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Home > HCAS > HCAS_PUBS > HCAS_JOURNALS > TQR Home > TQR > Vol. 29 > No. 4 (2024)

 

Abstract

This methodological paper offers a five-step model for preparing bilingual data for analysis. The article is guided by research in translating bilingual data into qualitative research. A systematic analysis of the research studies was conducted to identify workable options for preparing bilingual data for analysis. The proposed five-step model is comprised of (1) translator’s worldview and professionalism, (2) epistemological, ontological, and methodological considerations, (3) line of translation, (4) responsiveness to cultural terms and (5) eagerness to trustworthiness and reliability of the data. Researcher’s worldviews encompass the visibility of translator’s positionality for reflexivity, avoidance of biasness, and demonstration of professionalism. The bilingual data needs treatment based on epistemological, ontological, and methodological orientations which the translators need to be mindful of. The researcher must be clear on the line of translation in opting for direct translation, back-translation, single translation, or multiple translations. The translated data must be responsive to the cultural contexts to carry on sense and meaning. Depending on the methodological approaches and contextual requirements, the translation work must meet the needs of trustworthiness and validity. The literature is silent on the translations of non-verbal cues. Therefore, this problem should be dealt with separately in future research endeavors. Similarly, future research on linguistic contexts and translation work in diverse research approaches such as quantitative, qualitative, and mixed methods is needed.

Keywords

bilingual data, translation, qualitative research, systematic analysis. methodological approach

Author Bio(s)

Gulab Khilji is currently serving as Deputy Director (Curriculum) at the Bureau of Curriculum School Education Department Balochistan, Quetta. He received his PhD in Curriculum and Instruction, with a concentration in Curriculum Studies, from the University of North Texas, in 2016. He has been working on teacher education, education policy, curriculum, and education assessment in the province of Balochistan. His research interests include curriculum studies, education policy, teacher education, sociocultural theory, and qualitative research. Please direct correspondence to gulabkhilji@hotmail.com

Nazir Ahmed Jogezai is a research fellow at the Laboratory for Educational Innovation Research (LEIR), Institute of Education, National Research University Higher School of Economics, Moscow, Russia. Educational leadership and management, innovative capital and change implementation, and digitization of education and artificial intelligence (AI) in education are his areas of research interest. Recently, Jogezai has been working on the digitization of teaching and learning and AI in education across developed and least developed societies in relation to teachers’ digital competence, educational leadership, and innovation capital. His work aims to understand how the digital divide and digital inequalities across the two ends could be bridged. He continues to work on the expectations and apprehensions related to artificial intelligence in education within the context of learning ecology across developed and least developed societies. He has also been engaged in working on the interplay of innovative capital, such as the digitization of education and educational ecological systems, and how innovations can be made effective by understanding this very nexus. Please direct correspondence to ndjogezai@hse.ru

Acknowledgements

The authors acknowledge that no financial support was received for the writing or publication of this work.

Publication Date

4-5-2024

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 International License.

DOI

10.46743/2160-3715/2024.6035

ORCID ID

0000-0002-9628-6108

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