Best practices for cleaning eye movement data in reading research

One challenge that comes with studying eye movement behavior is deciding how to clean the eye movement data (e.g., fixation durations) before conducting analyses. Reading researchers must decide which data cleaning methods they will use and which thresholds they will set to remove eye movements that are not reflective of lexical processing. The purpose of this project was to determine what data cleaning methods are typically used and if there are any consequences of using different data cleaning methods. In the first study, an analysis of 192 recently published articles indicated that there is inconsistency in the reporting and application of data cleaning methods. In the second study, three different data cleaning methods were applied based on the literature analysis in the first study. Analyses were conducted to determine the impact of different data cleaning methods on three commonly studied effects in reading research (frequency, predictability, and length). Overall, standardized estimates decreased for each effect when more data were removed; however, removing more data also resulted in decreased variance. As a result, effects remained significant with each data cleaning method, and simulated power remained high for both a moderate and small sample size. Effect sizes remained consistent for most effects but decreased for the length effect as more data were removed. Seven suggestions are provided that are based on open science practices with the intention of helping researchers, reviewers, and the field as a whole.