7 Scatterplots

The video uses the same example as Activity 5, where we explored the relationship between mood and serotonin levels. This time, you will use the same data to produce one of the most common types of graph used to examine relationships between variables in social science research: a scatterplot.

In the previous activity, the correlation analysis showed a negative relationship between mood and serotonin levels. This means that as scores on one variable increase, scores on the other tend to decrease. But what does this relationship actually look like in the data?

A scatterplot allows us to see this. Each point on the graph represents a pair of scores, showing us how values on the two variables relate to one another. Looking at the overall pattern of points can help us identify both the direction and strength of a relationship.

The video will guide you through how to produce a scatterplot in Jamovi and explain how to interpret what you see.

Please note: later versions of Jamovi may look slightly different. You can find the options you need to produce a regression line under the ‘General Options’ menu.

Now that you have seen how to carry out a correlation analysis and produce a scatterplot in Jamovi, try these skills yourself using the example dataset provided alongside the previous activity.

You can download a PDF version of this tutorial, and the dataset used in it below:

File: Scatterplot tutorial

File: Correlation dataset

Start by reproducing the analysis shown in the video. You can then explore the data further by including additional variables in the correlation matrix and looking at their scatterplots. What patterns can you identify? Are the relationships positive or negative? Do some relationships appear stronger than others?

Practising these techniques will help you become more confident using Jamovi and, importantly, help you understand how numerical correlations and scatterplots work together to tell us about relationships between variables in research.