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• Fundamentals
  Introduction What is statistics Terms connected to data Measurement levels Significance SPSS basics
• One variable
  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
• Distributions
  Introduction Binomial Bivariate Normal Chi-Square (Standard) Normal Student t Wilcoxon (Signed Rank)
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Analyzing 3 or more paired scale variables

Visualization (under construction)

In the previous section we got a first impression from the sample data, by looking at the means and standard deviations. To visualise the results there are a few different options:

Bar Chart of Means
This is probably the most simplistic chart possible, but will not give much additional insight. It simply is a bar for each variable with as a height the value of the mean.

Split histogram
As with the nominal vs. scale visualisation, we could create a histogram for each variable, and simply add them underneath each other. This will already give more insight how the data was distributed for each variable than the Bar Chart of Means.

Split dot plot
We could also create a dot plot of each variable. This is similar as the split histogram, but if there are only a few data points or only a few different values this might be preferred.

Side-by-side box plot
Box-plots are a bit more technical, and are not understood quickly by those who never seen them. The show the so-called quartiles (minimum, 1st quartile, median, 3rd quartile, and maximum). They give more insight on how the data was distributed.

Parallel Coordinate chart
Another option is to create so called parallel lines. For each respondent we show how they score goes from one variable to another.

Scatterplot Matrix
The last option I would like to mention is a Scatterplot Matrix. In this graph a scatterplot is shown of each possible combination of two of the variables.

3+ Scale variables

Introduction

Impression

Visualisation

Omnibus test

Post-hoc test

Effect size

Reporting

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