Multivariate Statistics

In this chapter, you will learn that, while in univariate relationship analysis you are analyzing data pair-wise – in other words, this variable against that – in multivariate relationship analysis, you are assessing many variables (predictor variables) against a single variable (target, outcome, or hypothesis variable) simultaneously. In other words, you are testing this, that, and the other against a single outcome.

Testing multiple variables simultaneously has the distinct advantage that interactions between the variables can be controlled for.

OK, enough of the jargon – what does "controlled for" really mean?

Well, the bottom line is that univariate analyses do not take into account any factors other than the ones in the test. A univariate analysis of this against that tells you whether there is a relationship between a pair of variables, but it doesn't tell you whether that relationship is independent of other...