ABSTRACT
This chapter examines the steps between data collection and the reporting of results. It considers data cleaning and outlier treatment. To perform the data cleaning procedure, researchers will likely use one or more software programs. Eye-tracking records contain a wealth of information about participant behavior. Managing all that data, however, could be a challenge without the help of special software programs. Individual participants will contribute multiple observations to a data set; similarly, items will normally be seen by more than one study participant. A growth curve analysis is a mixed-effects regression model that describes the overall response curve that emerges from participants’ eye fixations over time. Growth curve models are designed to capture longitudinal data. In a visual-world eye-tracking experiment, the intercept will normally correspond to the likelihood of looks at the target image at the outset of the time window, or Time.
