'One size doesn't fit all': Lessons from interaction analysis on tailoring Open Science practices to qualitative...

The Open Science movement seeks to improve research transparency, accessibility, and public confidence in scientific findings. Most current initiatives prioritize solving reproducibility challenges inherent in hypothetico-deductive quantitative studies. Qualitative researchers are increasingly interested in adopting these open frameworks for their own work. However, no prior work had resolved how to effectively integrate these practices without ignoring the inherent diversity of qualitative inquiry. Many existing efforts suffer from a top-down approach where researchers force quantitative solutions onto qualitative problems. That uncertainty drove the authors to examine how specific qualitative traditions already handle data openness. This paper addresses the gap by analyzing established methods within interaction analysis. The authors demonstrate that these traditions offer a unique model for transparency that differs from standard quantitative approaches.

The aim of this study is to explore how Open Science practices can be effectively adapted for qualitative research. The authors seek to address the current lack of diversity in how these practices are applied to non-quantitative fields. This research investigates the tendency of current initiatives to force quantitative solutions onto qualitative problems. The study intends to contrast this top-down approach with the natural incorporation of transparency within interaction analysis. By examining conversation analysis and its related traditions, the authors aim to provide a more nuanced understanding of data sharing. The researchers want to highlight how analytic thinking has been historically embedded in specific qualitative methodologies. This work seeks to provide actionable lessons for scholars looking to improve transparency in their own qualitative studies. The ultimate goal is to move beyond a one-size-fits-all model toward a more tailored approach for the qualitative community.

Review approach involves a systematic examination of emerging literature regarding transparency practices in qualitative inquiry. The authors evaluate how current initiatives attempt to apply quantitative solutions to diverse qualitative methodologies. This assessment highlights the mismatch between existing tools and the specific needs of qualitative scholars. The study then shifts focus to the historical development of interaction analysis traditions. Researchers trace the evolution of data sharing and analytic transparency within conversation analysis since the 1960s. This analysis serves as a comparative benchmark for evaluating modern open science efforts. The authors synthesize these observations to derive a series of lessons for the broader qualitative community. This methodology emphasizes the importance of tradition-specific adaptation over universal application of standardized frameworks.

Key findings from the literature reveal that current Open Science efforts often fail to address the inherent diversity of qualitative research. The authors demonstrate that many initiatives prioritize finding problems for existing quantitative solutions rather than developing context-specific tools. The study highlights that conversation analysis has successfully embedded data sharing and analytic transparency into its methodology since the 1960s. This tradition serves as a primary example of how qualitative fields can naturally incorporate open practices. The authors identify a significant gap in how these practices are currently presented to the qualitative community. They show that a one-size-fits-all approach is ineffective for the varied traditions within qualitative inquiry. The review suggests that transparency is not a new concept for interaction analysis, but rather a long-standing feature of its research traditions. These results provide a clear contrast between forced integration and organic adoption of open science principles.

Synthesis and implications suggest that qualitative researchers should prioritize methods that naturally align with their specific analytic traditions. The authors propose that forcing standardized quantitative tools onto qualitative work often ignores the unique requirements of the field. Interaction analysis provides a successful model for embedding transparency directly into the research process. These findings indicate that sharing data and analytic thinking is not a new concept for certain qualitative traditions. The researchers argue that future adoption of open practices must be tailored to the specific needs of each qualitative sub-discipline. This review highlights that diversity within qualitative research requires diverse solutions rather than a single universal framework. The authors conclude that lessons from established traditions can guide more effective integration of open science principles. These insights provide a roadmap for qualitative scholars to enhance transparency while maintaining the integrity of their specific methodologies.