Abstract

NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Memory and Language. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A new framework is offered that uses multilevel logistic regression (MLR) to analyze data from ‘visual world ’ eyetracking experiments used in psycholinguistic research. The MLR framework overcomes some of the problems using 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. The current article provides solutions for the analysis of data sets from eyetracking experiments that use the ‘visual world’

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