Univariate, Bivariate, and Multivariate Data

In other words, data analysis all depends upon how many variables you're analyzing

Univariate analysis is about analysis of one variable. Usually we'll find descriptive statistics (measures of central tendency), or measures of variability, i.e. the range, SD and variance. We'll often make frequency distributions to see how often a certain variable popped up in the data.

Bivariate and multivariate analysis attempt to do more than describe. They explain relationships between one or more variables: which variables are related, and how.

Bivariate implies an analysis of only two.

Multivariate analysis helps us to specify the conditions under which relationships hold. We simultaneously analyze relationships between variables.

Often we use cross-tabulations to tabulate the joint occurrence of 2 or more variables. The result is called a contingency table.

Employed Not employed Total
Men 75 25 100
Women 25 75 100

Total 100 100 200


Here you can see that with univariate analysis, you would have found that there are 100 men, or 100 women, or 100 are employed, and 100 are not employed.

A bivariate analysis allows us to say that three-fourths of the men are employed, and only one-quarter of the women are employed.

A contingency table like the one above allows you to “see” data. Another way, some might consider a more refined way to look at of looking at is to create a scatterplot. You should be able to identify one of these.

Read the post below to refresh your memories