Use of statistical methods in translation and interpreting research: A longitudinal quantitative analysis of eleven peer-reviewed journals (2000–2020)

Chao Han, Xiaolei Lu and Peixin Zhang

The study reported on in the article examines the patterns and trends of statistical analysis in translation and interpreting (T&I) research, based on a longitudinal quantitative analysis of more than 3300 research articles sampled from eleven leading T&I journals (2000–2020). This evidence-based review is the first study to provide a systematic mapping of statistical methods used by T&I researchers. Our analyses suggest that (a) about 40% of the articles use statistics, and the use of statistics has been increasing over time; (b) the most frequently used inferential statistical techniques are the t-test, Pearson’s correlation, and chi-squared test; and (c) although the use of statistical methods has become increasingly diversified, about 90% of the methods used are basic-level statistics. We discuss these findings in relation to statistical teaching and learning for relevant stakeholders, especially T&I researchers.

Publication history
Table of contents

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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