Skip to main content
SpringerLink
Log in

Qualitative computing: Approaches and issues

  • Articles
  • Published:
Qualitative Sociology Aims and scope Submit manuscript

Abstract

This paper reviews three approaches to using computers to perform qualitative analysis. These approaches are distinguished by the way they represent knowledge in the computer—as text, things, or concepts—and the operations they permit on that knowledge. These approaches are compared and the advantages and disadvantages of each are identified based on the way they perform basic tasks of qualitative research. Finally, fundamental issues and problems likely to influence qualitative computing for years to come are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Aikens, J. 1983 “Prototypical knowledge for expert systems.” Artificial Intelligence 20: 163–210.

    Google Scholar 

  • Allen, J. 1981 “An interval-based representation of temporal knowledge.” Proceedings of the Seventh International Joint Conference on Artificial Intelligence (IJCAI-81), Vancouver, Canada, August, 221–226.

  • Allen, J. and C. Perrault 1980 “Analyzing intention in utterances.” Artificial Intelligence 16: 143–178.

    Google Scholar 

  • Amarel, S. 1962 “On the automatic formation of a computer program which represents a theory,” in Yovits, et al. (eds.), Self Organizing systems. Spartan Books.

  • Atre, S. 1980 Data Base: Structured Techniques for Design, Performance, and Management. New York: Wiley.

    Google Scholar 

  • Barr, A. and J. Davidson 1981 “Knowledge representation.” Pp. 141–122 in A. Barr and E. Feigenbaum (eds.), The Handbook of Artificial Intelligence, Vol. I. Los Altos, California: William Kaufman.

    Google Scholar 

  • Becker, H. and B. Geer 1960 “Participant observation: The analysis of qualitative field data.” In R. N. Adams and J. J. Preis (eds.), Human Organizational Research. Homewood, Il: Dorsey.

    Google Scholar 

  • Bledsoe, W. 1977 “Non-resolution theory proving.” Artificial Intelligence 9:1–35.

    Google Scholar 

  • Blum, R. 1982 “Automated induction of causal relationships from a time-oriented clinical database: The RX Project.” Proceedings of AMIA-82:307–311.

  • Bobrow, D. 1980 “Special issue on non-monotonic logic.” Artificial Intelligence 13(1,2).

  • Brent, E. 1981a “Resources fair: Technology in sociology.” A session at the 45th Annual Meeting of the Midwest Sociological Society, Minneapolis, Minnesota, April.

  • —— 1981b “Writing with a data base management program.” BYTE: The Small Systems Journal. 6(11): 18–34.

    Google Scholar 

  • Brent, E. 1983a “Microcomputer demonstrations—Sociological applications.” A session at the 47th Annual Meeting of the Midwest Sociological Society, Kansas City, Missouri, April.

  • Brent, E. 1983b “CONSULTANT: An expert system using a relational database.” Paper presented at the American Association for Medical Computing and Informatics Congress, San Francisco, California, April.

  • Buchanan, B. 1974 “Scientific theory formation by computer.” Proceedings of NATO Advanced Study Institute of Computer Oriented Learning Processes, Bonas, France.

  • —— 1982 “New research on expert systems,” in J. Hayes, D. Mitchie, and Y. Pao (eds.), Machine Intelligence 10. New York: Wiley.

    Google Scholar 

  • Carbonell, J. 1981 “A computational model of analogical problem solving.” Proceedings of the Seventh International Joint Conference on Artificial Intelligence (IJCAI-81), Vancouver, Canada, August, 147–152.

  • Charmaz, K. 1983 “The grounded theory method: An explication and interpretation.” Pp. 109–126 in R. M. Emerson (ed.), Contemporary Field Research: A Collection of Readings. Boston: Little, Brown.

    Google Scholar 

  • Charniak, E. 1978 “On the use of framed knowledge in language comprehension.” Artificial Intelligence 11: 225–265.

    Google Scholar 

  • Cohen, F. and C. Perrault 1979 “Elements of a plan-eased theory of speech acts.” Cognitive Science 3: 177–212.

    Google Scholar 

  • Davis, R. 1980 “Meta-rules: Reasoning about control.” Artificial Intelligence 15: 179–222.

    Google Scholar 

  • Davis, R. and D. Lenat 1982 Knowledge-Based Systems in Artificial Intelligence. New York: McGraw-Hill.

    Google Scholar 

  • Dietterich, T. 1982 “Learning and inductive inference.” Pp. 323–512 in P. Cohen and E. Feigenbaum (eds.), The Handbook of Artificial Intelligence, Vol. 3. Los Altos, California: William Kaufmann.

    Google Scholar 

  • Dietterich, T. and R. Michalski 1981 “Inductive learning of structural descriptions: Evaluation criteria and comparative review of selected methods.” Artificial Intelligence 16: 257–294.

    Google Scholar 

  • Doyle, J. 1979 “A truth maintenance system.” Artificial Intelligence 12: 231–272.

    Google Scholar 

  • Drass, K. 1980 “The analysis of qualitative data.” Urban Life 9: 332–353.

    Google Scholar 

  • Duda, R., J. Gaschnig, and P. Hart. 1980 “Model design in the prospector consultant system for mineral exploration,” in Mitchie (ed.), Expert Systems in the Microelectronic Age. Edinburgh: Edinburgh University Press.

    Google Scholar 

  • Duda R. and E. Shortliffe 1983 “Expert systems research,” Science 220–268.

  • Garfinkel, H. 1967 Studies in Ethnomethodology. Englewood Cliffs, N.J.: Prentice-Hall.

    Google Scholar 

  • Gassaway, R and W. Elder 1981 “Toward de-stygmatizing computers for qualitative sociology: A methodological essay.” Paper presented at the 45th Annual Meeting of the Midwest Sociological Society, Minneapolis, Minnesota, April.

  • Georgeff, M. P. 1982 “Procedural control in production systems.” Artificial Intelligence 18: 175–201.

    Google Scholar 

  • Glaser, B. 1978 Theoretical Sensitivity. Mill Valley, California: Sociology Press.

    Google Scholar 

  • Glaser, B. and A. Strauss 1967 The Discovery of Grounded Theory: Strategies for Qualitative Research. Chicago: Aldine.

    Google Scholar 

  • Goffman, E. 1974 Frame Analysis. New York: Harper & Row.

    Google Scholar 

  • Goldstein, I. and S. Papert 1977 Cognitive Science 1, 84.

  • Hayes, P. 1979 “The logic of frames.” Pp. 46–61 in D. Metzing (ed.), Frame Conceptions and Text Understanding. Walter deGruyter & Co., 46–61.

  • Heise, D. 1981 Sociological Methods & Research: Special Issue on Microcomputers and Social Research 9(4).

  • Hessler, R. 1983 “Using computers to organize qualitative field notes in a methods class.” Paper presented at the 47th Annual Meeting of the Midwest Sociological Society, Kansas City, Missouri, April.

  • Hirschman, L. and G. Story 1981 “Representing implicit and explicit time relations in narrative.” Proceedings of the Seventh International Joint Conference on Artificial Intelligence (IJCAI-81). Vancouver, Canada, August, 289–295.

  • Hobbs, J. 1981 “Metaphor interpretation as selective inferencing.” Proceedings of the Seventh International Joint Conference on Artificial Intelligence (IJCAI-81), Vancouver, Canada, August, 85–91.

  • Hutton, S. S. and S. R. Hutton 1981 “Microcomputer data ease management of bibliographic information.” Sociological Methods & Research, 9: 461–472.

    Google Scholar 

  • Kahn, K. and C. Gorry 1977 “Mechanizing temporal knowledge.” Artificial Intelligence 9: 87–108.

    Google Scholar 

  • Kirk, R. 1981 “Microcomputers in anthropological research.” Sociological Methods & Research, 9: 473–492.

    Google Scholar 

  • Kuipers, B. 1975 “A frame for frames: Representing knowledge for recognition,” Pp. 151–84 in D. Bobrow and A. Collins (eds.), Representation and Understanding. New York: Academic.

    Google Scholar 

  • Langley, P., G. Bradshaw, and H. Simon 1981 “BACON.5: The discovery of conservation laws.” Proceedings of the Seventh International Joint Conference on Artificial Intelligence (IJCAI-81), Vancouver, Canada, August, 121–126.

  • Lenat, D. 1978 “The ubiquity of discovery.” Artificial Intelligence 9: 257–285.

    Google Scholar 

  • —— 1982 “The nature of heuristics.” Artificial Intelligence 19: 189–249.

    Google Scholar 

  • Lindberg, D., G. Sharp, L. Kingsland, S. Weiss, S. Hayes, H. Ueno, and S. Hazelwood 1980 “Computer based rheumatology consultant.” In MEDINFO 80: Proceedings of the Third World Conference on Medical Informatics. Tokyo, Japan.

  • Lindsay, R., E. Buchanan, E. Feigenbaum, and J. Lederberg 1980 Applications of Artificial Intelligence for Organic Chemistry: The DENDRAL Project. New York: McGraw-Hill.

    Google Scholar 

  • Lofland, J. 1971 Analyzing Social Settings. Belmont, California: Wadsworth.

    Google Scholar 

  • Manning, P. K. 1982 “Analytic induction.” Pp. 273–302 in R. B. Smith and P. K. Manning (eds.), Qualitative Methods: Vol. II of Social Science Methods. Cambridge, Massachusetts: Ballinger.

    Google Scholar 

  • Martin, J. 1977 Computer Data-Base Organization. 2nd Ed. Englewood Cliffs, New Jersey: Prentice Hall.

    Google Scholar 

  • Mills, C. W. 1959 The Sociological Imagination. New York: Free Press.

    Google Scholar 

  • Minsky, M. 1975 “A framework for representing knowledge.” Pp. 211–277 in P. Winston (ed.), The Psychology of Computer Vision. New York: McGraw-Hill.

    Google Scholar 

  • Mitchell, T. 1982 “Generalization as search.” Artificial Intelligence 19: 203–226.

    Google Scholar 

  • Nilsson, N. J. 1980 Principles of Artificial Intelligence. Palo Alto, California: Tioga.

    Google Scholar 

  • Schank, R. 1975 Conceptual Information Processing. New York: North-Holland.

    Google Scholar 

  • Schank, R. and R. Abelson 1977 Scripts, Plans, Goals, and Understanding. Hillside, New Jersey: Lawrence Erlbaum.

    Google Scholar 

  • Schank, R. and K. Colby 1973 Computer Models of Thought and Language. San Francisco: Freeman.

    Google Scholar 

  • Schmidt, C., N. Sridharan, and J. Goodson 1978 “The plan recognition problem: An intersection of psychology and artificial intelligence.” Artificial Intelligence 11: 45–83.

    Google Scholar 

  • Seidel, J. 1983 “THE ETHNOGRAPH,” computer programs demonstrated at the 47th Annual Meeting of the Midwest Sociological Society, Kansas City, Missouri, April.

  • Shortliffe, E. 1976 Computer-Based Medical Consultations: MYCIN. New York: North-Holland.

    Google Scholar 

  • Simon, H. 1977 “Artificial intelligence systems that understand.” Proceedings of the 5th International Conference of Artificial Intelligence (IJCAI-77), Cambridge, Massachusetts, August, 1059–1073.

  • Stefik, M., J. Aikins, R. Balzer, J. Benoit, L. Birnbaum, F. Hayes-Roth, and E. Sacerdoti 1982 “The organization of expert systems: A tutorial.” Artificial Intelligence 18: 135–173.

    Google Scholar 

  • Stefik, M. and P. Friedland 1978 “Machine inference for molecular genetics: Methods and applications.” Proceedings of the National Computer Conference, June.

  • Sykes, R. 1977 “Techniques of data collection and reduction in systematic field observation.” Behavior Research Methods and Instrumentation 9: 407–417.

    Google Scholar 

  • Ullman, J. 1980 Principles of Database Systems. Potomac, Maryland: Computer Science Press.

    Google Scholar 

  • Waterman, D. and F. Hayes-Roth 1978 Pattern-Directed Inference Systems. New York: Academic.

    Google Scholar 

  • Weiss, S., K. Kern, C. Kulikowski, M. Uschold 1981 “A guide to the use of the EXPERT consultation system.” Department of Computer Science, Rutgers University, New Brunswick, New Jersey.

    Google Scholar 

  • Werner, O. 1982 “Microcomputers in cultural anthropology, APL programs for qualitative analysis.” BYTE 7(7): 250–280.

    Google Scholar 

  • Winograd, T. 1975 “Framework representations and the declarative-procedural controversy.” Pp. 185–210 in D. Bobrow and A. Collins (eds.), Representation and Understanding. New York: Academic.

    Google Scholar 

  • Winston, P. 1975 “Learning structural descriptions from examples.” Pp. 157–210 in P. Winston (ed), the Psychology of Computer Vision. New York: McGraw-Hill.

    Google Scholar 

  • —— 1978 “Learning by creating and verifying transfer frames.” Artificial Intelligence 10: 147–172.

    Google Scholar 

  • Winston, P. and B. Horn 1981 LISP. Reading, Massachusetts: Addison-Wesley.

    Google Scholar 

  • Znaniecki, F. 1934 The Method of Sociology. New York: Farrar and Rinehart.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Sociology, University of Missouri, 108 Sociology, 65211, Columbia, MO

    Edward Brent

Authors
  1. Edward Brent
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

This research was conducted while the author was on sabbatical leave from the University of Missouri and a post-doctorate fellow on NIH grant LM 07006 from the National Library of Medicine. This paper has benefited greatly from the comments of Peter Hall.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Brent, E. Qualitative computing: Approaches and issues. Qual Sociol 7, 34–60 (1984). https://doi.org/10.1007/BF00987106

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF00987106

Keywords

Search

Navigation