Summary
In this workshop, we will look at five different analysis methods for analysing the time-course of visual world eye-tracking data (growth curve analysis, cluster-based permutation analysis, bootstrapped differences of timeseries, generalised additive modelling, and divergence point analysis). I will first introduce these analysis methods and discuss the advantages and disadvantages of each method to help you choose the appropriate method for your research question. In the tutorials, I will guide you through the analysis steps to help you understand what each code is doing.
R package
If you would like to follow along the analysis during the tutorials, please download the stats.VWP package from my GitHub website (https://github.com/aineito/stats.VWP). (Please make sure to do this a couple of days before the workshop as I will still be working on the package until then). The tutorials can be launched anytime, so I hope they will be a useful guide when you apply the analysis to your own data. If you are not very familiar with R but would like to learn how each method works, you are welcome to just watch me going through the codes. Downloading the package is completely optional.
Schedule
| Time | Day 1 | Day 2 |
| 9:00 – 9:50 | Introduction to different methods of analysing visual world eye-tracking data (talk) | Bootstrapped differences of timeseries (tutorial) |
| 9:50 – 10:00 | Break | Break |
| 10:00 – 10:50 | Growth curve analysis (tutorial) | Generalised additive modelling (tutorial) |
| 10:50 – 11:00 | Break | Break |
| 11:00 – 11:50 | Cluster-based permutation analysis (tutorial) | Divergence point analysis (tutorial) |
Reference
Ito, A. & Knoeferle, P. (2022) Analysing data from the psycholinguistic visual-world paradigm: Comparison of different analysis methods. Behavior Research Methods. doi: 10.3758/s13428-022-01969-3
Links to Videos
You can watch video recordings from Day 1 and Day 2 of the workshop on our YouTube channel:
Day 1:
https://youtu.be/GIodDcXTaQ8
Day 2:
https://youtu.be/r3jwo1dE-Ik