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Analysing a nominal and scale variable

Part 2: Visualisation

To visualise the sample data between a nominal and a scale variable various options exist, but two commonly used ones are a side-by-side box plot and a split histogram. For each of these options something can be said. The side-by-side box plot is a visualisation of some descriptive measurements, and therefor a bit more technical. A downside of a split histogram is that it becomes perhaps too large if you have a lot of different categories in the nominal variable.

For the example used on the previous page, I'll use a split histogram such as the one shown in Figure 1.

Figure 1. Location vs Grade for course.

The number of bars (bins) can change the look of this. There are various formal rules on how many bars there should be, and even more rule of thumbs. I recommend using between 5 and 12 bars. In this example I've used 8.

When looking at the histograms you can look at the general shape, where the peak is for each and if there might be some unusual scores. From Figure 1 we see the same things as we already established from the descriptive statistics. In the report I recommend using a ‘Introduce – Show – Tell’ approach. So when reporting this graph, it could be for example like this:

Now that we have a good impression from the sample data, we can move on to see what this sais about the population, which will be the topic for the next page.

As mentioned earlier an alternative visualisation is to use a box-plot

Nominal vs Scale

Introduction

Impression

Visualisation

Test for differences

Post-hoc test

Effect size

Reporting

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