# Chapter Summary

### Chapter 10

Bivariate analysis is a statistical tech­nique designed to detect and describe the rela­tionship between two variables.A relationship is said to exist when certain values of one vari­able tend to “go together” with certain values of the other variable.

A bivariate table displays the distribu­tion of one variable across the categories of another variable.It is obtained by classify­ing cases based on their joint scores for two variables.

Percentaging bivariate tables are used to examine the relationship between two variables that have been organized in a bivariate table. The percentages are always calculated within each category of the inde­pendent variable.

Bivariate tables are interpreted by compar­ing percentages across different categories of the independent variable. A relationship is said to exist if the percentage distributions vary across the categories of the independent variable.

Variables measured at the ordinal or interval-ratio levels may be positively or negatively associated. With a positive associa­tion, higher values of one variable correspond to higher values of the other variable. When there is a negative association between vari­ables, higher values of one variable correspond to lower values of the other variable.

Elaboration is a technique designed to clarify bivariate associations. It involves the introduction of control variables to interpret the links between the independent and depen­dent variables.

In a spurious relationship, both the independent and dependent variables are influenced by a causally prior control variable, and there is no causal link between them.

In an intervening relationship, the con­trol variable follows the independent variable but precedes the dependent variable in the causal sequence.

In a conditional relationship, the bivari­ate relationship between the independent and dependent variables is different in each of the partial tables.