The past decades have witnessed an increase in the amount of quantitative data generated in translation and interpreting (T&I) research, a trend catalyzed by a number of interconnected factors. One notable driver behind this is the increasing sophistication of research designs in which multiple strands and/or phases of data are collected and integrated (e.g., parallel and sequential mixed-methods research) to examine complex and multifactorial T&I-related phenomena (Han 2018; Meister 2018). Another important driver relates to the fast-paced development of quantitatively oriented research such as corpus-based T&I studies (De Sutter and Lefer 2020), cognitive T&I studies (Muñoz Martín, Sun, and Li 2021), and other data-driven research such as testing and assessment of T&I (Han 2022) and quantitative surveys of T&I stakeholders (Mellinger and Hanson 2020). The third driver pertains to the growing use of sophisticated data-collection instruments, which has led to the increased complexity and availability of quantitative data. A case in point is the application of keylogging, eye tracking, brain imaging, and psychological testing to capture psycho-cognitive processes in cognitive T&I studies (Carl, Bangalore, and Schaeffer 2016). The last driver is the easier accessibility of powerful statistical programs (e.g., SPSS, R) that make data processing and analysis more flexible and less time-consuming (Mellinger and Hanson 2017). As a result of these interrelated factors, there seems to be greater frequency, diversity, and complexity in statistical analyses conducted to explore and understand quantitative data in T&I research.

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