Translation process research (TPR) relies on objective behavioral data to uncover the cognitive mechanisms underlying translation. This paper provides a comprehensive methodology for using Translog-II, a specialized tool for recording user activity data during translation tasks. We outline the complete experimental workflow—from project configuration to data collection—demonstrated through an English-to-Chinese translation-from-scratch case study. The study details the integration of Translog-II with the CRITT Translation Process Research Database (TPR-DB) to facilitate advanced post-processing. Key technical challenges are addressed, specifically the complexities of keystroke-to-word mapping for logo-graphic scripts requiring Input Method Editors (IMEs). We further demonstrate automated alignment protocols, multidimensional error annotation, and data visualization techniques utilizing Python scripts and Shiny R interfaces. The results indicate that while automated mapping is generally robust, specific technical noise, particularly regarding long deletions, can be mitigated through systematic analysis. Ultimately, this protocol establishes a reproducible framework for exploring translator behavior, enhancing the precision of data-driven insights into cognitive translation processes.