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A Basis for Scaling Qualitative Data
Abstract
IN A GREAT deal of research in the social and psychological sciences, interest lies in certain large classes of qualitative observations. For example, research in marriage is concerned with a class of qualitative behavior called marital adjustment, which includes an indefinitely large number of interactions between husband and wife. Public opinion research is concerned with large classes of behavior like expressions of opinion by Americans about the fighting ability of the British. Educational psychology deals with large classes of behavior like achievement tests. It is often desired in such areas to be able to summarize data by saying, for example, that one marital couple is better adjusted than another marital couple, or that one person has a better opinion of the British than has another person, or that one student has a greater knowledge of arithmetic than has another student. There has been considerable discussion concerning the utility of such orderings of persons. It is not our intention in this paper to review such discussions, but instead to present a rather new approach to the problem which seems to afford an adequate basis for quantifying qualitative data. This approach has been used successfully for the past year or so in investigating morale and other problems in the United States Army by the Research Branch of the Morale Services Division of the Army Service Forces. While this approach to quantification leads to some interesting mathematics, no knowledge of this mathematics is required in actually analyzing data. Simple routines have been established which require no knowledge of statistics, which take less time than the various manipulations now used by various
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Self-citationO’Brien, H. L., Cairns, P., & Hall, M. (2018) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2018.01.004 · Cited by 1,014
Frick, J., Kaiser, F. G., & Wilson, M. (2004) · Personality and Individual Differences · doi:10.1016/j.paid.2004.02.015 · Cited by 965
Hobart, J., & Cano, S. (2009) · Health Technology Assessment · doi:10.3310/hta13120 · Cited by 621
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Doob, L. W. (1947) · Psychological Review · doi:10.1037/h0058371 · Cited by 310
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Metadata
- Authors
- Guttman, L.
- Venue
- American Sociological Review
- Publisher
- SAGE Publications
- Published
- 1944-04
- Volume
- 9
- Issue
- 2
- Pages
- 139
- ISSN
- 0003-1224 (ISSN-L)
- OpenAlex
- W2332834163
- Cited by
- 1,317
- References
- 0
- FWCI
- 9.49
- Type
- journal article
- Language
- en
- Open access
- closed
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