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Best practices for cleaning eye movement data in reading research.

Michael A Eskenazi1

  • 1Department of Psychology, Stetson University, DeLand, FL, 32723, USA. meskenazi@stetson.edu.

Behavior Research Methods
|May 24, 2023
PubMed
Summary

Researchers face challenges in cleaning eye movement data for reading studies. This study found that while data cleaning methods vary, they consistently preserve effect significance and power, offering data-driven suggestions for improvement.

Keywords:
Eye movement behaviorOpen scienceReading

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Area of Science:

  • Cognitive Psychology
  • Psycholinguistics
  • Reading Science

Background:

  • Studying eye movement behavior in reading requires rigorous data cleaning, particularly for fixation durations.
  • Inconsistent methods and thresholds for removing non-lexical processing eye movements complicate data analysis.
  • Standardizing data cleaning is crucial for reliable and reproducible reading research.

Purpose of the Study:

  • To investigate current data cleaning practices in eye movement research on reading.
  • To evaluate the consequences of applying different data cleaning methods on key reading effects.
  • To provide evidence-based recommendations for data cleaning in the field.

Main Methods:

  • A literature analysis of 192 published articles identified common data cleaning techniques and inconsistencies.
  • Three distinct data cleaning methods, derived from the literature review, were applied to reading data.
  • Statistical analyses examined the impact of these cleaning methods on frequency, predictability, and length effects.

Main Results:

  • Inconsistency in reporting and application of data cleaning methods was observed across published studies.
  • More aggressive data cleaning led to decreased effect size estimates but also reduced variance.
  • Despite variations in cleaning, key reading effects remained statistically significant, with high simulated power.
  • Effect sizes were largely stable, though the length effect showed a decrease with increased data removal.

Conclusions:

  • Data cleaning methods in reading research exhibit variability, impacting effect size estimates but not statistical significance or power.
  • The study offers seven suggestions grounded in open science principles to enhance data cleaning practices.
  • Adopting standardized and transparent data cleaning protocols is recommended for researchers, reviewers, and the broader scientific community.