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  Introduction a binary variable a nominal variable an ordinal variable a scale variable
• Two variables - unpaired
  Introduction binary vs binary binary vs ordinal binary vs scale nominal vs nominal nominal vs ordinal nominal vs scale ordinal vs ordinal ordinal vs scale scale vs scale
• Two variables - paired
  Introduction two nominal two ordinal two scale
• 3+ variables - paired
  Introduction 3+ binary 3+ ordinal 3+ scale
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Nominal vs. Nominal

Part 1: Impression of the sample data (cross table)

To begin analysing a potential association between two nominal variables, we can start by getting an impression from the sample data using a so-called cross table, or also known as a contingency table.

For example, I might be interested to know if gender has an association with the marital status, and generate the following cross table from my sample.

Note that in the example the column totals add up to 100% each, which makes it easy to compare the results between the two genders. Depending on your results you might prefer to set each row as 100% or even based on the grand total.

Looking at the percentages in the example cross table, it seems that most percentages are similar with the biggest difference between Male and Female at widowed.

Rather than using a cross table we might want to visualise the results, which will be discussed on the next page.

Two nominal variables

Introduction

Impression <=

Visualisation

Test for association

Post-hoc test

Effect size

Reporting

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