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 of product offerings and now features numerous products allowing users to run multivariate analysis to quantitative data. Increased use of multivariate analysis can be attributed to two related causes: (1) first, the prevalence of computer technology as a means to automate specific research processes such as data analysis, and (2) second, the increasing amount, availability, and accessibility of information prompted the use of multivariate analysis and consequently, software programs that feature these techniques.

The prevalence and eventual dominance of computer technology as the researcher's aid in completing studies and researches have increased as more features are added that enabled the researcher to conduct further analyses of the variables being studied. Because of computer technologies, information became available and accessible, and information has increased as a result. With the multitude of information available to the researcher/user, there is a need to make sense of data that univariate and bivariate cannot explain or process efficiently. Thus, when the researcher...

Univariate analysis has limitations in terms of providing breadth and depth to the data generated because it only studies a variable, and can only describe actual data and not compare it against other variables. Bivariate analysis provides the researcher an opportunity to look at two variables and examine their relationship with each other. Through this type of analysis, the researcher can then make meaningful descriptions and determine the nature and strength of the association or relationship between the two variables.
Multivariate analysis, however, takes the researcher further into the data analysis by providing both breadth and depth to the data. The researcher is no longer limited to just one or two variables; she or he can study numerous variables either in groups (dependence technique) or as a whole or sing set (interdependence technique). In fact, with the help of multivariate analysis, the researcher can explore the data available in terms of possible relationships that may exist that she or he may not have thought about or looked into during the design phase of the study. (Exploratory analysis of data, of course, must be logical and does not just involve the running variables not logically related to each other -- that is, relationships that logically, do not make sense to the researcher and objectives of the study). In effect, multivariate analysis gives more meaning to data, giving the researcher a holistic perspective that will aid her/him in interpreting the results, and provide a more knowledgeable conclusion and recommendations to his/her study.