ABSTRACT
This chapter analyses contingency tables and Chi-square tests when analysing associations between binary data. It considers Cramer’s V and its use as a check and a helpful measure for assessing the associations in a contingency table. The Chi-square test is said to be distribution free and is called a non-parametric test. A non-parametric test does not rely on assumptions being made about the population. Contingency tables are a useful tool in allowing the researcher to become familiar with their data. In SPSS contingency tables are called ‘crosstabs’ and in Stata ‘two-way tables’. The chapter demonstrates how odds ratios can be used with contingency tables, to find the likelihood of male coffee consumers being more likely to drink Americano, latte and espresso than females. The researcher classified the coffee drinks offered by the shop into two categories. The first group defined as drinking Americano, latte and espresso with the second group being all other types of coffee drinks.
