data analysis tools for research with mediator

Warning: This plot will be available from JASP version 0.12 onwards. From this plot, we may already want to conclude that the relation between affection and closeness is moderated by gender. Conversely, the relation between affection and closeness for female parrots is clear and positive. Specifically, the line is almost flat for male parrots, indicating that showing affection to your male parrot has a very small effect on the closeness between you. As you can see, the line indicating the relation between affection and closeness has a different angle for male parrots than for female parrots. Using the descriptive statistics module in JASP, we can create a scatter plot of the data, with on the x-axis the affection, on the y-axis the closeness, and with different colours for male and female parrots (by adding gender as the split variable). In addition, a gender dummy (0-1) and a gender categorical variable (Male – Female) are included. The dataset looks like the following screenshot, with an affection variable on a scale from 0-25, and a closeness variable measured on a scale of 1-10. You can download the data we generated for this example here and the JASP file here. If this is the case, we say the effect of affection on closeness is moderated by the parrot’s gender.

However, this effect may be different for a male or a female parrot. The idea is that the amount of affection shown by the owner has a positive effect on the closeness between them and their parrot. This phrasing becomes clearer through an example: say we have a (fictional) dataset where we used standardised scales to measure observed closeness (Y) of a pet parrot to its owner and the amount of affection behaviour (X) shown by the owner.

Moderation means that the effect of a variable X on variable Y is different for different levels of a third variable. In this tutorial, we start with the latter for a simple example case, focusing on how to perform hypothesis tests and how to interpret the coefficients in the model. Mediation means that the effect of a variable X on variable Y is (partially) indirect, through the variable M. Since version 0.10.1, JASP can do both! This blogpost goes through two introductory examples, showing how mediation and moderation can be performed in JASP. Over the past few years, we’ve found that mediation and moderation analysis are highly requested features.

Data analysis tools for research with mediator