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Part of
Eye Tracking and Multidisciplinary Studies on Translation
Edited by Callum Walker and Federico M. Federici
[Benjamins Translation Library 143] 2018
► pp. 71–94

Chapter 5
Analysing variable relationships and time-course data in eye-tracking studies of translation processes and products

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Eye tracking has become a central research method in quantitative approaches in Translation Studies over the past decade. While the number of such studies is growing, methodological limitations have arisen in the application of eye tracking to the study of translation processes and their products. This chapter discusses two interrelated limitations of our current application of eye tracking: the analysis of the relationships between variables within eye tracking data, and the analysis of individuals and groups over time. Firstly, after a concise review of current applications of eye tracking in the discipline, I discuss the strengths and limitations of the traditional factorial designs used within Translation Studies. I then build upon previous arguments for the adoption of regression modeling in the future eye-tracking studies in our discipline, where I use simple in-discipline examples to show the advantages of regression modeling over factorial designs. I then extend this argument by presenting growth curve modeling as a means to analyse the rich time-course data found in eye-tracking studies of translation. Finally, I conclude by identifying the ways in which this approach can become more accessible to researchers in our discipline and by arguing that Translation Studies provides a unique and challenging testing ground for the cognitive and psychological sciences.
Article outline
  • 1.Introduction
  • 2.Background
  • 3.Factorial designs in eye-tracking studies in TS
    • 3.1Forced categorisation
    • 3.2Limitation on factors
    • 3.3Missing data
    • 3.4Treatment of individual differences
  • 4.Regression modelling in eye-tracking studies in TS
    • 4.1Forced categorisation
    • 4.2Limitation on factors
    • 4.3Missing data
    • 4.4Treatment of individual differences
    • 4.5Moving from factorial to regression designs
  • 5.Growth curve modelling for time-course data
  • 6.Conclusion
  • References
Published online: 16 October 2018
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https://doi.org/10.1075/btl.143.05doh
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