Corpus-based translation studies (CBTS) have undergone significant evolution, transitioning from descriptive methodologies to theoretical and applied approaches in recent years. However, the analysis of corpus-based research outcomes is crucial, and the absence of a unified framework often leads to less experienced researchers overlooking critical factors. This, in turn, results in varied interpretations of the same data, substantially compromising the objectivity and scientific rigor of the approach. Inspired by House’s (2014) model of translation quality assessment, Berman (2009)’s view on translation criticism, and De Sutter and Lefer (2020)’s multi-methodological, multifactorial, and interdisciplinary approach to CBTS, this study proposes a tripartite empirical-analytical framework to help researchers identify the potential factors influencing translator decision-making: textual characteristics, translator’s personal attributes, and the sociocultural context of the target language. To evaluate its utility, utilizing the mixed-effects logistic regression method, a case study is conducted to examine significant factors conditioning the reporting verb say and its Chinese translations in an English-Chinese parallel corpus of news texts, employing Appraisal Theory as the basis to determine equivalences and non-equivalences between the source language and target language. The case study shows that the framework facilitates a comprehensive analysis of the corpus findings by encompassing diverse perspectives within this scaffold. As digital technology, studies in multimodal discourse, and CBTS continue to intersect, the framework can also incorporate non-linguistic elements and AI translation tools, provided there are explicit criteria for examining translation phenomena. This framework equips researchers with a comprehensive set of perspectives, enabling them to consider as many factors as possible, thus bolstering the objectivity and scientific rigor of CBTS. The combined use of the structured framework and the multivariate analysis technique offers a holistic approach and stands as a critical advancement in CBTS by standardizing the analysis process and mitigate the subjective variability inherent in explaining translation phenomena.
