastly,one problem of classificatory reliability is simply that people forget what thecategories mean, or allow their meaning to shift. Here the network, as a kindof semantic grammar, can help a good deal in maintaining the stability ofmeanings.

A very real problem with the method is that one has to learn it. It lookscomplicated, perhaps absurdly so. We find that it is not too hard to begin, butthat (in our limited experience) difficulties soon set in. The difficulties aresometimes the outcome of the power of the method, in that one tries to handlemore complexity than one is ready for. It will clearly be necessary, if themethod is judged to have any value, to develop exercises and problems whichstudents could use in order to learn it.

In conclusion, it must be stressed that the method in no way forcesanything on the analyst. Quite the reverse: we feel that, whatever itsproblems and need of development, the central claim that might be made forit is that it both allows and forces the analyst to find out what he wants to say,and to make that explicit. It does not tell him how to analyse data. It is not asubstitute for understanding and insight. All it does is to provide aframework for developing one's own analysis. Indeed, in using it, we havefound to our cost that it exposes in a cruel light many of the inadequacies andsuperficialities in one's own thinking. That by itself may be enough of anadvantage to be going on with.

References

BERNSTEIN, B. (Ed) 1972, Class, Codes and Control, Vol. 2 (Routledge and KeganPaul: London).

BERRY, M. 1975, 1977, Introduction to Systemic Linguistics Vol. 1: Structures andSystems, Vol. 2: Levels and Links (Batsford: London).

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S u m m a r i e s

EnglishA method for the analysis of qualitative data, based on the linguistic device of systemicnetworks used to represent a structure of possibilities, is reported. Some initial applicationsare described, and work in progress including a computing system is outlined.

DeutschVorgestellt wird eine Methode für die Analyse qualitativer Daten. Sie basiert auf derlinguistischen Vorrichtung von Systemnetzen, die zur Darstellung einer Struktur vonMöglichkeiten gebraucht werden. Einige erste Anwendungen werden beschrieben, und diefortlaufende Arbeit einschließlich eines Rechensystems wird skizziert.

FrançaisCet article expose une méthode pour l'analyse qualitative des données. Cette méthode se basesur un système linguistique de réseaux, utilisés pour la description d'une structure depossibilités. De plus, les auteurs décrivent les premières applications et présentent le travail encours, y compris un système d'ordinateur.

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bliss y ogborn 1979

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