A problem often not detected in the interpretation of survey research is the potential interaction between subgroups within the sample and aspects of the survey. Potentially interesting interactions are commonly obscured when data are analyzed using descriptive and univariate statistical procedures. This paper suggests the use of cluster analysis as a tool for interpretation of data, particularly when such data take the form of coded categories. An example of the analysis of two data sets with known properties, one random and the other contrived, is presented to illustrate the application of cluster procedures to survey research data.
DennettS.BowersD. (1976) Introduction to multivariate techniques. London: Macmillan.
3.
BlashfieldR. K. (1980) The growth of cluster analysis: Tyron, Ward, and Johnson. Multivariate Behavioral Research, 15, 439–458.
4.
BlashfieldR. K. (1980) Propositions regarding the use of cluster analysis in clinical research. Journal of Consulting and Clinical Psychology, 48(4), 456–459.
5.
ChignellM. H.StaceyB. G. (1980) Exploratory cluster analysis of attitude structure in the New Zealand electorate. Psychological Reports, 47, 258.
6.
ChignellM. H.StaceyB. G. (1981) The classification of patients into diagnostic groups using cluster analysis. Journal of Clinical Psychology, 37, 151–153.
7.
DixonW. J.BrownM. B. (1981) BMDP statistical software. Berkeley, CA: Univer. of California.
8.
KachiganS. K. (1982) Multivariate statistical analysis. New York: Radius Press. LORR, M. (1983)Cluster analysis for social scientists. San Francisco, CA: Jossey-Bass.
9.
NieN. H.HullC. H.JenkinsJ. G.SteinbrennerK. S.BentD. H. (1970) Statistical package for the social sciences. (2nd ed.) New York: McGraw-Hill.
10.
RomesburgH. C. (1984) Cluster analysis for researchers. Belmont, CA: Lifetime Learning.
11.
SneathP. H. A.SokalR. R. (1973) Numerical taxonomy. San Francisco, CA: Freeman.
