Statistical tests of association

There are many ways to test for the association between variables. We have focused on use of tables because this approach is relatively easy to understand and apply. Further, tables can be prepared entirely by hand, without the need for computer support. Sometimes tabular presentation is not the only or even the best way to test for associations among variables. In the next chapter we present several ways of conducting statistical tests of association.

Aids

Key terms

Bivariate analysis Cell Contingency table Control variable Controlled comparison

Cross classification Multivariate analysis Statistical inference Statistical test of significance

Main Points

1. Bivariate analyis is the simultaneous analysis of two variables. It is used to test for the association of an independent and dependent variable. Bivariate analyses can be completed by constructing two-way tables with columns for one variable, usually the independent variable, and rows for the dependent variable. Bivariate relations can also be tested using statistical measures of association.
2. The way bivariate analyses are done depends on the level of measurement used for each variable. Different methods are used for variables measured at the nominal, ordinal, and interval or ratio levels.
3. For nominal or ordinal variables, cross classification or contingency tables generally are used to present results.
4. Results in bivariate tables are usually expressed in percentages. To read percentages, first determine the direction in which the percentages were calculated — either down the columns or across the rows - and then compare percentages in the opposite direction. If the percentages add to 100% or close to this going down the columns, compare percentages across the rows; do the reverse if percentages add across the rows.
5. In bivariate tables based on ordinal measurements, the direction of association can be interpreted as positive or negative.
6. Multivariate analysis is the simultaneous analysis of three or more variables. It is frequently used to see whether a relationship between two variables remains when a third variable, called the control variable, is taken into account.
7. Multivariate analyses are also done to determine the separate and joint effects of two variables upon a dependent variable.

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