Multivariate
techniques are used to study datasets in consumer and market research,
quality control and quality assurance, process optimization and process
control, and research and development. These techniques are particularlyimportant
in social science research because social researchers are generally
unable to use randomized laboratory experiments, like those used in
medicine and natural sciences. Here multivariate techniques can
statistically estimaterelationships
between different variables, and correlate how important each one is to
the final outcome and where dependencies exist between them.
Because
most data analysis tries to answer complex questions involving more
than two variables, these questions are best addressed by multivariate
statistical techniques. There are several different multivariate
techniques to choosefrom,
based on assumptions about the nature of the data and the type of
association under analysis. Each technique tests the theoretical models
of a research question about associations against the observed data. The
theoreticalmodels are based on facts plus new hypotheses about plausible associations between variables.
Multivariate
techniques allow researchers to look at relationships between variables
in an overarching way and to quantify the relationship between
variables. They can control association between variables by using cross
tabulation,partial
correlation and multiple regressions, and introduce other variables to
determine the links between the independent and dependent variables or
to specify the conditions under which the association takes place. This
gives a muchricher
and realistic picture than looking at a single variable and provides a
powerful test of significance compared to univariate techniques.
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