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COMPUTER-ASSISTED ANALYSIS OF QUALITATIVE GERONTOLOGICAL RESEARCH

 

Roger Hiemstra, Syracuse University

Elizabeth Essman, SUNY College of Technology at Utica

Norbert Henry, University College, Syracuse University

Dorothea Palumbo, Syracuse University

 

Qualitative research has tremendous potential for breaking new ground in gerontological research. QUALOG, a qualitative analysis program using logic programming and computer support, has been developed. This article describes how QUALOG was utilized to analyze data from a qualitative study of older adult learners.

 

Educational Gerontology, 13: 417-426, 1987

Copyright © by Hemisphere Publishing Corporation, A Taylor and Francis Journal.

Reprinted by Permission of the Journalís editor

Stylistically, the article has been converted to APA, 5th Edition.

 

Qualitative research has a useful role to play in helping scholars understand adult behavior from the perspective of subjects. Brookfield (1983) and Rockhill (1982), in fact, urge educators of adults to look carefully at the appropriateness of research methodology because quantitative research frequently is overused or misused.

 

Researchers using qualitative methodologies approach data collection and analysis inductively. Interviews and/or participant observations can be used to inductively build theory. Such theory is grounded in the subject's observed behavior and words rather than in a deductive testing of an a priori hypothesis or theory. For example, Fingeret (1983) used in-depth, unstructured interviewing and participant observations to demonstrate the importance of social networks to illiterate adults. Her initial work led to some working theories about the literacy movement in the United States (Fingeret, 1984).

 

The popularity of qualitative educational research methodology is both recent and growing, especially if one looks beyond the interviewing efforts of people like Houle (1961) and Tough (1967, 1979).

 

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One of the first clarion calls for such qualitative approaches to research was made by Mezirow (1971). He urged educators of adults to utilize Glaser and Strauss' (1967) "grounded theory" methodology in furthering the development of a theory of practice for the field. Subsequent work by Mezirow and his colleagues (Mezirow, Darkenwald, & Knox, 1975; Darkenwald, 1980) provided some useful models for related research. For example, Jones and Charnley (1978) utilized a grounded theory approach to assess the impact of adult literacy. Long (1983) noted that a total of 13 grounded theory studies were reported at the Adult Education Research Conference between 1977 and 1980. Rubenson (1982) also notes the popularity and importance of grounded theory approaches.

 

Other qualitative approaches have been used, too. Warren (1982) describes the use of the written life history as a prime research tool for adult educators. Gibbons and colleagues (1980) studied the biographies of 20 top achievers for self-directed characteristics. Brookfield (1981) combined an open-ended interviewing approach with grounded theory analysis techniques to determine the learning activities, attitudes, and networks of 25 successful independent learners in England. Hiemstra (1982) reported on the case study method combined with grounded theory in a longitudinal study of 30 older adults. This latter study serves as a focal point for this article in that new data were gathered and new qualitative analyses undertaken.

 

More specifically, this article demonstrates how a qualitative research tool can be used to examine the learning behavior of older adults. Use of such techniques as in-depth interviews allows interviewees to define and clarify a perception of themselves as successful lifelong learners. The researcher goes to the subjects to gather and examine information with a minimum of predetermined theoretical expectations. This technique, however, presents some difficulties for the researcher when organizing and analyzing the volume of data collected. A common method of organizing data for analysis involves the use of multiple copies of field notes which are cut and placed in file folders according to categories determined by a researcher's review of the data. Problems can easily arise when using such a system. Required researcher time and expense can be immense. The problems of managing large amounts of data efficiently, and the possibility of missing important insights through improper filing or coding also exist.

 

The advent of computer-assisted data analysis has helped to resolve some of these problems. Computers have a tremendous

 

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potential and have been used extensively as a tool for quantitative data analysis. Their extension into qualitative analysis is a predictable outgrowth of such benefits as speed, storage potential, and multiple variable manipulations. Conrad and Reinharz (1984) provide some insights into the use of computers for qualitative research. However, because the researcher must stay intimately involved in qualitative research in terms of coding, recoding, memo writing, and so on, the computer remains simply a tool for facilitating analysis efforts. It is the researcher's use of this tool that determines the degree to which computer-assisted data analysis is appropriate and effective in qualitative research.

 

A computer-assisted, qualitative data analysis scheme using logic programming has been developed at Syracuse University. The purpose of this article is to describe this scheme, entitled "QUALOG" (Hiemstra, 1986; Shelly & Sibert, 1985), and to provide some incentive for increased use of qualitative inquiries and computer-assisted analysis in gerontological research.

 

QUALOG

 

Qualitative data analysis involves various activities used to organize, interpret, synthesize, conceptualize, and report information. The researcher examines data for recurring themes, word patterns, study foci, relationships among categories, evolving themes, and even hypothesis-testing opportunities. Frequently, qualitative analysis can require considerable time, resources, and painstaking manipulation of small bits of data.

 

The developers of QUALOG have produced a series of computer programs designed to aid in the analysis efforts. A computer is used to keep track of a study's evolution, including such mechanical tasks as organizing the data, coding information, writing memoranda, and interpreting data. The interpretation phase can be quite powerful, including such functions as tabulating the number of times various codes are used, comparing information with different coding labels for commonalities, and even the testing of tentative hypotheses through a sophisticated query process. The availability of a recoding system enables researchers to refine queries and hypothesis testing as the analysis evolves.

 

QUALOG is based on a logic programming system that uses logical assertions and a theorem-proving capability to establish the existence or nonexistence of one or more results that satisfy conditions the researcher establishes in what is called an answer template. The template is designed by the researcher thinking about possible or

 

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probable relationships among terms and then stating such associations in natural words or terms. Results arise as a by-product of the proof required for the theorem-proving activity. LOGLISP (Robinson & Sibert, 1981), the programming system in which QUALOG is written, integrates logic programming with LISP (McCarthy, 1960; McCarthy, Abrahams, Edwards, Hart, & Leven, 1965; Meehan, 1979), primarily a language used for symbolic computations. The combination provides a flexibility required to work directly with symbolic information, prose, field notes, subsequent memoranda, and evolving relationships within the data.

 

To use QUALOG the researcher enters data into separate files for later manipulation. There currently are two mechanisms for entering data, such as interviews, field notes, observational information, and memoranda. One method involves the use of an IBM VM/CMS (Virtual Machine/Conversational Monitor System) or a DEC (Digital Equipment Corporation) VAX main frame computer with the VMS (Virtual Memory System) file editor or word-processing entry program. The individual entering data uses an on-line terminal or modem-connected keyboard to "type" in the information. Data from magnetic tape, floppy disk, or transcribed audiotape can be used, but they must be entered as a main frame file through appropriate communication devices for final preparation. Another method is to use an optical character reader, which translates typed or printed material into a digitalized data file.

 

A word-processing program on the main frame permits subsequent corrections, deletions, or additions. Data are next formatted so that unique (sequential) numbers are given to each line of, say, 50 to 60 characters long. This feature is necessary so that any section of text can be easily referenced. Once the formatted data are available, the researcher manually codes all information (usually by marking off appropriate sections of data from a printed version of any formatted files and assigning coding-category labels). The "codes" are then entered through QUALOG'S coding program. A memo-writing function also is available to record the researcher's notes and ideas that evolve throughout the analysis effort. All inputted information is referenced chronologically by coding category and by data groups.

 

Finally, the researcher can retrieve data in a variety of formats based on the coded information. This step is used to deduce instances where data confirm or disconfirm formally expressed queries about any coded information, text in data files, and any other information associated with the data relevant to emerging hypotheses. Storing, revising, rerunning queries, and retrieving

 

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memoranda both chronologically and by coding categories are the mechanisms available to test hypotheses and generate theory.

 

USING QUALOG FOR DATA ANALYSIS

 

Baseline Study

 

An impetus for the data-analysis effort described here was a longitudinal study begun in 1975 by the senior author (Hiemstra, 1982). Over a two-month period he interviewed, observed, and gathered documents from 30 older adults in Iowa and Nebraska. Informal follow-up to this initial study period took place in 1976-1979 via letters, telephone calls, and visits with several subjects. A more formal open-ended questionnaire in 1983 completed the study's initial phase.

 

The study's purpose was to examine the lives, thoughts, and words of selected older adults who were by reputation successful adult learners. Subjects were asked to participate based on the recommendations of professional adult educators or gerontologists. Formal higher education achievement or current learning activities were not included as criteria for participation. The intention was to develop a "grounded theory" about success of the lifelong learner.

 

Relevant demographic information from the initial group of 30 participants included the following: 40% were males and 60% were females; 90% had an average or above average income; 86% lived in apartments or their own homes; 83% were married and living with their spouses or were widowed; 33% were college graduates; and 36% had had careers teaching in public schools.

 

A number of significant themes emerged from the data analysis completed in 1982; the analysis involved developing a coding scheme, hand sorting data according to several developed codes, and feedback by colleagues as the information emerged. Regarding an overall category labeled "health," the subjects appeared to have a high level of mental alertness, generally enjoyed life, were well-groomed, and had a fairly significant level of relaxation. Another category evolved and was called "A Personal Involvement in Learning." The subjects were for the most part active readers, engaged in study projects, and pursued a wide range of learning activities. All subjects had achieved educational backgrounds in colleges, normal schools, or specialized training beyond high school.

 

Many of the subjects were actively involved in writing projects. Several had published books and professional articles, were writing poetry, or were conducting personal research of one kind or another.

 

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Finally, a majority of participants viewed themselves as successful older adult learners. They identified keeping active, being curious, self-reliance, and a strong parental influence as major factors fostering their success as adult learners. Most also stated that they practiced some form of meditation or self-reflection as a source of inner strength and as a resource for coping with life's vicissitudes.

 

The subjects were all known to be involved in church, family, volunteer activities, and/or educational pursuits. The subjects all lived at home and were, or had been, married. A teaching background also was prevalent in either the subjects themselves or within their families.

 

This brief summary of the initial analysis of data is presented as a framework from which the present phase finds its impetus and direction. In the current phase six new subjects were studied. Each junior author carried out a case study of two subjects selected because by reputation and activity they resembled subjects in the earlier study. Information from four participants in the initial study also were included to provide a base of 10 successful learners, all over 70 years of age. This group comprises the database for a present analysis of information by the QUALOG computer-assisted data analysis scheme.

 

Computer-Assisted Data-Analysis Effort

 

Data analysis actually began before the researchers gathered information on any new subjects. Discussion and dialogue between the four researchers was structured by the initial study's analysis efforts. Newly gathered information was reviewed and reflected upon during subsequent group sessions. Progressing through the mechanical processes of QUALOG, such as scanning, transcribing, and creation of working (T) and analyzing (I) files, the need to collaborate on developing comprehensive lists of codes and subcodes became apparent.

 

The development of codes and subcodes can be a complicated process. There must be agreement on definitions of code words and the connotation and context in which any code will be applied to the data must be clarified. Researchers using qualitative techniques rely on experience and sensitivity to emerging themes discernible in the data to assist in that process. Approximately eight hours of group sessions were required for clarification and acceptance of the final list. Main codes were developed by identifying a repetitive theme within one or more interviews. Subcodes were used to further clarify, describe, and/or narrow the theme. Strength of the main

 

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codes lay in the number of times a theme could be identified throughout the interviews.

 

The need for clearly defined main codes is clear. For example, when coding a theme such as "retirement," subjects' words, phrases, or paragraphs were placed in this code when that word itself was used, when a related view was expressed, or a related attitude described. Subcodes augment or clarify the main code. For example, "positive" or "negative," "self" or "family," could be used to provide further description of "retirement."

 

Another main code example, "inner resources," became an umbrella for "serenity," "inner strength," or spirituality." Subcodes used for clarification in this instance could include "self," "spouse," or "family." It was also possible to link two main codes such as "retirement" with "inner resources," if the data (an interviewee's own words) indicated such a linkage could be made or was being made during an interview.

 

Two additional commands within QUALOG were particularly useful in the next step of data analysis. After the raw data had been coded, it was possible to begin generating working hypotheses or formalized linkages between anticipated or suspected relationships. The end purpose of a grounded theory is to explicate human behavior. Since the main and subcodes are a "shorthand" for subjects' words, behaviors, and attitudes, working hypotheses are a method of blending researchers' summaries of what was observed with subjects' actual words and behaviors to develop a theory of human behavior.

 

The first of these additional commands, "fatcodes," rapidly lists the number of times a main code is used. This command can be varied to select one particular interview or used across all interviews. For example, in one particular interview "retirement" was mentioned 13 times, sometimes when the interviewee was describing his retirement process or attitudes about being retired, and other times when talking about seemingly unrelated areas. Across all 10 interviews the term was mentioned nearly 100 times.

 

The second command, "pcodes," is valuable in that it identifies actual line numbers in the text where a specific main code or subcode is mentioned by a subject. This information enables a researcher to return to the original copy of the interview and by referencing the line numbers review actual words as they were used by a subject. In the above example, it would be possible to read as many lines as desired above or below each of the 13 times retirement is mentioned. in the above. example to obtain some ideas regarding the context in which an interviewee brought up the term. It also

 

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would be possible to quickly scan any text for all 10 interviewees where "retirement" is mentioned only if it is in relationship to or in context with a discussion about spouse or family. Thus, working hypotheses can be generated, analyzed initially, and refined until some level of saturation is reached. Recoding of data is always possible to assist in the refinement process.

 

Memo writing is also facilitated by the use of "fatcodes" and "pcodes." As new insights, themes, ideas or methodological issues occur to researchers, summary notes or memos can be added to the overall base. In other words, memo writing assists with recording, organizing, and storing the researcher's thoughts and ideas as data analysis proceeds. QUALOG also facilitates collaboration and a sharing of ideas among research group members as a printout of all memos is an integral program feature, thereby fostering clarification and group communication.

 

The QUALOG program is a useful and powerful tool for data analysis in qualitative research. Generation and interpretation of field note themes into main and subcodes assists in the development of working hypotheses. These hypotheses become the basis for further research analysis and theory development, which, of course, remain the researcher's task.

 

CONCLUSIONS

 

QUALOG is a valuable tool for sorting, storing, and organizing vast amounts of data usually collected in qualitative research. The manual cut-and-paste approach to coding information and data analysis is transferred by means of this computer program to an efficient, easily retrievable system. The program allows for multiple researcher manipulations of data into manageable units of information. A further benefit of QUALOG is that associations, themes, and trends encompassing the inductive research process are easily discernible. Memos also are an integral part of the QUALOG data collection process; they are easily stored and retrieved to serve as additional sources of data insights and reflections.

 

In this particular project, group collaboration was useful for developing codes, themes, and hypotheses. Mutual support and sharing of information and ideas was also a singular advantage to the researchers in learning techniques and intricacies of the QUALOG program.

 

As with any new computer system, there are limitations to its utility. Of particular caution to a neophyte user of QUALOG is the potential that interest in any mechanical processes of a computer

 

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program might distract from the research focus. In addition, researchers must be prepared to expend large blocks of time learning this sophisticated computer-assisted data-analysis system. The state of the art at present is only as good as the researcher's understanding and ability to use and apply QUALOG to raw data. Frustration is inevitable when the computer system is down, data are inadvertently lost, or inappropriate commands result in useless information. Nevertheless, the program's strengths and usefulness can far outweigh its limitations.

 

REFERENCES

 

Brookfield, S. (1981). Independent adult learning. Studies in Adult Education, 13(1), 15-27.

Brookfield, S. (1983). Methodolatory in adult education research: Straw men and false dichotomies. Adult Education Research Conference Proceedings, 24, 32-37.

Conrad, P. & Reinharz, S. (1984). Computers and qualitative data. (Special issue of Qualitative Sociology.) New York: Human Sciences Press.

Darkenwald, G. G. (1980). Field research and grounded theory. In H. B. Long, R. Hiemstra, and Associates, Changing approaches to studying adult education. San Francisco: Jossey-Bass.

Fingeret, A. (1983). Social network: A new perspective on independence and illiterate adults. Adult Education Quarterly, 33, 133-146.

Fingeret, A. (1984). Adult literacy education: Current and future direction (Information Series No. 284). Columbus: ERIC Clearinghouse in Adult, Career, and Vocational Education, The National Center for Research in Vocational Education, The Ohio State University. (ERIC Document Reproduction Service No. ED 246 308).

Gibbons, M., Bailey, A., Comeau, P., Schmuck, J., Seymour, S., & Wallace, D. (1980). Toward a theory of self-directed learning: A study of experts without formal training. Journal of Humanistic Psychology, 20(2), 41-45.

Glaser, B. & Strauss, A. (1967). The discovery of grounded theory. Chicago: Aldine.

Hiemstra, R. (1982). The elderly learner: A naturalistic inquiry. Adult Education Research Conference Proceedings, 23, 103-107.

Hiemstra, R. (1986). QUALOG: A qualitative data analysis program. Adult Education Quarterly, 36, 169-170.

Houle, C. O. (1961). The inquiring mind. Madison: University of Wisconsin Press.

Jones: H. A. & Charnley, A. H. (1978). Adult literacy: A study of its impact. Leicester, England: Natural Institute for Adult Continuing Education.

Long, H. B. (1983). Characteristics of adult education research reported at the Adult Education Research Conference 1971-1980. Adult Education,, 33, 79-96.

McCarthy, J. (1960). Recursive functions of symbolic expressions and their computation by machine, part 1. Communications of the Association for Computing Machinery, 3, 184-195.

 

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McCarthy, J., Abrahams, P. W., Edwards, D. J., Hart, T. P., & Leven, M. I. (1965). LISP 1.5 programmer's manual (2nd ed.). Cambridge, MA: M.LT. Press.

Meehan, J. R. (1979). The new VCI LISP manual. Hillsdale, NJ: Lawrence Erlbaum.

Mezirow, J. (1971). Toward a theory of practice. Adult Education, 21,135-147.

Mezirow, J., Darkenwald, G. G., & Knox, A. B. (1975). Last gamble on education: Dynamics of Adult Basic Education. Washington, DC: Adult Education Association of the U.S.A.

Robinson, J. A. & Sibert, E. E. (1981). The LOGLISP user's manual (CIS Technical Report). Syracuse, NY: Syracuse University, School of Computer and Information Science.

Rockhill, K. (1982). Researching participation in adult education: The potential of the qualitative perspective. Adult Education, 33, 3-19.

Rubenson, K. (1982). Adult education research: In quest of a map of the territory. Adult Education, 32, 57-74.

Shelly, A. & Sibert, E. (1985). The QUALOG user's manual (CIS Technical Report, CIS-85-2, DEC-10 Version). Syracuse, NY: Syracuse University, School of Computer and Information Science.

Tough, A. M. (1967). Learning without a teacher. Toronto: Ontario Institute for Studies in Education.

Tough, A. M. (1979). The adult's learning projects (2nd ed.). Austin, TX: Learning Concepts.

Warren, C. E. (1982). The written life history as a prime research tool in adult education. Adult Education, 32, 214-228.

 


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