These are slides from a two-day Eye-Tracking Analysis workshop given at San Diego State University for the SDSU/UCSD Joint Doctoral Program in Language and Communication Sciences in January 2019. The purpose of the workshop was to provide a hands-on introduction to preprocessing eye-tracking data and analyzing those data using growth curve analysis (GCA). The focus was on eye-tracking data, but GCA is broadly applicable to longitudinal or time course data and many of the preprocessing steps apply to other kinds of physiological measures (such as EEG). Day 1 focused on general data "wrangling" and specifically preprocessing eye-tracking data. Day 2 focused on growth curve analysis. The workshop was conducted in R (and RStudio) using the following packages:
- tidyverse packages for reading, managing, and graphing data.
- lme4: for fitting growth curve models.
- gazeR: functions for preprocessing eye-tracking data and example data sets.
- A few additional data sets: Visual Search, Deviant Behavior
|Day 1, Part 1||Data Wrangling|
|Day 1, Part 2||Pre-Processing Eye-Tracking Data|
|Day 2, Part 1||GCA Basics|
|Day 2, Part 2||Non-Linear Change, Within-Subject Effects|
|Day 2, Part 3||Logistic GCA|
|Day 2, Part 4||Visualizing Higher-Order Polynomial Terms|
|Day 2, Part 5||Quantifying and Analyzing Individual Differences
Final exercise solution