Social scientists are increasingly asked to contribute to action-research projects using their skills as qualitative
researchers. The large volume of textual data produced by even a fairly small qualitative study poses special
problems in terms of data management. Recent advances in the use of microcomputers for qualitative data
management and analysis provide at least a partial solution. The paper describes an effective method for
managing qualitative data on microcomputers using textual data management software and incorporating
appropriate coding procedures.
Dr. Joel Gittelsohn (Ph.D.) is working in the Division of Human Nutrition, Department of International Health,
School of Hygiene and Public Health, The Johns Hopkins University, 615 N. Wolfe St. Baltimore, MD 21205.
Social scientists participate in action-research projects in various capacities: as an operations
researcher in an urban teen pregnancy project, an ethnographer in an AIDs intervention, as a
social worker for a weaning intervention, and so on. In recent years, there has been a rapid growth
in the use of qualitative or semi-qualitative data-gathering in connection with health and nutrition
programmes. The insights provided by qualitative data have proven valuable for programme
planning, implementation and evaluation (Coreil et al., 1989; Bentley et al., 1990). Researchers
using qualitative methods generate large amounts of context-rich descriptive textual data
(Peshkin, 1988). For instance, social workers may develop hundreds of detailed individual case
studies. Commonly, the contribution of the social scientist in these circumstances has been as an
advisor and as a producer of reports. Commonly, in these reports, only the barest summaries of
the qualitative data collected are presented, emphasizing numbers and counts, the quantitative
aspects of qualitative data. Qualitative data presented in numeric form (usually as tabulated lists)
has minimal influence on decision-making at the policy and planning levels. It is often viewed as
poor quantitative research, based on an inadequate, unrepresentative sample. There are at least
three ways to deal with this perception and to improve the utilisation of qualitative data in
action-research projects: (1) improve data collection; (2) improve data management systems; and
(3) develop new means of qualitative data analysis (Looker et al., 1989). This paper focuses on the
second component: improving qualitative data management.
This paper describes an approach for managing and coding qualitative (textual) data using
microcomputers. The use of microcomputers in quantitative research is not new, but their use in
qualitative research is still in its formative stages (Richards and Richards, 1987; Fielding and Lee,
1991). The method presented here is especially useful for working with qualitative data gathered
as part of a multidisciplinary research/intervention project. The method is currently being used to
manage textual data collected as part of a teenage pregnancy intervention project in Baltimore, for
dietary management of diarrhoea research in Nigeria, and for NGOs working in women's
reproductive health in India and Nepal (Bentley et al., 1990; Bentley et al., 1991). Besides these
types of interdisciplinary projects, I believe that this method is also effective for individual social
scientists conducting more traditional ethnographic fieldwork, and for use with the intensive case

612 Joel Gittelsohn
study data generated by social workers. It is my contention that an organised, accessible database
of qualitative data will allow social scientists to increase their contribution to applied intervention
projects, as well as to more theoretically-oriented studies.
The Challenge of Textual Data
Qualitative research commonly generates thousands of pages of written (or "expanded") notes.
Within these notes lie interviews with key informants, transcripts of focus group sessions,
observations of behaviours and events, secondary data (such as clinic records and genealogy
charts), and the social scientist's comments and thoughts about what these data all mean
(Bernard, 1986). These diverse data present a tremendous challenge for management and
analysis. How should these notes be organised? Should they be coded? If so, in what system?
How can the investigator locate examples within the large body of textual data that will allow him or
her to confirm or reject their theoretical assumptions?
Bernard (1988) writes, "No single researcher, working alone for less than two years, can produce
more field notes than she or he can grasp by pawing and shuffling through them." This may be
true for the lone ethnographer or social worker, but as stated earlier, increasingly, qualitative data
are being gathered by teams of data collectors. If four data collectors (an investigator plus three
research assistants), produce just eight pages of notes each day for a (relatively) short three
month study, the result is over 2500 pages of fieldnotes! Not only is this a large number to "paw
and shuffle" through, but the investigator may not be entirely familiar with the data collected by his
or her assistants.
Text Management Using Microcomputers
The solution proposed here for the managing of textual data is the use of a word processing
programme (such as Word Star or Word Perfect) and a text retrieval and management programme
(such as ZylNDEX). Note that this solution requires that all textual data be entered on the
microcomputer. While this may constitute a difficult step for many individuals/organisations, the
results in terms of accessibility of data make the effort worth-while.
There are dozens of word processing programmes available. In the USA, Word Perfect is the most
commonly used programme — while in India, Word Star holds away. When selecting a word
processing programme for entry of textual data, the following features are essential: spell checking,
word searching, user-definable dictionary and some kind of macro-key function (a type of small
user-develop programme, where multiple keystrokes can be accessed with a single Keystroke).
The uses of these special word processing features will be described later in this article.
A complete approach for managing, coding and retrieving qualitative data is outlined in Figure 1.
In the next three paragraphs I present an overview of the process of managing textual data on
microcomputers, then cover specific topics in greater detail.
"Raw" fieldnotes or case history notes (i.e., handwritten "jottings" or notes made during the
actual data collection) should be typed as expanded fieldnotes on a microcomputer. Alternatively,
expanded fieldnotes can be written on paper and given to a professional word processor for entry.
These expanded fieldnotes should then be "spell-checked" for errors2. In order to use this
method, interviews will have to be translated into English. Key concepts in the local language

Management of Qualitative Data Using Microcomputers 613
Figure 1
Tool Used
Raw Field Notes
Hand-Written/Tape Recorded
Expanded Field Notes
Either expanded into notebooks and
later entered into Word Perfect; or
expanded directly into Word Perfect
Word Star or Word Perfect
Review of Expanded Field Notes &
Word Star or Word Perfect and
Derivation of Preliminary Codes
Word Perfect Macros
Coding of Expanded Field Notes
Word Star or Word Perfect Macros
Indexing Expanded Field Notes and
Searching Through Expanded Field
Notes and Codes
(such as illness terms) can be entered in a romanised format3. Careful attention should be paid to
keeping the spelling of local terms consistent.
I suggest that each data file have a unique, meaningful filename and each file represent a discrete
data collection unit: i.e., one interview, one observation of a key event, one focus group session. In
projects I have worked on, we use a format for naming files where the first two characters indicate
the type of data collected (i.e., key informant interview, focus groups); the next six indicate the date
of data collection (in year, month, day order). The first two characters of the filename extension are
the initials of the data collector and the last digit is an order number for the day (for example,
KI900711.JG1 would be the filename for the first key informant interview conducted by Joel
Gittelsohn on July 11, 19904).
These expanded notes should be reviewed, and a preliminary coding list derived. The notes
should then be coded within the word processing programme. Once the preliminary coding
process is complete, the notes can be indexed using ZylNDEX. Preliminary searches of the data
should be conducted at this point. If the investigator is still in the field, these preliminary searches
can help him or her identify "gaps" in the data (i.e. areas for continued research). The coding list
should be refined and expanded at this point as well.
How Text Management Programmes (like ZylNDEX) Work
ZylNDEX offers "full-text" information retrieval, that is, finding any word, phrase, or number or
combination of these elements wherever they occur in all your documents. ZyINDEX's first program

614 Joel Gittelsohn
function-indexing, works by creating a list of every word and number in specified text files, keeping
track of what file (or files) each word is in and where the word is located within each file. Common
words like articles and conjunctions (e.g. the, or, and ...), called "noise words", are ignored to
save space on the computer's hard disk. ZylNDEX manages existing data files without altering
them and requires no specially identified key-words. ZylNDEX can recognise a wide variety of word
processing formats (such as Word Star and Word Perfect), plus LOTUS 1-2-3, dBASE III, and ASCII
ZylNDEX second programme function, searching, uses the specially created index lists to locate
user-specified text "strings" (groups of words that the searcher desires to locate). Searches may
be refined by the use of Boolean operators (AND, OR, NOT), wildcard characters, parentheses and
a "search within" feature which lets you specify the proximity (in number of words) of two search
items. Searching in ZylNDEX is very fast. Depending on the speed of your microcomputer, the
program can search through thousands of pages of text in just a few seconds.
Once located using the search feature, important text can be marked and extracted for analysis.
Unlike some programs which pull out isolated " h i t s " , ZylNDEX permits the searcher to view the full
text, allowing the user to see the context of the "hits".
Coding Qualitative Data
What are codes? A code is an "abbreviation or symbol applied to a segment of order to
classify the words...they usually derive from research questions, hypotheses, key concepts or
important themes" (Miles and Huberman, 1984), Codes have a number of different functions
relating to textual data, including organisation, retrieval of information, assembly and reduction of
data into analysable units. As well, Miles and Huberman (1984) observe that "coding is a form of
continuing sets the agenda for the next wave of data collection". .
There are many different systems for coding qualitative data. These can be divided into three
basic categories: numeric, mnemonic and word-based systems. In any coding system, codes
represent key concepts and ideas in the text. For instance, a code may signify: "here is an
example of the expression of female power within the household". It is very time-consuming
to write that whole sentence down manually on your notes each time it is relevant to do so,
therefore, abbreviated forms (i.e., codes) are used. Numeric codes, where number represent key
concepts and ideas, are usually the most concise form of coding. It is far easier to write a number
code (i.e., 562.02) than it is to write out the whole phrase above. On the other hand, numbers are
hard to remember. It is easy for a coder to miscode textual data when numeric coding systems are
utilised. Mnemonic coding systems, where a set of letters are used to represent the key concept or
idea, are a popular alternative to numeric coding. For instance, using a mnemonic system, the
phrase above could be coded as: F E M POW HH.EXM. This kind of code makes more sense to
the coder, is easier to use, and may even be understandable to the lay user. Finally, word codes
use whole words to indicate key concepts and ideas in the text, such as EXAMPLE OF FEMALE
POWER IN HOUSEHOLD. Generally, while whole word codes are much clearer to the user
(particularly to the lay user) they have not been widely adopted because it takes too long to code
qualitative data manually in this manner.
In addition to the type of code to apply, codes can be applied to data at several levels of
abstraction. Miles and Huberman (1984) describe three coding levels, descriptive, interpretive,
and explanatory. At the lowest level are codes that describe things as they are found in the dataset

Management of Qualitative Data Using Microcomputers 615
(descriptive), at a higher level codes represent judgements by the coder about what is happening
in the data (interpretive), and at the highest level they relate important concepts according to
inferred patterns (explanatory), "pattern codes are explanatory or inferential codes, ones that
identify an emergent them, pattern, or explanation that the site suggests to the analyst" (Miles and
Huberman, 1984).
A third aspect of coding relates to the number of different codes to apply to a textual dataset. The
intensive coding schemes described by several authors (Bernard, 1988) have several deficiencies:
(1) Such coding schemes must be constantly updated by modification and/or additions to
accommodate the needs of a dynamic database, one that grows and changes as the needs
and purposes of the database change.
(2) Intensive coding schemes are not "user-friendly". Many of these systems involve hundreds of
numeric or mnemonic codes which require a great deal of time and sophistication to learn and
apply correctly.
(3) As professional/trained coders are required to learn and apply the multitude of complex
codes, increased expenses are entailed.
(4) The "accessibility" of large qualitative datasets is very low when complex coding systems
(particularly numeric or mnemonic) are utilised, especially for lay users.
Coding for a Microcomputer-Based Text Management System
Use of microcomputer-based textual management system requires that text be entered using a
word processing programme (such as Word Star or Word Perfect). However, once textual data
files have been entered on a word processing programme and indexed using textual data
management software (such as, ZylNDEX), it can still be difficult to find certain kinds of information.
For instance, a direct observation of a woman ordering her husband to buy her a dress might be a
good example of "household decision-making" in action, but how would you locate it? In ZylNDEX
every word in the file becomes a "searchable" word, but it is rare that abstract concepts will
consistently be part of the data to be located. The solution lies in the use of codes.
Developing a coding scheme can be a useful analytic and retrieval tool, but no coding scheme can
cover every possible way of looking at textual data without being impossibly large, unwieldy and
complex. I propose that while the coding of qualitative data is necessary, it should be minimised
when using text management and retrieval software like ZylNDEX.
If coding of qualitative data is kept to the minimum, the result is a more user-friendly and
accessible database. A reduction in the number of codes improves intra- and inter-coder reliability.
As well, there can be a tremendous saving in time and money. The coding method described
below allows non-social scientists to maintain and access a textual database.
Components of a "Minimalist" Coding System for Microcomputers:
1. Use a word-based coding system instead of numbers or mnemonics to reduce confusion.
Word codes are more likely to be understood by more people.
2. Do not use words that already appear in the text, as they can be easily searched by text
management software, such as ZylNDEX. Thus, lower-level "descriptive" codes are usually
not necessary.
3. Codes should be used for concepts at high and occasionally medium levels of abstraction
(explanatory and interpretive).

616 Joel Gittelsohn
4. While only a rough guideline, a good minimalist coding system should have less than 50
codes. Expansion of the system may be required as the research grows to cover new areas of
5. If required, a word-based coding system can be tied into other coding systems. For instance, a
word-based system tied into Murdock's (1971) Outline of Cultural Materials coding system
might yield the following hybrid code:
[#STATUS OF WOMEN—562.02#]
Figure 2 gives an example of a coding system developed by an Indian NGO for use with a
qualitative dataset on women's health.
Figure 2
How to Apply Codes to Textual Data Using Microcomputers
All textual data should be entered on some type of word-processing programme. Codes should be
clearly marked as separate from the textual data. I recommend making codes appear different
from regular text by capitalising them, boldfacing them, putting them in brackets and by adding a
symbol on each end of the code (for instance, [#STATUS OF WOMEN#]). The extra symbol allows
you to search exclusive or inclusive of the code. For a limited number of codes, it is possible to
"hot-key" a number of codes — wherein codes are inserted into the text in response to one or two
keystrokes. For instance, in the WordPerfect software programme, macros can be developed
using the "alt" key in combination with a letter to print the code. This saves a lot of time in the
coding process, and is more rapid than manually entering numeric or mnemonic codes. In terms of
location of the codes, I recommend placing the codes immediately following the text they relate to.

Management of Qualitative Data Using Microcomputers 6 1 7
That way proximity searches are likely to be most effective (see section below). Locating
appropriate places to insert codes can be a lengthy process involving reading and rereading
fieldnotes. ZylNDEX can be used to hasten this process, by permitting rapid searching for "coding
points". For an example of how this system for coding would be used, consider the excerpt from
an interview conducted in an Indian village (Figure 3).
Figure 3
J # male anthropologist G # female research assistant
K# village woman O # K's husband
[11:20 am]
[K covers head with sari when father-in-law comes into courtyard. He has brought laddu (sweets)
to give to the children.]
J : Do women get khamjoori more than others?
K : Yes...females are themselves weaker than men... as they are weak, they can become more
weak. [#FEMALE STATUS*] [K rubs her head]
G :Do you have a headache?
K : Yes, f do, constantly.
G :Are you treating it some way?
K : Nothing right now...but if I have ghee then I can put dakhani merchi (black pepper) and elaichi
powder in it and make small balls (to be eaten).
K : [to O, orders]: Go and borrow money! As I am busy with these people.
[she has told husband to borrow money to take her to a private doctor]
[11:30 am] [O cutting sugarcane, offers some to G and J]
G :What were you talking about with your husband?
K: I have told my husband it is better to go to the government hospital, but our neighbours advised
us to continue previous treatment.
February 29, 1992
How to Search a Textual Database
Once a computerised textual database has been created and coded, how does the user begin
accessing (or "searching through") the data? I recommend that you start your search on a text
database by defining your specific research question(s). There can be several levels of complexity
in the question: simple term questions, simple category questions, middle-level abstraction
questions, and high-level abstraction questions. Here is an example of each type:
1. What information do we have on the symptom, "fever" (a simple term)?
2. What information do we have on "diarrhoea" (a more complex category, containing many

618 Joel Gittelsohn
3. What information do we have on what people believe are the causes of diarrhoea (middle-level
4. What information do we have on the overall complexity of the local medical system (high-level
abstraction)? For instance, do people commonly use more than one type of health provider?
The procedure for searching the indexed database would differ depending on the type of research
question asked. As a general rule of thumb, it makes sense to start with the simple term and
simple category types of questions. If these basic questions cannot be answered by the materials
in the database, it makes no sense to move onto more complex questions. In the following
examples, search "requests" are written in the syntax preferred by ZylNDEX.
For a simple term search, just use words as they appear in the text. This works especially well with
nouns. For example, to find information on the concept "fever", the appropriate search term might
look like this:
(fever or joro)
The simple search shown above would retrieve all files that contain either the word fever or the
word joro6. All the searcher needs to know is the English word and appropriate local word for the
For a simple category search, use combinations of words (usually synonyms) as they appear in
text. This also works especially well with nouns. The format for a category search is: (synonym1 or
synonym2 or synonym3 ... or localterm1 or localterm2 or localterm3...). For example, to find
information on the concept "diarrhoea," an appropriate search term might look like this:
(diarrhoea or faeces or dysentary or gu or disaa)7
The searcher here needs to know all the English words and local words for the category. ZylNDEX
has several special features (thesaurus, wild cards, access to indexed word lists) to assist the user
in determining the available synonyms for a particular key concept or term.
For a middle-level abstraction search, again use combinations of synonyms as they appear in text
(although codes may be needed as well). This type of search usually takes the form of a set of
noun synonyms matched with a set of verb synonyms, usually linked by a proximity term. The
format for a mid-level abstraction search is: (synonym1 or synonym2 ... or localterm1 or localterm2...)
w/#(synonym1 or synonym2 ... or localterm1 or localterm2). For example, to find information on
the middle-level abstraction "causes of diarrhoea," an appropriate search term might look like
(diarrhoea or faeces or dysentary or gu or disaa) w/10 (cause* or get* or make)
The search above requests that all diarrhoea-related terms within 10 words of any cause-related
term to be retrieved. It is crucial that the searcher needs to know all the English words and local
words for the terms/categories and how people talk about them. Especially the types of
expressions/verbs which people use when they talk about diarrhoea and its causes.
For a high-level abstraction search, it is likely that codes will be required, perhaps in combination
with nouns and verbs. For example, to find information on the high-level abstraction "pluralistic
illness explanatory system," an appropriate search term might be:
[#PLURALISTIC I L L N E S S E X P L A N A T I O N * ] w/10 (diarrhoea or faeces or dysentary or gu or disaa)

Management of Qualitative Data Using Microcomputers 6 1 9
In the preceding search request, any use of the code [#PLURALISTIC ILLNESS EXPLANATION*]
within 10 words of diarrhoea-related term will be retrieved. The searcher needs to be familiar with
the coding system as well as all English words and local words for the terms/categories and how
people talk about them.
At the very foundation of the process for managing textual data described in this paper is careful
attention to producing good expanded fieldnotes. This cannot be overemphasised. ZylNDEX
makes practically every word in each one of your data files a "key word," so the more attention
you pay to getting it all down accurately, the more effective and efficient your searches will be.
Multi-disciplinary research and intervention projects could make good use of this microcomputer-
based method for managing and accessing their qualitative data. A detailed qualitative database is
a valuable resource for an organisation. Unlike quantitative data, a qualitative database does not
require the assistance of a statistician or programmer to produce useful information. Certainly,
care must be exercised that unfounded conclusions are not drawn on the basis of inadequate use
of the dataset, but this is a problem that is common to quantitative datasets as well.
A qualitative database provides a number of attractive features to organisations, including its great
depth and breadth of information, approachability and expandability. As organisations evolve and
desire to investigate new areas of concern, they are likely to find that their qualitative database
supplies a lot of information on topics they had not expressly investigated before. This is because
the qualitative research approach emphasises contextualisation. Beliefs and behaviours do not
occur in isolation, but are part of larger patterns of interlinked social and cultural features. But
these patterns are complex and difficult to tease out and describe. A microcomputer-based
management system is an effective means of organising and accessing qualitative data, bringing
out its full contribution for action-research projects.
1. I have reviewed several other textual management programmes (e.g. Ask Sam, Word Cruncher, GOFER,
etc.), but have found ZylNDEX optimal for the purposes described. The programme satisfies the majority of
criteria laid out by Wellman and Sims (1990) for textual data management software.
ZylNDEX operates only on IBM and IBM-compatible machines. Users of Macintosh microcomputer system
have some software available for textual data management, including NUDIST (Non-numerical Unstructured
Data Indexing Searching and Theorizing) and Hypersoft.
2. Most word processing programmes today have a spell-checking feature.
3. While it is not possible at this point in time, it may soon be feasible to use a special computer "card" (such as
TRANSCRIPT) so that qualitative data can be entered and managed in the local language directly without
translation into English.
4. The advantage of this particular file-naming system is that files can be easily sorted by type and date of data
5. Words or combinations of words in indexed files that meet the user's search criteria.
6. The Nepali word for fever.
7. ZylNDEX has a feature which permits you to write macros to allow you to quickly state certain kinds of search
terms. For instance, a search for information on breastfeeding might require a search expression like:

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