Demographic characteristics may be used to generate this profile. Results generated may show that after cluster analysis, respondents who belong to the upper middle to upper class socio-economic group are identified as having a high degree of health consciousness, while respondents aged between 25 and 25 are the ones who most rely on self-medication. Multidimensional scaling, meanwhile, will be useful in this example by mapping out these attitudes towards health, giving the researcher and user of research an idea about the spread of these attitudes in a multidimensional space, as well as determine the dimensions generated and in which dimensions attitudes are located or positioned. Again, as with cluster analysis, MDS can make use of the demographic characteristics to map against the attitude statements/characteristics (interesting analyses would be characteristics vs. geographic location, educational attainment, age group membership, among others).

Software programs like the SPSS and SAS have expanded its range...
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