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

¹Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

The British Journal of Social Psychology

|August 12, 2022

Summary

This article examines how the Open Science movement, which focuses on transparency and data sharing, can be adapted for qualitative research. The authors argue that current methods often force quantitative solutions onto qualitative work, and instead suggest learning from the long-standing, built-in transparency practices of interaction analysis.

Area of Science:

Open Science practices within qualitative research methodology
Sociological research traditions and interaction analysis

Background:

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.

Purpose Of The Study:

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.

Main Methods:

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.

Main Results:

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.

Conclusions:

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.

Keywords:

conversation analysis discursive psychology interaction analysis open data open science preregistration qualitative research replication reproducibility transparency in research reproducibility crisis research methodology data sharing scientific integrity

Frequently Asked Questions

The authors propose that interaction analysis traditions, such as conversation analysis, have historically integrated data sharing and analytic transparency. Unlike quantitative fields that adopt external solutions, these traditions developed open practices as a natural component of their methodology since the 1960s.

The researchers identify conversation analysis, discursive psychology, ethnomethodology, and membership categorisation analysis as the primary traditions. These fields serve as the foundation for the authors' argument that transparency is already embedded in specific qualitative research practices.

The authors emphasize that these traditions are necessary because they demonstrate how transparency can be built into the research process from the start. This contrasts with other qualitative approaches that attempt to retrofit external quantitative tools onto their existing workflows.

The researchers utilize this data type to highlight how analytic thinking and raw data have been shared within conversation analysis since the 1960s. This evidence supports their claim that open practices are not entirely new to qualitative inquiry.

The authors measure the effectiveness of Open Science integration by contrasting top-down, quantitative-focused approaches with natural, tradition-specific incorporation. They find that the former often fails to address the diversity of qualitative research, whereas the latter succeeds.

The researchers propose that future efforts must avoid a one-size-fits-all model. They suggest that qualitative scholars should tailor open practices to the specific requirements of their unique traditions rather than adopting standardized quantitative solutions.

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