Supporting Multiple Perspectives:  Case Studies of Using
Hypertext in Qualitative Research

Mark A. Horney
University of Oregon

     This paper presents three case studies of educational
researchers using a hypertext editing program as part of their
data analysis process. The studies come from a larger project
examining hypertext authoring in a variety of contexts (Horney,
1991). I begin with a short introduction to the concepts of
hypertext and follow the case studies with a short analysis of
lessons to be drawn from the experiences of these researchers. 
Hypertext
     Hypertext is a form of electronic writing and although its
origins go back to at least 45 years  (Bush, 1945), hypertext
software has been generally accessible only in the past few
years. There are differing views as to what exactly constitutes
hypertext, but it is generally described as either
"non-sequential writing" (Nelson, 1987, p. 1/17), or as a web of
informational nodes that are linked together and which readers
access via a navigation system in a sequence of their own
choosing (Dede, 1988, p. 96).      The utility of hypertext
software for qualitative researchers can be seen by considering
its application to traditional þsegment and sortþ techniques of
data analysis wherein researchers first þ... divide observed
phenomena into units, and [then] indicate how units are like and
unlike each otherþ (Goetz & LeCompte, 1984, p. 170).
Operationally this process often amounts to marking up paper
transcripts with margin notes or colored markers, cutting the
transcripts apart, and piling or pasting the resulting text
fragments into appropriate categories. 
     A researcher using a hypertext editing system for this task
follows the same general steps, but commits results to paper
only, if ever, in the final stages. Segmenting is accomplished by
separating transcript fragments and placing each in a separate
hypertext node. Fragments can be defined by some arbitrary rule
(e.g. starting a new node after each change in speaker or for
each new paragraph), or for semantic reasons as judged by the
researcher. Once nodes have been established, comparisons and
contrasts are drawn by erecting labeled links between the nodes.
Categorization is accomplished by creating new nodes, one for
each category, and then linking each data node to appropriate
categorical nodes. Categories can be examined by using the
navigational system to traverse links electronically, or by
printing their nodes to paper.      Just as the term hypertext is
somewhat ill-defined, software packages with hypertext features
vary a good deal in their capabilities. A list, with brief
descriptions is given by Fielding and Lee (1991, p. 195-199). The
participants in this study used EntryWay, a hypertext editing
system which I developed. EntryWay runs on the Macintosh family
of computers and is based on HyperCard (Apple Computer, 1987).
Like all hypertext systems EntryWay provides for nodes, links,
and a system of navigation. Sample nodes are shown in *Figures 1
and 2. 




   *Figure 1.
EntryWay Data
Node*Figure 2.
EntryWay Thread


   EntryWay nodes can be configured with a variety of different
spaces, called fields, for researchers to enter text. *Figure 1
has been set up as a data node with a central field to hold a
data fragment, and three surrounding fields for commentary. The
four corner arrows indicate links to other nodes and the menu bar
across the top provides commands for navigation and node/link
manipulations. Each node is named, in this case S110, which was
chosen to indicate this as the 110th node of an interview with an
informant called Shannon (this and all other participant names
are pseudonyms). Text of this sort can be automatically imported
into EntryWay documents from word processing files.
   *Figure 2 shows a special type of EntryWay node called a
þthread.þ Threads are used to form categories. The list to the
left shows the members of the thread þCatIntersect,þ each of
which is another node of the document. The text held on the
member nodes of a thread can be exported from the document to
word processing files where it can be viewed, printed, or
transferred for manipulation by yet other programs. Other than
maintaining this special list, threads operate like any other
node and can themselves become thread members, and the þanchorsþ
of links. This versatility is useful in representing
relationships among coding categories and their attendant
constructs.    Finally, EntryWay also has the capability to draw
þmapsþ showing thread and link relationships among nodes. This
feature will be explained in the third case study. This brief
overview of the principles of hypertext and the capabilities of
EntryWay can not do justice to either, but it should suffice to
make the following case studies comprehensible. 
The Case Studies
  These case studies were part of a larger research project which
aimed to gain a broad experience with hypertext and from that
experience to tease out factors affecting hypertext authors as
they work. To accomplish this goal, I created EntryWay, analyzed
the characteristics of hypertext to identify particular tasks for
which it might be useful, sought out individuals willing and
needing to accomplish these tasks, taught them to use EntryWay,
assisted them in their work, and closely observed their
activities. The accounts presented here were based on data
collected from taped interviews, direct observations, the
hypertext documents created by each researcher, and from computer
monitoring transcripts kept automatically by EntryWay showing
activities. Dr. Peter Stone
  Dr. Stone was an assistant professor in a Department of
Physical Education, where his research focused on teacher
socialization. At the time we met, Stone and a colleague were
studying a teacher educator, a Dr. Rogers. They had collected a
large body of data about Rogersþ teaching methods, and were in
the final stages of data analysis. Part of their data consisted
of interviews with five of Rogersþ students. Stone was interested
to see if the perceptions of these students supported the
interpretations he had drawn from other data. After seeing a
demonstration, Dr. Stone agreed to use EntryWay in analyzing
these transcripts.
  I originally planned an observational study of Dr. Stoneþs work
with EntryWay. I expected to introduce him to hypertext, help
plan an interview analysis document, observe him on some
occasions while he worked, and then reconstruct his experience
from the program monitors, the observations and a final
interview. In practice however, I became a participant observer
and collaborated with Stone at each stage of the analysis. In
part, this collaboration was necessary because of problems with
hardware. Stone owned a Macintosh Plus computer, which he used
for word processing, but which did not have a hard disk. This
deficiency threatened to limit the size and functionality of
Stoneþs document and so all of our work was done on a better
computer which I supplied. Since I could not leave the computer
with him, we had to meet and work together. This situation had
the advantage that I was able to watch Stoneþs entire process of
data analysis, but ongoing scheduling problems extended the
project over a period of several months. 
  Our work with the interviews proceeded in five steps: (a)
preparation of the interview transcripts; (b) primary coding of
the transcript elements; (c) secondary coding; (d) examining
thread intersections; and (e) exporting threads. I did all of the
preparatory work and will describe that process in detail since
similar procedures were followed in the other cases.
  I began with a disk containing text files of the interviews
which had been transcribed from audio tape. I first divided the
transcripts into paragraph sized chunks (referred to as
transcript þelementsþ), each marked by a change in speaker, with
generally one node containing a question and the following node
the answer. These chunking decisions were made more or less
arbitrarily, but often with a eye towards making chunks smaller
rather than larger.

  I next imported the prepared files into an EntryWay document,
which we called þROGERS.þ Once all of the data nodes were in
place, I created several þorganizationalþ threads to facilitate
navigation and searches. For instance the thread ELEMENTS
contained all 650 data nodes. ANSWERS and INTERVIEWERS contained
just nodes with student responses or just researcher questions.
The S, M, T, H, and P threads each contained nodes from one of
the interviews. Once these threads were in place, I made
printouts for Stone showing the interview text segmented and
labeled as it appeared in ROGERS.
  As a final step, I made ROGERSMAP, a map intended to show Stone
the relationships among the organizational threads and to serve
as a navigation tool. However, we never used the map after our
first work session. The entire process of preparing ROGERS took
approximately six hours.
  The next stage in working with ROGERS was the primary coding of
the transcripts, which was accomplished in two work sessions.
This involved placing data nodes onto 18 threads, Q1 through Q18,
one for each of the questions in the interview protocol. Stone
later added four additional questions. This process took
approximately five and a half hours and served to re-acquaint
Stone with the data, re-organize the data from its chronological
ordering into a content ordering, and to eliminate material which
was irrelevant. Note however, that all the data always remained
available within ROGERS, and that nodes could always be viewed
from either ordering.   As Stone worked, I gave him instructions
on the use of EntryWay. These directions had to be repeated in
each session since Stone forgot various commands in the long
interims between sessions. Only towards the end, when we had
three sessions in two weeks, was Stone able to proceed
independently. I also made suggestions about document
organization and about short cuts.
  Stone worked steadily, moving from element to element,
occasionally using the TRAIL, a list showing recently visited
nodes, to access one of the questions. Some elements were handled
quickly, such as statements by the interviewers, which Stone only
glanced at and didnþt categorize. Others were visited for the
10-20 seconds it took to read and place them. 
  Once Stone completed the primary coding process, he began
secondary coding by re-coding the data using categories derived
from other data about Dr. Rogers:       So far these are the
three main categories that weþre working with:      experiences
as a mother, her multiple roles as faculty member, and then     
her relationships with the students ... Thereþs also a secondary
area      dealing with knowledge and issues central to teaching
that she thinks are      particularly important.

  In the two work sessions comprising this secondary round of
coding Stone created seven new threads (VIDEOTAPING, MOTOR
LEARNING, MOTHER, ROLES, STUDENTS, BACKGROUND, STYLES) and
revisited his data placing nodes into these new categories. There
were two changes in his work pattern. First, rather than visiting
all nodes, he only coded those which had already been placed on
one of the 22 question threads, and second, he used EntryWayþs
keyword search function to locate some data. For instance, for
the thread VIDEOTAPING we searched for þvideo,þ þtaping,þ and
þtape.þ EntryWay automatically placed any entry containing those
words on the VIDEOTAPING thread. These searches did not eliminate
the need to read each node, but did provide Stone with some
confidence that information relating to his categories did exist
within the data set.
  During this time Stone added five additional þstudentþ
categories (Practice, Lectures, Substitute, Confident, Planning)
which he thought complemented the original seven þteacherþ
categories: þThis is one of the things I thought was going to
happen if I went through the questions. Iþd begin to see things
that I missed, and I don't have a category for.þ
  When finished with this secondary coding, Stone felt his work
with ROGERS was essentially complete. His only further
requirement was to obtain printouts of the student and teacher
threads. In these he was going to search for, on paper, dominat
themes and find þstudent quotes to fit into some of the category
definitions we already have.þ I suggested that such dominate
themes might be found in the intersection of his teacher and
student categories. I proposed to show this intersection by
making a map of the relationships between these categories. I was
particularly eager to do this because at this point Iþd been
considering the usefulness of maps for some time, but hadnþt made
or found one of any real value. I was also interested to see if I
could push Stone into a deeper analysis of the relationships
among categories, the sort of secondary analysis that might
better justify the use of hypertext. Stone agreed the
intersection might be useful.   Creating an intersection thread
(*shown in Figure 2) of the teacher and students categories was
relatively simple. Making the map proved to be more difficult and
for technical reasons this was abandoned. Therefore, I simply
made a printout of the intersection thread, which contained 35
data nodes, and sent it along to Stone with printouts of each of
the other categories.
  In our final meeting Stone discussed the process of creating
and using hypertext in qualitative research. Stone allowed that
at the beginning he wasnþt sure of exactly what I wanted and let
us work on transcripts he thought would be easy to analyze. None
the less, Stone found the process þenormously useful ... it
certainly saved time.þ      What I found most useful were these
parts where the sections were broken      down. Originally we
started off by looking at questions, Iþd say OK letþs      put
question one together and question two together and so forth.
That was      ok, that was useful and that's about where I
usually wind up with this kind      of analysis anyway. But when
we were able to breakdown the categories      and actually fit
the quotes in with the category, at least a preliminary     
category. It just makes it that much easier to analyze, to
immediately see in      front of me and realize we do have a
category, that thereþs enough there,      that itþs a legitimate
category. At least close enough to being a final      category
that I could begin drafting the document. It also makes it easy
to      have the quotes right there.
What I [normally] do is take the transcripts and either cut them
or highlight them in some way, and group them. I think about them
for a while, and come back and see if I can regroup them. Then,
maybe write a draft of just this one section, see how that comes
out, and then go back and see if I need to regroup them again,
does everything fit, is there anything that doesnþt make sense?
The number of steps I take are a lot longer than [using EntryWay]
because what we did on the computer in a matter of not even
seconds, what would be a physical movement, and so would take
much longer. I try not to cut up the transcripts because once you
do that, its kind of like your thread elements, once you cut up
the transcript you more or less loose the thread elements.   I
suggested to Stone that even more time could have been saved if
we skipped the first round of coding (organizing the transcripts
according to the interview protocol), and had gone straight to
categorizing by the teacher and student themes used in the
secondary coding. Stone objected:
     I don't know if I could have, because when you gave me the
printouts of      the questions, I could see it was very labored.
The questions didnþt give us      the answers we were after. I
saw that, and in reading the answers I began      to see what the
questions should have been. That's what the categories     
became.

  Stone noted a change in the way he treated quotes:
     ... I donþt know if it was the program that allowed me to do
this, or a      change Iþm making in my own way of analyzing
data. I find sometimes I      get a good quote and Iþll hang too
much on it, I'll put more value in it      because itþs worded
well or expresses an idea that I find attractive, and it     
blows the weight of that idea out of proportion to what it should
be. So      this time, this program kind of allowed me to focus
on a bigger picture.  
     I think the speed had something to do with it. For example,
in question      one, we sat down that one morning and had almost
all the questions sorted      in two or three hours. Ordinarily
I'd take two or three hours for one      question and try to sort
one question out, get that set, and then move on to      the next
one.

     I wasnþt focused on the specifics immediately. I was focused
on trying to      analyze the larger data set rather than just
the specific question. 
  Stone believed the use of EntryWay saved enough time that he
was able to do a secondary analysis of materials he would
normally only go through once, and that this in turn allowed him
to avoid becoming too attached to the easily found items that
might dominate his thinking inappropriately. Stoneþs task was one
of data reduction. The student interviews were not the main focus
of his study of Dr. Rogers, they existed only as support. What we
did with EntryWay was to sift out a few pertinent statements from
the hundreds of transcript elements.

Noel Salo
  Noel Salo was a teacher on sabbatical leave doing a graduate
degree in educational computing. He had completed the data
collection phase of his dissertation when he asked for a
demonstration of EntryWay. Some weeks later, somewhat to my
surprise, Salo called with some specific questions and indicated
he was already using the program. He called several more times
with questions or problems, all of which I was able to diagnose
over the phone. After he finished with EntryWay, I met with him
to get copies of his documents and to interview him about his
experiences. Of all the participants, Salo had the least support,
either in designing his documents, or in operating the program.  
Saloþs task was simple and straightforward. His dissertation data
consisted of transcripts taken from interviews with ten educators
concerning goals for mathematics education, and totaled some 250
pages of typescript. Salo used EntryWay to sort transcript
elements into 19 previously defined categories using procedures
similar to those used by Stone. This took approximately 20 hours
spread over 10 days.    Once the coded elements had been
exported, Salo used a word processing program to read, print,
search and manipulate the categories. Because he was familiar
with the material, he found it necessary to see only a quarter of
the categories in print and read the rest from the computer
screen. To find particular segments in the word processing file,
Salo used the FIND function built into the word processor while
moving between several files, each viewed through a separate
window on the computer screen. Once items were located, they were
cut and pasted into the dissertation. Salo used this technique to
create a simulated panel discussion among his informants. When
asked whether this task could have been accomplished more easily
in EntryWay, Salo replied that EntryWay was too slow and that he
hadnþt learned enough about the program to do the required
operations, such as keyword searches.
  Before EntryWay came along, Salo planned to print out his
transcripts, cut them up and thumb tack them to the walls of his
apartment. He was happy to have avoided this. To sum up his
experience Salo said þI found it very valuable in organizing the
paragraphs from my transcripts into themes.þ While Salo found
EntryWay to be an effective tool for accomplishing the simple
straight forward segmentation and sorting of his interview
transcripts, it did not seem to present him with any new or
different insight into either his task, or his data.
Dr. Fiona Shaw
  Dr. Shaw held a position in a School of Education where she was
involved in a study of a mathematics classroom. She was
replicating in the 7th grade her previous work from a 5th grade
class on how functions and graphing are taught and how teachers
use instructional representations. Shawþs data consisted of audio
transcriptions from video tapes of seven math lessons. She
planned a four step analysis process: (a) Code the transcript
elements by þactivity structures.þ That is, the kind of activity
taking place: assignment, development, shared presentation,
enrichment, guided practice, review, seat work, management,
closure, etc.; (b) Prepare a þcontent outlineþ and cross
reference this with the activity structures; (c) Read the
prepared transcripts for interesting features including:
comparison of the presented content to the received content,
tracing differences in the use of a particular activity
structures across different lessons, and pulling out all of the
ideas being presented using the teacherþs own words; and (d)
Represent the information from the first three steps as a
semantic network.   It was in the last step that Shaw found the
most interesting application of EntryWay. She preferred to work
with her data in visual forms and so our direct purpose became to
set up a document where Shaw could manipulate EntryWay map of the
content and processes in the lessons she was investigating.
  As I had done for Stone, I prepared an EntryWay document,
LESSONS, to contain Shawþs transcripts. And also as with Stone we
worked together on most occasions, with myself at the keyboard,
and Shaw working from printed EntryWay threads. In part this
arrangement was driven by the need for efficiency since it was
difficult to schedule time on the communal computer we found it
necessary to employ. We used this computer because of its power,
and also because Shaw planned to have some of the coding work
done by graduate students and this was the only machine with open
access. This part of the plan collapsed, however, when Shaw was
unable to get enough students, or enough money to pay them, and
we were pressed to move forward faster than the students could
work. However, we continued to use this machine for some time.  
After we had coded one lesson (called Lesson A) for activity
structures and content, we began our first experiments in map
making. Our first effort, the Concepts A map (*Figure 3) was
intended to show how the content of the lesson was distributed
among the transcript elements. 


*Figure 3.
ConceptsA Map

  EntryWay maps
represent nodes by
small fields
containing node
titles, and show link and thread relationships by lines drawn
between the fields. Fields can be added to a map one at a time,
or in groups with commands such as þAdd all members of thread A,þ
or þAdd any node linked to any node already present.þ The fields
are placed randomly on the screen and then moved into some
meaningful arrangement by the author. The size, font, and style
of the text can also be adjusted. Pointing at and clicking on a
field allows readers to move to that node.   Lines showing link
and thread relationships are drawn automatically between
associated fields, a thin line showing links, such as from A30 to
A31, or with a thick line drawn from item to item of a thread
list, as with the line from the þcommunicationþ thread to each of
its members, A22b, A23, A26, A52, etc.
  Map making thus is an iterative process of adding node fields,
and then moving them about the screen, unscrambling thread and
link lines, as some meaningful pattern evolves. As we worked with
ConceptsA it quickly became apparent that no such pattern was
likely to emerge since the thread lines were always impossibly
tangled. I thought the map useless, but Shaw claimed ConceptsA
was þimmensely helpful:þ       The reason this is helpful is that
I find this kind of data analysis      overwhelming. Itþs very
easy to get lost in your data, and the connections      are what
I'm most interested in. I'm trying to overcome the chronological  
   organization thatþs implicit in this type of a transcript.
This is very helpful      in beginning to rearrange and
reorganize the data.

     I think the links will begin to tease out patterns and the
threads will help      me decide whether these are the
appropriate categories, because again,      these are categories
that are going to emerge from each transcript. If I find     
that may not be the most powerful way to think about a particular
lesson,      then its time to take a look at that category.

  Because of the difficulties with ConceptsA I rewrote the
EntryWay mapping software so that thread membership was shown by
lines radiating from the thread field. Shaw dubbed these as
þConesþ. By the time Iþd finished, Shaw had some new ideas about
the data:
     I'm fairly certain I want to do an analysis of questions she
asks, because      the questions she asks are very rich, they
serve many purposes, and theyþre      very provocative. One
analysis of the questions is [to ask] where do the      questions
happen, in which activity structure so to speak. I was thinking
the      activity structure map would have the different activity
structures, what      ever they are, and then I would have linked
that to all the questions that      happened in each activity
structure.
  
  So, we created threads for different categories of questions
(e.g. JustifyThinking, Management, OpenEndedKnowledge,
OpenEndedProcess, OpinionInference, OpinionJustify,
RightAnswerInference, RightAnswerKnowledge etc.), and began
coding the elements of the A and B threads by these categories,
as well as for activity structures and content.
  At this time Shaw began working on LESSONS alone at her home
away from her office where she was constantly interrupted. She
found that even though her home computer was much slower than the
office machine, it was sufficient for map making where there are
long intervals when the software operates alone. At home she was
able to do other work while waiting on the computer, and found
the difference between a 5 minute gap and a 20 minute gap
unimportant. By the time of our next meeting, Shaw had created
question threads for the second lesson, B, and had it partially
coded. Together we finished coding B and began QuestBMap (*See
Figure 4). This work was my last substantial contribution to
LESSONS.
  A change took place in Shawþs thinking about maps at this
point. Beyond serving as devices for her to think about the data,
they could also be used to communicate:      Another way to think
of it, Qrankin is right answer knowledge questions      which are
a big part of everyone's teaching. So it might be worthwhile to   
  have one picture establishing that point: Yes this teacher is
like lots of      other teachers, she asks lots of questions.



*Figure 4.
QuestBMap

  QuestBMap is
organized with
transcript elements
containing teacher
questions scattered along the diagonal and its sides of the map.
Threads representing activity structures (e.g. ASreview, ASDevA,
ASdevB, etc.) are located above the transcript elements with
cones drawn down to their members. Threads for the different
types of questions are shown below the elements along with their
cones.   Shaw drew a number interpretations from this map:
     I realized in looking at the map that Warm and Review
weren't different      [categories], that really theyþre the same
thing. When a teacher is      reviewing content, whether the
purpose is to get kids ready for the day or      to close up the
day, that to me is the same thing in this situation. By seeing    
 those two separate cones I realized, wait a minute I really
don't think      Warmup and Review are different, I think theyþre
the same thing. 
     Just seeing the cones evolve says something. This teacher
tends to move      from one activity structure to the next. Sheþs
not jumping around back and      forth between the activity
structures, which to me indicates this teacher is      real
together for her management and her design of classroom
instruction. 
     The other nice thing I like about the maps is that I have
this chronological      trace of the questions.

     Whatþs intriguing now when you look to the other side of the
map is that      the types of questions she asks vary quite a bit
from one activity structure      to the next. Itþs not like she's
only asking one kind of question. ... Let me      make an
exception to that. During the review time she is asking these
right      answer knowledge questions, what in Bloomþs taxonomy
would be called      lower level questions. Thatþs pretty much
the kind of questions sheþs      asking during this review. Her
purpose here is to go over some concepts      and some procedures
the studentþs have already learned and so she wants      the
right answer and she wants to go through it. She sticks to that
kind of      question there pretty much all through her review.
... She ask these right      answer knowledge questions through
out the lesson and we find that the      open ended questions are
scattered throughout the lesson as well. 
     What the map makes me want to do is kind of highlighting
areas I want to      go back into the transcript and look at
again, or even into the video tapes. 
     My hunch is that weþll see a real progression where these
justify your      thinking questions become more and more
dominate as the lessons      progress through the unit. The maps
will help me make ferret out that      particular hunch.

     Iþd like a map like that for all seven lessons, because I
think that will be a      real strong picture of how this teacher
breaks up a class period. 
  In summing up the usefulness of hypertext and her experience
with EntryWay Shaw said:
     What this does is confirm my hunch, my impressions of this
teachers classroom, of      her teaching, which to me is a
challenge in qualitative research. Weþre constantly      exposing
ourselves to the richness of a situation and then weþre getting
these      insights, these hunches. Often its very difficult to
go back and reconstruct all the      bits of evidence that lead
to that particular hunch or insight. With this I'm feeling     
confident now. I feel I've got a nice base to support my hunches
or my analyses of      this particular teacher.

     I'd say the maps are the most helpful to me when I get to
that stage. You      know coding data, that's not too different
from what I do with paper and      pencil. [I] sit down and break
it into elements and code things. But being      able to pull
together that coding into visual forms, thatþs real helpful for   
  me. The second way I think its real helpful, as kind of a
hidden benefit in a      way, is that it really does force you to
be systematic and to clean up your      categories. I guess I was
impressed with that as I worked with it because     
inconsistencies hit me very quickly. Theyþre not always that
apparent when      you have 40 pages of a transcript that youþve
scribbled on in the margins.      Where as in this, it was very
clear to me that my Review and Warmup      [categories] were the
same thing.

  When this study ended Shaw had only completed maps for the
first two of the seven lessons making up this math unit. She
planned to make maps similar to QuestBMap for each lesson and
used them as the basis for the analysis of her data. Shaw was the
only author in this study to succeed in conceptualizing and
executing useful hypertext maps. She was also the only author to
organize entries with independent sets of threads. With these two
accomplishments, LESSONS is the most advanced document produced
in this study.
Implications
  The experiences of these qualitative researchers elicit three
observations about the utility of hypertext for data analysis.
First, the use of hypertext burdens researchers with a new set of
interrelated demands on their skills and resources: 
 Researchers
must have adequate access to appropriate hardware and software.


    Researchers must acquire both the computer skills and the
hypertext skills      necessary to exploit the advantages offered
by hypertext, and to minimize      its disadvantages. 


    Researchers must be able to efficiently move data and data
representations      into and out of different software packages,
and across the barriers      separating different media. 


    The multimedia capabilities of hypertext editing systems
require      researchers to acquire multimedia production and
editing skills in addition      to the traditional writing skills
that are already and always needed. 

    Because hypertext provides greater fluidity in data
manipulation,      representation, and interpretation, extra
responsibilities are imposed on      researchers to maintain
document/data integrity.

  Second, this study suggests that hypertext editing systems can
reduce the time and effort required to perform the basic
segmenting and sorting procedures that underlie many data
analysis procedures. This advantage is augmented by the fact that
hypertext furnishes researchers with computer files, rather than
the piles of paper scraps and note cards, that can be easily
manipulated by other computer applications such as word
processors, data bases, and presentation graphics programs.   
Third, researchers in this study reported positive changes in
their analytic methods. They believed hypertext allowed them to
be more focused, systematic, and organized. Further, the
flexibility of hypertext allowed them to maintain a larger
perspective than was ordinarily possible during data analysis.
These observations suggest the possibility that hypertext may at
some point in the future, significantly alter the methods and
expectations of qualitative research.
  Research continues on the applications of hypertext in
qualitative research. This work includes improvements in the
editing software, the further development of analytic techniques
appropriate for hypertext, the integration of hypertext based
processes with other analytic procedures, creating training
materials, and investigating the cognitive states involved during
the use of hypertext.
*Figures were not compatible with the electronic publishing
methods used.  Figures are available from author on request

References

Apple Computer, Inc. HyperCard [Computer Program]. Cupertino, CA:
Apple Computer,      Inc.

Bush, V. (1945). þAs we may think.þ Atlantic Monthly, 176(1),
p.101-108. 
Dede, C. (1988). The role of hypertext in transforming
information into knowledge. In       W.C. Ryan (Ed.), Proceedings
of the National Educational Computing Conference      '88 (pp.
95-102). Eugene, OR: International Council on Computers for
Education. 
Goetz, J. P., & LeCompte, M. D. (1984). Ethnography and
qualitative design in      educational research. San Diego:
Academic Press, Inc. 
Fielding N. G., & Lee R. M. (Eds.). (1991). Using computers in
qualitative research.       London: Sage.

Horney, M. A. (1991). Case studies of authoring in hypertext. 
Unpublished doctoral       dissertation, University of Oregon,
Eugene, Oregon.

Nelson, T. H. (1987). Literary machines. South Bend, IN: The
Distributors.