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Multidimensional Explanatory Analysis of Translation Universals

Chang-Soo Lee 1

1한국외국어대학교

Accredited

ABSTRACT

The main objective of the paper is to demonstrate the usefulness of mutlivariate explanatory data analysis in analyzing datasets with multiple variables in corpus-based translation studies, particularly those geared toward testing various translation universal hypotheses. The paper accomplishes this objective by carrying out a case study in which principal component analysis (PCA) is employed to test the validity of four linguistic features which are alleged in the literature to be representative of simplification and explication hypotheses, using a comparable corpus of English translations of Korean fiction and authentic English fiction. The case study renders empircal support to the ‘conjunctive’ and ‘that’ explication hypothesis, while finding the others irrelevant as features of translation universals. In the process of the case study, the relevant steps and procedures of using PCA are illustrated, and its merits are discussed in terms of allowing an in-depth integrated explanatory analysis of multiple variables as opposed to the traditional confirmatory statistical methods that simply focus on testing statistical significance.

Citation status

* References for papers published after 2023 are currently being built.