The Visual World
Data analysis for "visual world" experiments
The Visual World | R code for empirical logit regressionR code for empirical logit regression
Sat 08 Sep, 2007 at 6:38 AM
Analyzing eyetracking data requires handling two sources of nonindependence: the first due to the fact that multiple observations are sampled from the same individual (sampling-based dependencies), and the second arising out of the nature of eye movements (eye-movement-based dependencies). Sampling-based dependencies are easily handled by standard multilevel (a.k.a. "mixed-effects") regression. Handling EM-based dependencies, however, requires special treatment, without which the standard errors in the model can be underestimated—which, in turn, can inflate the Type I error rate. The two remedies I have recommended are (1) logistic regression using 'robust' standard errors and (2) empirical logit weighted regression, which uses aggregation to filter out dependencies.
Here are the files from a re-analysis of Kronmüller & Barr (2007) using empirical logit regression: (tarball) (zip). Details of the reanalysis are reported in the MLR paper.
I will post a 'walkthrough' of the code sometime soon.
Posted Sat 08 Sep, 2007 at 6:38 AM | Link | Comments (2)
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is to offer possible solutions for analyzing data from visual world
experiments, and to encourage discussion/criticism of those solutions.
I am not a statistician, and the advice presented on this website
should be taken as just that—advice. It has not been subject to
peer review, it may contain errors (which I hope that commentary from
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