Categorical (or discrete) variables are used to organise observations into groups that share a common trait. The trait may be nominal (e.g., sex or eye colour) or ordinal (e.g., age group, risk level), and, in general, the number of groups within a variable is 20 or fewer (Imrey & Koch, 2005).
There are various statistical procedures that can be used to analyse categorical data. For this particular piece, I’ll explain how to analyse two categorical variables by checking their statistical significance and strength of their relationship.
The dataset is derived from a list of sampled households. The aim of the data collection process was to track progress of drinking water, sanitation and hygiene using WHO/UNICEF Joint Monitoring Programme (JMP) service ladders. …