Analyzing 'visual world' eyetracking data using multilevel logistic regression

A new framework is offered that uses multilevel logistic regression (MLR) to analyze data from 'visual world' eye-tracking experiments used in psycholinguistic research. The MLR framework overcomes some of the problems with conventional analyses, making it possible to incorporate time as a continuous variable and gaze location as a categorical dependent variable. The multilevel approach minimizes the need for data aggregation and thus provides a more statistically powerful approach. With MLR, the researcher builds a mathematical model of the overall response curve that separates the response into different temporal components. The researcher can test hypotheses by examining the impact of independent variables and their interactions on these components. A worked example using MLR is provided. (C) 2007 Elsevier Inc. All rights reserved.