Once you have results for your free classification task, you'll need to code what stimuli participants grouped together. We recommend having around 30 participants or more for your eventual analysis. If you use the format pictured below, you will then be able to use an R script that creates similarity matrices from your data.
Make sure you label your columns this way so that the R script will work correctly.
Subject: The participant's ID
Version: The version of the task that the participant did
Context: The context for that slide
Group: Which group on that slide you are coding
Tokens: Which tokens were grouped together in that group
Let's use the following participant's results as a model. Here is their first slide:
This participant's ID is B50. They did version A of our task. This is slide 1, and In Version A, the context on the first slide is "ata". They made 5 groups of stimuli on this slide, so let's choose a group at random and code it, for instance the group at the left containing 19, 14, 23, 20, and 24. Since this is the first group we're coding on this slide, we can label it group 1 under Group and put in the token numbers under Tokens. For the token numbers, we want to separate them with commas and no spaces. It doesn't matter what order the token numbers are in within that cell. So now we have:
If we code the rest of the groups on this slide, we have:
Here is B50's second slide:
And below is the coding for this slide added to the spreadsheet. Since B50 made 4 groups on this slide, we only have 1-4 under Group:
Here is B50's slide 3:
And the coding for this slide added to the spreadsheet:
This participant's results are finished being coded! Do this for all your participants and you'll be ready to analyze your data. You'll need to save this spreadsheet as a tab-separated text file for use with the R script to create similarity matrices.
In this post I'll discuss how to create a free classification task, also known as a free sort task, which we apply to non-native perception in Daidone, Kruger, and Lidster (2015). This task is useful for determining the perceptual similarity of non-native sounds and examining what acoustic, phonological, or indexical dimensions of the stimuli matter for listeners. It can be used to examine segmental or suprasegmental phenomena and can be used to predict their discriminability (check out our slides from New Sounds 2019). Here is an example of what our Finnish length free classification task looks like in PowerPoint. The numbers on the slide are sound files that participants click on and listen to and then group by which seem similar to them.