- Email: [email protected]
The use of a relational database in qualitative research on educational computing
C0ntpa~s E&K. Pnnrcd III Great
Vol. 15. No. i-3. pp. 213-210. Bntain. All rights resened
1990 CopyrIght
5
0360. I3 I 5 99 53 00 + 0.00 1990 Perpamon Press plc
THE USE OF A RELATIONAL DATABASE IN QUALITATIVE RESEARCH ON EDUCATIONAL COMPUTING LAURAR. WINER and MARIO CARRI~RE Centre Quebecois de Recherche sur les Applications Pedagogiques de ~~rdinateur, 2001 Boulevard St-Laurent, Montreal. Quebec, Canada H2X 2f3 Abstract-This paper discusses the use of a relational database as a data management and analysis tool for non-experimental qualitative research. The relational database is Reflex Plus (Borland Int.); the research project is the Vitrine 2001, a project devoted to the study of computer-based learning environments, which uses participant observation and semi-structured interviews as the main data collection methods. The Vitrine research is centered around the Ion~tudinaI observation of actions and processes; in this context, a needwas identifiedfor computer-based tools to help in the data management and analysis process. The problem with constituting such tools, particularly in an explorative and qualitative context, is that one cannot establish a priori the data management and analysis needs.
INTRODUCTION
The potential of using computers to assist with qualitative data has been recognized for more than 20 years[l]. The advent of microcomputers has made the potential more accessible for most researchers. In 1984, a special issue of Quafitatiw Sociology [2] was devoted to computers and qualitative data, and in addition to the ten articles, the annotated bibliography of twenty items[3] attests to the growing interest. Text processing has been the most common type of application; however, this tool only partially addresses the problems of organizing, classifying, and manipulating the quantity of data generated in most qualitative studies. This is not to dismiss the Super Typewriter” use of computers as text processors; however, we feel that computers have the potential to play other roles, specifically, that of a “Super Filing System”. In order to be of maximum use to researchers working with qualitative data, computer tools should help not only with the mechanical acts of storing and printing data (text processors), but shouid also be able to help with organizing, manipulating, cross-referencing, and --walking through” data, both one’s own and those of fellow researchers, while providing different perspectives on the data or specified subsets. As has long been known in data management, one cannot approach the problem simply by asking the user what information will be needed[4]. First, the user’s perceived needs are often indete~inate; second, even if one could establish all of one’s perceived needs, those needs will evolve. Miles and Huberman[5] describe in some detail methods for qualitative data analysis. The proposed systematic approaches to finding meaning in a set of data are supported by various tactics for the specific tasks of generating meaning and testing or confirming findings. These tactics require that researchers be able to classify data under a variety of headings, link them to related data, search, display, count and group data according to a variety of criteria, as well as search for data that are not present. INFORMATION
SYSTEMS
These considerations led us to take the approach used in computer and management science known as information systems. An info~ation system has as its goal to create and conserve a representation of an organization’s activities in order that the actors within that organization can study and control its behaviour[6]. An information system consists of the ensemble of human resources, physical resources, and methods used in processing information within an organization[7]. The principal component of our information system is a database, selected because a database is essentially a computerized record-keeping system, i.e. a system with the basic function 213
24
LAURAR.
WISER
and
MARIO
CARRI~RE
of maintaining information and making that information available on demand[g]. It is important to realize that the database is one component of an information system, not THE information system in its entirety. For example, other components of our system are the collective memories of the researchers, written notes, photos, computer programs, and children’s productions. These other components are all cross-referenced in the database, but are not entirely contained within it. To try to computerize all aspects of an organization will not only hinder serendipity and intuition, but also inevitably lead to inefficiency at best, and incoherence and incomprehensibility at worst. Once the decision to have a database as the key component of our ~nfo~ation system had been taken, the next step was one of implementation. This is not a new problem. As Tardieu ef a/.[61 describe, the work of the ANSI/SPARC Committee (published in 1977) addressed the difficulties involved in going directly from a perceived reality to a database. The process of structuring a database is one which significantly benefits from intermediate steps or levels. The principal idea that we adopted from this work is the importance of separating one’s information needs from any technical constraints imposed by specific software. In order to arrive at this separation, we used the intermediate step of creating a conceptual model. In fact, this step of creating a conceptual model is in and of itself a useful activity and is often recommended in qualitative research [5]. Articulating the essentials, identifying the elements, and defining the relations is a process that forces the creation of a clear, external representation of what is often, especially in qualitative research, a complex soft system[9]. However, the utility is clearly increased many times over if this representation can be implemented in a dynamic form which allows for interaction between the model and the researcher. The tools created must therefore be as powerful, simple, responsive and supple as possible. The use of a relational database was seen as offering a modular and extensible external memory, open to contributions from individual researchers, and readily accessible to their queries. Using the Vitrine* project to illustrate, Fig. 1 shows the information flow between the activity under study (i.e. pupils’ visits to the research site), the researchers, the pupils, and the info~ation system while the activity is underway, as well as after the activity has finished. During the course of the research, information goes into the information system from the research activity. The information system channel becomes two-way with the researchers after the activity has finished (i.e. ceased to exist) as it then serves as a memory, allowing a replay of the “reality” as observed and fed into the system by the researchers. Any recording tool could simply replay the reality; the significant advantage of a computer-based information system, especially one which uses a database, is that data contained within it are structured in such a way that the user can search for specific information with the aid of a structured query system. DATABASES The main function of databases in information systems is to store and retrieve information. In order to perform this task efficiently, database systems typically try to reduce data redundancy so that people are always working on the same physical data. This has two major advantages: (1) it reduces the volume of data by reducing repetition; (2) it ensures that modifications made to data are immediately transmitted throughout the system [8]. Step I-buildiirg
a conceptuaf model
As stated above, the first step in implementing a database in a qualitative research project should be to build a conceptual model of the research environment. We adapted the MERISE method for database design [ IO), to design a model of the “reality’* in question, the Vitrine 200 1. This model is composed of stable entities, concrete or abstract, e.g. people, places, things, statements, and the relationships between them[6]. The entities and relationships are neutral; they do not contain inherent value judgements such as success/failure, liked/not liked, etc. (See Fig. 2-Conceptual Model. In order to aid readers who are unfamiliar with this form of notation, we will give a brief *The VitrineZOOl[13Jis an on-going projectofAPO QUEBEC, the Quebec Center for Research in Educational Computing, a non-profit Quebec government corporation for applied research and development in educational computing.
Fig. I. Information
Pupils
Reseomh Actlvhy
flow between information
Environment
system, research activity
Researchers
and researchers.
(a) During
research activity
and (b) after research activity has linished.
Researchers
J
- PmdUetPnr
Fieptr - Pmgramr
\
-rI
LAURA
R. WISER and MARIO CARRI~RE
APO
O&bec
- Vltrme
Conceptual
Configuration
2001
model
1.3
Configuration -name
Hardware
I-----,
0
Object Rclotion
+
A PIoDerty
Oneto
one relation
One to many relation Key characteristic
Fig. 2. Initial version of conceptual model of Vitrine 2001 project (1987-1988).
“walk-through” of part of the model. Take for instance the entities Teacher and Pupil which are linked by the relationship Teaches. Teacher to Teaches has the cardinality 1,n. This means that one Teacher can teach many Pupils. Pupils to Teaches also has the cardinality 1,n. Therefore, one Pupil can have more than one Teacher. If this cardinality were to be changed to l,l, it would mean that one Pupil could have only one Teacher.) The important fact to remember is that each conceptual model represents one view of reality. Clearly, conceptual models of the same “reality” may differ according to their authors; no one is more or less correct in absolute terms. As well, as our view or understanding of a system changes, our model may be modified to reflect those changes. Step 2-implementing
the model
Once a useful conceptual model has been designed, it is relatively straightforward to implement this in the form of a relational database. We used Reflex PlusTM(Borland Int.)* on a MacintoshTM with 2 Mbyte of RAM. Reflex PlusT” was chosen because of its simplicity, user interface, price, and the technical support available from the producer. Our database contains information on the co-researchers (pupils, observers, animators, teachers, visitors, etc.), the equipment, the software, their relationships within the environment, as well as comments and observations (see Fig. 3 for the conceptual model as implemented in Reflex PlusTM).
*Although Borland Int. claims that Reflex Plus[l4] is a relational database, strictly speaking, it is not. According to the generally accepted definition[8, pp. 320-3221 a relational database should not have predefined access paths to support database manipulation. Reflex Plus TMviolates this condition by having data structures called “links” that are used expressly to manipulate data. This, however, does not diminish its usefulness or power for our purposes.
RelatIonal
database
Fig. 3. Initial
in educational
logical
research
217
model.
What is extremely important to note is that in the data entry process, Reflex PiusTMautomatically manages the links; once the objects are entered into the database, the links between the entities (i.e. the relationships) must only be indicated once. In this way, changes are automatically reflected throughout the system and the amount of data is reduced. The advantages of a database are clearly exploited to minimize data redundancy while maximizing their consistency and accuracy. Figure 4 provides a representation of the data management performed by the system; i.e. the automatic creation of links and subsequent sharing of data. RESEARCH
The database as implemented for the 1987-1988 Vitrine project contains information on 19 primary-school children who visited the Vitrine 23 times for 2.5 h per visit. Approximately 60 different computer-based activities were available for them to choose from. Data gathered by four researchers consist of observation notes, reports filled out by the children, children’s productions and computer programs. To give an order of magnitude, this consists of approx. 1700 pages of data which translates into 1.2 Mbyte of data in Reflex PlusTM files. While in no way minimizing the benefits derived from creating a conceptual model of the research environment, these benefits do not help in the problems of organizing, classifying and manipulating the data once they are collected. As discussed above, there are established approaches for qualitative data analysis. How, then, does our “super filing system” help in applying these tactics? Mechanical acts such as counting frequency of events (e.g. how many times did two children work together and on what activities?) would clearly be a daunting task if physically looking through the stacks of paper were the only method available. This is a trivial query in our system. Criteria for counting can be based on any of the fields in the database. However, the query system is not limited to such simple mechanical acts. Searches can be done which check for links between entities (e.g. how many times, when and where did two or more children work on the same activity?) or include scanning free text fields for included text (e.g. what activities are described by the child with the word ‘fun’ or ‘boring’?).
LAURA
R. WISER and
-
Links created by the researcher
- _ . .
Links created by Reflex
MARIO CARR&RE
I
Plus
Fig. 4. Representation
of data management.
In order to note reIations between variables, see plausibility, build a logical chain of evidence, or apply the other tactics for generating meaning, it is often useful to be able to reorganize and represent data in different formats. The database can be conceived of as an entailment mesh [l 1,121 or “fishnet” which allows for any of the entities or “knots” to be taken as the starting point. For example, children’s and/or researchers’ comments may be presented by child, activity, visit, hardware configuration, or any other variable. One child can be followed by extracting everything relating to him or her (partners, notes, productions, etc.) over all visits. For testing or confnming findings, many of the same capabilities of the database are useful. Additionally, the ability to search on the absence of particular data, e.g. x is NOT present, becomes important in looking for negative evidence as well as checking out rival explanations. One tactic which is often cumbersome is that of triangulation; however, because the database either contains or points to different kinds of data from different researchers, it facilitates exploring independent paths to the same conclusions. As well, consistently contradictory observation notes from different
Relational database in educational research
researchers may indicate researcher bias, or incomplete conflicting evidence from different sources.
CAUTIONARY
explanations
219
may be indicated
by
NOTES
While the benefits of working with Reflex Plus TM have been enormous, and have, for us, far outweighed the difficulties, it is important that these difficulties be stated clearly. The first major problem, logistics, is to a large extent specific to our hardware and software, as well as to the scope of the research project. The absolute quantity of data is an important factor, and this must be evaluated for each research project. The technical problems (amount of data and processing speed) would clearly not have been issues if we had been working with more sophisticated software and hardware. As it is, we have had to upgrade our Macintosh Plus to its limit of 4 Mbyte, and its processing speed leaves something to be desired when performing complex queries. The next move is either to step up to a Macintosh II, or invest in a minicomputer and change our software completely. While either of these solutions is practicable, the resource investments required (both monetary and human) increase significantly and must be considered seriously before undertaking either. A second problem is not specific to our implementation, but rather to the conceptual and technical problems inherent in any such undertaking. The services of a database design consultant are extremely useful in the set-up phase to aid in the modeling of the environment and the implementation of the conceptual model in whatever software is chosen. Additional technical support may be required during the early stages, depending on the familiarity of the users with the software, its complexity, and the complexity of the database design. This may be provided either on-site, by the vendor, or through a combination of both. CURRENT
ACTIVITIES
This approach to data management in our work has carried on into the second year of our research project. The set-up phase over and all the “bugs” out, the researchers are now independent of the computer scientist in the on-going work. The conceptual model undergoes periodic revisions to better reflect the reality of the Vitrine project as it evolves, and our deepened understanding of it. Up to now, these refinements have been relatively easy to incorporate. The utility of the system to respond to our requirements as researchers is also continually being tested. Certain elements are lacking in the system as it currently exists; some can be and are being modified, others are software limitations and may prove to be determiant factors in how far we can go with the current installation. CONCLUSION It is still too early for us to draw any final conclusions about the impact on the research process itself of the use of a database, but this aspect must not be neglected. However, it is clear to us that there are enormous benefits for researchers to be gained from the facility with which one can manipulate data, as well as from the structure one is obliged to create to store these data. However, as much as we may look to computer tools to help in qualitative research, it is important to remember that as of yet, computers do not understand data, they only manipulate them. Data in a computer does not have any “meaning”, the meaning, like beauty, is and probably always will be in the eye of the beholder. REFERENCES 1. Stone P. J., Dunphy D. C., Smith M. S. and Ogilvie D. M.. Analysis. MIT Press, Cambridge,
The General Inquirer: A Computer Approach ro Content
Mass. (1966). 2. Conrad P. and Reihnarz S., Qual. Social. Special Issue: Computers and Qualitative Data 7, No. l/2 (1984). 3. Dennis D. L., “Word Crunching”: an annotated bibliography on computers and qualitative data analysis. Qual. Sociof. Special Issue: Computers and Qualitative Data 7, No. l/2, 148-156 (1984).
220
LAURAR. WISER and MARIOCARRI~RE
4. Ackoff R. L.. Management misinformation systems. ,bfgmr Sci. 14, No. 4, 147-156 (1967). 5. Miles M. B. and Huberman A. M., Qualiratire Dofa Analysrs: A Sourcebook of 1Vew,Werhodr. Sage. Beverly Hills, Calif. (1984). 6. Tardieu H., Nanci D. and Pascot D., Conception d’un Sysf&ne d’In/brmnrion. Gaetan Morin. Paris (1985). 7. GALACSI. Les Sysremes d’lnformation: An&yse el Conception. Bordas. Paris (1986). 8. Date C. J.. An Inrroduction IO Darabase Svstems. 4th edn. Vol. 1. Addison-Wesley. Reading, Mass. (1986). _ 9. Check1and.P.. Systems Thinking, Svstems Practice. Wiley, Chichester (1981). 10. Tardieu H., Rochfeld A. and Colletii R., La M&hode MERISE: Principes et Ourils. Les Editions d’Organisation, Paris (1983). 1I. Pask G., The Cybernerics of Humnn Learning und Performance. Hutchinson, London (1975). 12. Pask G., Cowers&ion, Cognifion and Learning. Elsevier, Amsterdam (1976). 13. Winer L. R., Research on computer-based learning environments: the Vitrine 2001. Cnn. J. educ. Commun. 17, No. 3, 159-166 (1988). 14. Borland Int., Reflex Plus: the database manager [computer program]. Borland Int. Scotts Valley, Calif. (1987).
Vol. 15. No. i-3. pp. 213-210. Bntain. All rights resened
1990 CopyrIght
5
0360. I3 I 5 99 53 00 + 0.00 1990 Perpamon Press plc
THE USE OF A RELATIONAL DATABASE IN QUALITATIVE RESEARCH ON EDUCATIONAL COMPUTING LAURAR. WINER and MARIO CARRI~RE Centre Quebecois de Recherche sur les Applications Pedagogiques de ~~rdinateur, 2001 Boulevard St-Laurent, Montreal. Quebec, Canada H2X 2f3 Abstract-This paper discusses the use of a relational database as a data management and analysis tool for non-experimental qualitative research. The relational database is Reflex Plus (Borland Int.); the research project is the Vitrine 2001, a project devoted to the study of computer-based learning environments, which uses participant observation and semi-structured interviews as the main data collection methods. The Vitrine research is centered around the Ion~tudinaI observation of actions and processes; in this context, a needwas identifiedfor computer-based tools to help in the data management and analysis process. The problem with constituting such tools, particularly in an explorative and qualitative context, is that one cannot establish a priori the data management and analysis needs.
INTRODUCTION
The potential of using computers to assist with qualitative data has been recognized for more than 20 years[l]. The advent of microcomputers has made the potential more accessible for most researchers. In 1984, a special issue of Quafitatiw Sociology [2] was devoted to computers and qualitative data, and in addition to the ten articles, the annotated bibliography of twenty items[3] attests to the growing interest. Text processing has been the most common type of application; however, this tool only partially addresses the problems of organizing, classifying, and manipulating the quantity of data generated in most qualitative studies. This is not to dismiss the Super Typewriter” use of computers as text processors; however, we feel that computers have the potential to play other roles, specifically, that of a “Super Filing System”. In order to be of maximum use to researchers working with qualitative data, computer tools should help not only with the mechanical acts of storing and printing data (text processors), but shouid also be able to help with organizing, manipulating, cross-referencing, and --walking through” data, both one’s own and those of fellow researchers, while providing different perspectives on the data or specified subsets. As has long been known in data management, one cannot approach the problem simply by asking the user what information will be needed[4]. First, the user’s perceived needs are often indete~inate; second, even if one could establish all of one’s perceived needs, those needs will evolve. Miles and Huberman[5] describe in some detail methods for qualitative data analysis. The proposed systematic approaches to finding meaning in a set of data are supported by various tactics for the specific tasks of generating meaning and testing or confirming findings. These tactics require that researchers be able to classify data under a variety of headings, link them to related data, search, display, count and group data according to a variety of criteria, as well as search for data that are not present. INFORMATION
SYSTEMS
These considerations led us to take the approach used in computer and management science known as information systems. An info~ation system has as its goal to create and conserve a representation of an organization’s activities in order that the actors within that organization can study and control its behaviour[6]. An information system consists of the ensemble of human resources, physical resources, and methods used in processing information within an organization[7]. The principal component of our information system is a database, selected because a database is essentially a computerized record-keeping system, i.e. a system with the basic function 213
24
LAURAR.
WISER
and
MARIO
CARRI~RE
of maintaining information and making that information available on demand[g]. It is important to realize that the database is one component of an information system, not THE information system in its entirety. For example, other components of our system are the collective memories of the researchers, written notes, photos, computer programs, and children’s productions. These other components are all cross-referenced in the database, but are not entirely contained within it. To try to computerize all aspects of an organization will not only hinder serendipity and intuition, but also inevitably lead to inefficiency at best, and incoherence and incomprehensibility at worst. Once the decision to have a database as the key component of our ~nfo~ation system had been taken, the next step was one of implementation. This is not a new problem. As Tardieu ef a/.[61 describe, the work of the ANSI/SPARC Committee (published in 1977) addressed the difficulties involved in going directly from a perceived reality to a database. The process of structuring a database is one which significantly benefits from intermediate steps or levels. The principal idea that we adopted from this work is the importance of separating one’s information needs from any technical constraints imposed by specific software. In order to arrive at this separation, we used the intermediate step of creating a conceptual model. In fact, this step of creating a conceptual model is in and of itself a useful activity and is often recommended in qualitative research [5]. Articulating the essentials, identifying the elements, and defining the relations is a process that forces the creation of a clear, external representation of what is often, especially in qualitative research, a complex soft system[9]. However, the utility is clearly increased many times over if this representation can be implemented in a dynamic form which allows for interaction between the model and the researcher. The tools created must therefore be as powerful, simple, responsive and supple as possible. The use of a relational database was seen as offering a modular and extensible external memory, open to contributions from individual researchers, and readily accessible to their queries. Using the Vitrine* project to illustrate, Fig. 1 shows the information flow between the activity under study (i.e. pupils’ visits to the research site), the researchers, the pupils, and the info~ation system while the activity is underway, as well as after the activity has finished. During the course of the research, information goes into the information system from the research activity. The information system channel becomes two-way with the researchers after the activity has finished (i.e. ceased to exist) as it then serves as a memory, allowing a replay of the “reality” as observed and fed into the system by the researchers. Any recording tool could simply replay the reality; the significant advantage of a computer-based information system, especially one which uses a database, is that data contained within it are structured in such a way that the user can search for specific information with the aid of a structured query system. DATABASES The main function of databases in information systems is to store and retrieve information. In order to perform this task efficiently, database systems typically try to reduce data redundancy so that people are always working on the same physical data. This has two major advantages: (1) it reduces the volume of data by reducing repetition; (2) it ensures that modifications made to data are immediately transmitted throughout the system [8]. Step I-buildiirg
a conceptuaf model
As stated above, the first step in implementing a database in a qualitative research project should be to build a conceptual model of the research environment. We adapted the MERISE method for database design [ IO), to design a model of the “reality’* in question, the Vitrine 200 1. This model is composed of stable entities, concrete or abstract, e.g. people, places, things, statements, and the relationships between them[6]. The entities and relationships are neutral; they do not contain inherent value judgements such as success/failure, liked/not liked, etc. (See Fig. 2-Conceptual Model. In order to aid readers who are unfamiliar with this form of notation, we will give a brief *The VitrineZOOl[13Jis an on-going projectofAPO QUEBEC, the Quebec Center for Research in Educational Computing, a non-profit Quebec government corporation for applied research and development in educational computing.
Fig. I. Information
Pupils
Reseomh Actlvhy
flow between information
Environment
system, research activity
Researchers
and researchers.
(a) During
research activity
and (b) after research activity has linished.
Researchers
J
- PmdUetPnr
Fieptr - Pmgramr
\
-rI
LAURA
R. WISER and MARIO CARRI~RE
APO
O&bec
- Vltrme
Conceptual
Configuration
2001
model
1.3
Configuration -name
Hardware
I-----,
0
Object Rclotion
+
A PIoDerty
Oneto
one relation
One to many relation Key characteristic
Fig. 2. Initial version of conceptual model of Vitrine 2001 project (1987-1988).
“walk-through” of part of the model. Take for instance the entities Teacher and Pupil which are linked by the relationship Teaches. Teacher to Teaches has the cardinality 1,n. This means that one Teacher can teach many Pupils. Pupils to Teaches also has the cardinality 1,n. Therefore, one Pupil can have more than one Teacher. If this cardinality were to be changed to l,l, it would mean that one Pupil could have only one Teacher.) The important fact to remember is that each conceptual model represents one view of reality. Clearly, conceptual models of the same “reality” may differ according to their authors; no one is more or less correct in absolute terms. As well, as our view or understanding of a system changes, our model may be modified to reflect those changes. Step 2-implementing
the model
Once a useful conceptual model has been designed, it is relatively straightforward to implement this in the form of a relational database. We used Reflex PlusTM(Borland Int.)* on a MacintoshTM with 2 Mbyte of RAM. Reflex PlusT” was chosen because of its simplicity, user interface, price, and the technical support available from the producer. Our database contains information on the co-researchers (pupils, observers, animators, teachers, visitors, etc.), the equipment, the software, their relationships within the environment, as well as comments and observations (see Fig. 3 for the conceptual model as implemented in Reflex PlusTM).
*Although Borland Int. claims that Reflex Plus[l4] is a relational database, strictly speaking, it is not. According to the generally accepted definition[8, pp. 320-3221 a relational database should not have predefined access paths to support database manipulation. Reflex Plus TMviolates this condition by having data structures called “links” that are used expressly to manipulate data. This, however, does not diminish its usefulness or power for our purposes.
RelatIonal
database
Fig. 3. Initial
in educational
logical
research
217
model.
What is extremely important to note is that in the data entry process, Reflex PiusTMautomatically manages the links; once the objects are entered into the database, the links between the entities (i.e. the relationships) must only be indicated once. In this way, changes are automatically reflected throughout the system and the amount of data is reduced. The advantages of a database are clearly exploited to minimize data redundancy while maximizing their consistency and accuracy. Figure 4 provides a representation of the data management performed by the system; i.e. the automatic creation of links and subsequent sharing of data. RESEARCH
The database as implemented for the 1987-1988 Vitrine project contains information on 19 primary-school children who visited the Vitrine 23 times for 2.5 h per visit. Approximately 60 different computer-based activities were available for them to choose from. Data gathered by four researchers consist of observation notes, reports filled out by the children, children’s productions and computer programs. To give an order of magnitude, this consists of approx. 1700 pages of data which translates into 1.2 Mbyte of data in Reflex PlusTM files. While in no way minimizing the benefits derived from creating a conceptual model of the research environment, these benefits do not help in the problems of organizing, classifying and manipulating the data once they are collected. As discussed above, there are established approaches for qualitative data analysis. How, then, does our “super filing system” help in applying these tactics? Mechanical acts such as counting frequency of events (e.g. how many times did two children work together and on what activities?) would clearly be a daunting task if physically looking through the stacks of paper were the only method available. This is a trivial query in our system. Criteria for counting can be based on any of the fields in the database. However, the query system is not limited to such simple mechanical acts. Searches can be done which check for links between entities (e.g. how many times, when and where did two or more children work on the same activity?) or include scanning free text fields for included text (e.g. what activities are described by the child with the word ‘fun’ or ‘boring’?).
LAURA
R. WISER and
-
Links created by the researcher
- _ . .
Links created by Reflex
MARIO CARR&RE
I
Plus
Fig. 4. Representation
of data management.
In order to note reIations between variables, see plausibility, build a logical chain of evidence, or apply the other tactics for generating meaning, it is often useful to be able to reorganize and represent data in different formats. The database can be conceived of as an entailment mesh [l 1,121 or “fishnet” which allows for any of the entities or “knots” to be taken as the starting point. For example, children’s and/or researchers’ comments may be presented by child, activity, visit, hardware configuration, or any other variable. One child can be followed by extracting everything relating to him or her (partners, notes, productions, etc.) over all visits. For testing or confnming findings, many of the same capabilities of the database are useful. Additionally, the ability to search on the absence of particular data, e.g. x is NOT present, becomes important in looking for negative evidence as well as checking out rival explanations. One tactic which is often cumbersome is that of triangulation; however, because the database either contains or points to different kinds of data from different researchers, it facilitates exploring independent paths to the same conclusions. As well, consistently contradictory observation notes from different
Relational database in educational research
researchers may indicate researcher bias, or incomplete conflicting evidence from different sources.
CAUTIONARY
explanations
219
may be indicated
by
NOTES
While the benefits of working with Reflex Plus TM have been enormous, and have, for us, far outweighed the difficulties, it is important that these difficulties be stated clearly. The first major problem, logistics, is to a large extent specific to our hardware and software, as well as to the scope of the research project. The absolute quantity of data is an important factor, and this must be evaluated for each research project. The technical problems (amount of data and processing speed) would clearly not have been issues if we had been working with more sophisticated software and hardware. As it is, we have had to upgrade our Macintosh Plus to its limit of 4 Mbyte, and its processing speed leaves something to be desired when performing complex queries. The next move is either to step up to a Macintosh II, or invest in a minicomputer and change our software completely. While either of these solutions is practicable, the resource investments required (both monetary and human) increase significantly and must be considered seriously before undertaking either. A second problem is not specific to our implementation, but rather to the conceptual and technical problems inherent in any such undertaking. The services of a database design consultant are extremely useful in the set-up phase to aid in the modeling of the environment and the implementation of the conceptual model in whatever software is chosen. Additional technical support may be required during the early stages, depending on the familiarity of the users with the software, its complexity, and the complexity of the database design. This may be provided either on-site, by the vendor, or through a combination of both. CURRENT
ACTIVITIES
This approach to data management in our work has carried on into the second year of our research project. The set-up phase over and all the “bugs” out, the researchers are now independent of the computer scientist in the on-going work. The conceptual model undergoes periodic revisions to better reflect the reality of the Vitrine project as it evolves, and our deepened understanding of it. Up to now, these refinements have been relatively easy to incorporate. The utility of the system to respond to our requirements as researchers is also continually being tested. Certain elements are lacking in the system as it currently exists; some can be and are being modified, others are software limitations and may prove to be determiant factors in how far we can go with the current installation. CONCLUSION It is still too early for us to draw any final conclusions about the impact on the research process itself of the use of a database, but this aspect must not be neglected. However, it is clear to us that there are enormous benefits for researchers to be gained from the facility with which one can manipulate data, as well as from the structure one is obliged to create to store these data. However, as much as we may look to computer tools to help in qualitative research, it is important to remember that as of yet, computers do not understand data, they only manipulate them. Data in a computer does not have any “meaning”, the meaning, like beauty, is and probably always will be in the eye of the beholder. REFERENCES 1. Stone P. J., Dunphy D. C., Smith M. S. and Ogilvie D. M.. Analysis. MIT Press, Cambridge,
The General Inquirer: A Computer Approach ro Content
Mass. (1966). 2. Conrad P. and Reihnarz S., Qual. Social. Special Issue: Computers and Qualitative Data 7, No. l/2 (1984). 3. Dennis D. L., “Word Crunching”: an annotated bibliography on computers and qualitative data analysis. Qual. Sociof. Special Issue: Computers and Qualitative Data 7, No. l/2, 148-156 (1984).
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4. Ackoff R. L.. Management misinformation systems. ,bfgmr Sci. 14, No. 4, 147-156 (1967). 5. Miles M. B. and Huberman A. M., Qualiratire Dofa Analysrs: A Sourcebook of 1Vew,Werhodr. Sage. Beverly Hills, Calif. (1984). 6. Tardieu H., Nanci D. and Pascot D., Conception d’un Sysf&ne d’In/brmnrion. Gaetan Morin. Paris (1985). 7. GALACSI. Les Sysremes d’lnformation: An&yse el Conception. Bordas. Paris (1986). 8. Date C. J.. An Inrroduction IO Darabase Svstems. 4th edn. Vol. 1. Addison-Wesley. Reading, Mass. (1986). _ 9. Check1and.P.. Systems Thinking, Svstems Practice. Wiley, Chichester (1981). 10. Tardieu H., Rochfeld A. and Colletii R., La M&hode MERISE: Principes et Ourils. Les Editions d’Organisation, Paris (1983). 1I. Pask G., The Cybernerics of Humnn Learning und Performance. Hutchinson, London (1975). 12. Pask G., Cowers&ion, Cognifion and Learning. Elsevier, Amsterdam (1976). 13. Winer L. R., Research on computer-based learning environments: the Vitrine 2001. Cnn. J. educ. Commun. 17, No. 3, 159-166 (1988). 14. Borland Int., Reflex Plus: the database manager [computer program]. Borland Int. Scotts Valley, Calif. (1987).