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Purpose
This unit is intended to acquaint the user with DtSearch's searching function. It provides a method for efficiently searching a qualitative database.
Instructions
Once a textual database has been created and coded, how should a user begin accessing the data? It may be useful to start the search on a text database by defining specific research questions. 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 has been collected on the symptom: fever (a simple term)?2. What information has been collected on types of (or "definitions of") diarrhea (a simple category, containing many terms)?
3. What information has been collected on what people believe are the causes of diarrhea (middle-level abstraction)?
On Form 3.1, record all the different research questions to be answered using the qualitative database. At the moment, do not worry if there is "enough" data on the subject or not. The questions should be generated as a kind of "free-list" without worrying about moving for less complex questions to more complex questions. This form should be completed by all interested office staff and other concerned outsiders.
When the research staff has written down appropriate research questions, review their list and write down a master list of questions on Form C3.1. This master list should be ordered by level code, starting with simple terms. It will now be possible to plan out the search strategy.
The method 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 questions cannot be answered by the materials in the database, it makes no sense to move on to more complex questions.
Searching Strategies
1. 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)
All the searcher needs to know is the English word and appropriate local words for the term.
2. For a simple category search, use combinations of words as they appear in the text. This also works especially well with nouns. For example, to find information on the concept "diarrhea," an appropriate search term might look like this:
(diarrhea and disaa or shit or gu or dysentery)1
1 Suggestion: Write macros to allow for quick constructions of certain kinds of search terms. For instance, a search for information on breastfeeding might require a search expression like: (BREAST* or BM or DUDH or MOTHER'S MILK).
The searcher in this example needs to know all the English words and local words for the category.
3. For a middle-level abstraction search. Use combination of words as they appear in the text, although codes may be needed as well. This works with nouns and verbs, usually in combination. For example, to find information on the middle-level abstraction "causes of diarrhea," an appropriate search term might look like this:
(diarrhea or disaa or shit or gu or dysentery) w/10 (cause* or get* or make)
The searcher needs to know all the English words and local words for the terms/categories and how people talk about them. Especially, what types of expressions/verbs do people use when they talk about diarrhea and its causes?
4. 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 high level abstraction "pluralistic illness explanatory system," an appropriate search term might be:
(# Pluralistic Illness Explanation #)
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. Here also the code book is valuable, especially if the person entering or coding the data is not the "searcher."
Under the finalized research questions, write out likely search terms in the space provided. It should be possible to write at least 23 different ways of approaching each question. Feel free to experiment during the searches to see what works and what does not work.
FIGURE 2
Flow Chart for Managing Qualitative Data
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Process |
Tool Used |
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RAW FIELD NOTES |
Hand-Written/Tape Recorded |
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EXPANDED FIELD NOTES |
Either expanded into notebooks and later entered into WordPerfect; or expanded directly into WordPerfect (computer files) |
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SPELL-CHECKING |
WordPerfect |
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REVIEW OR EXPANDED FIELD NOTES & DERIVATION OF PRELIMINARY CODES |
WordPerfect and WordPerfect Macros |
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CODING OF EXPANDED FIELD NOTES |
WordPerfect Macros |
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INDEXING EXPANDED FIELD NOTES AND CODES |
ZylNDEX, DtSearch |
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SEARCHING THROUGH EXPANDED FIELD NOTES AND CODES |
ZylNDEX, DtSearch |
FORM C3.1
Summary of Research Questions and Generating SEARCH Terms
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Summarized Research Questions |
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ANTHROPAC
Analytic Technologies
Tel/Fax: (803) 783-0603
DtSearch
Dt Software, Inc.
2101 Crystal Plaza
Arcade, Suite 231
Arlington, VA 22202
Tel: (703) 521-9427
Fax: (703) 521-6140
ZylNDEX
ZyLAB
100 Lexington Drive
Buffalo Grove, IL 60089
Tel: (708) 459-8000
1-800-544-6339
Fax: (708) 459-8054