Sharing transparently: Caring for trustworthiness in qualitative data analysis in L2 research

Abstract

Abstract The Open Science (OS) movement promotes transparency across the research process. This study examines how transparently qualitative data analysis is reported in second language (L2) research, where unclear methods can undermine trustworthiness. We conducted a qualitative content analysis of 50 empirical articles, randomly sampled from the ten highest-ranked L2 journals (based on a combined SJR/JCR index) published between 2021 and 2025. Using Chenail’s (2009) triadic framework, we analyzed how authors (1) conceptually present their methodologies, (2) operationally describe data-analytic procedures, and (3) contextualize their methods within relevant literature. Findings reveal a substantial “transparency gap”: most studies label their methods (e.g., thematic or content analysis) yet provide limited epistemological justification, sparse detail on multi-author collaboration—the “collaboration black box”—and frequent, poorly explained methodological “mash-ups.” We argue for stronger methodological accountability by researchers, instructors, and journal gatekeepers to better align L2 qualitative research with core OS principles.