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Analysing a binary vs. scale variable

Introduction

You might be interested if one group had a higher mean, than another group. The variable that defines the groups is then a binary variable, while the variable with the scores should be a scale variable. The analysis can be done using the following steps:

Part 1: Descriptive statistics.
Use descriptive statistics to get an impression of the data, using:

1. Statistical measures. The mean for central tendency, and standard deviation for dispersion
2. Visualise the data with a split histogram.

Part 2: Inferential statistics
After the first impression determine what can be said about the population based on your sample data by:

1. Performing a test to see if the means in the population will be equal (Welch t-test)
2. Determine how big the difference will be (effect size Cohen’s d)

The example used in this chapter will be using as a binary variable gender (which had the options male or female), and the scale variable ‘grade’, which ranged from 0 to 100.

Let's begin with the statistical measures in the next section.

Binary vs Scale

Introduction

Impression

Visualisation

Test

Effect size

Reporting

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