Data Sharing in Interpretive Engineering Education Research: Challenges and Opportunities from a Research Quality Perspective

Oct 15, 2026

Uploaded by Rizky

This article examines challenges and opportunities for sharing qualitative data in engineering education research. It is informed by a framework of interpretive research quality, centered on the concept of Communicative Validation. Drawing on the authors’ practice anecdotes, it analyzes what contextual aspects of the investigated social reality and research process must be understood for secondary analysis. The article argues that meaningful data sharing requires thick metadata to preserve theoretical, methodological, and value commitments and to support tentative re-use of data.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Abstract

Introduction

Literature Review

| | Ad | vances in Engineering Edu | cation | | | | --- | --- | --- | --- | --- | --- | | | | | | | | | | | | | | | SPRING 2016 Data Sharing in Interpretive Engineering Education Research: Challenges and Opportunities from a Research Quality Perspective JOACHIM WALTHER NICOLA W. SOCHACKA University of Georgia Athens, GA AND ALICE L. PAWLEY Purdue University West Lafayette, IN ABSTRACT This article explores challenges and opportunities associated with sharing qualitative data in engineering education research. This exploration is theoretically informed by an existing framework of interpretive research quality with a focus on the concept of Communicative Validation. Drawing on practice anecdotes from the authors’ work, the article examines which aspects of the social reality under investigation and what details of the contextual research process would need to be understood by a researcher attempting a secondary analysis. To foster a thoughtful discussion in the community, we propose to conceptualize data sharing as the tentative re-use of data analogous to the concept of the tentative transfer of interpretive research findings. We contend that such a tentative re-use of data would need to be supported by communicating sufficiently detailed “thick metadata” to safeguard the continuity and robustness of the theoretical, methodological, and value commitments made in the original study. Key words: interpretive research, data sharing, research quality INTRODUCTION Set in the context of recent calls for and efforts to promote data sharing in engineering education research (National Science Foundation 2014), this article explores challenges and opportunities associated with sharing qualitative data in this interdisciplinary field. More specifically, we argue that SPRING 2016 1 ADVANCES IN ENGINEERING EDUCATION Data Sharing in Interpretive Engineering Education Research: Challenges and Opportunities from a Research Quality Perspective the ever-broader range of interpretive methods of inquiry that are being adopted by the engineering education community prompts the need to discuss some of the epistemological assumptions that underpin this paradigm and implications of these assumptions for the secondary analysis of qualitative data. In the context of qualitative research in other fields, scholars have explored secondary analysis in practical terms (Hinds, Vogel, and Clarke-Steffen 1997, Bishop 2006, Fielding 2000), from an epistemological perspective (Mauthner, Parry, and Backett-Milburn 1998, Parry and Mauthner 2004, Thomson et al. 2005), and with respect to ownership and copyright of data, participant confidentiality, and informed consent (Hammersley 1997, Parry and Mauthner 2004, Yardley et al. 2014, Thomson et al. 2005) . Building on these considerations, this article explores challenges and opportunities for sharing qualitative data from a research quality perspective. More specifically, we draw on a parsimonious framework for qualitative research quality (Walther, Sochacka, and Kellam 2013) to examine how considerations of research quality, both in original and secondary studies, might inform decisions of whether or not and, if so how, to share qualitative data. We first provide a brief overview of relevant discussions in other disciplines and then introduce the Quality Framework to theoretically anchor our later practice-based explorations. LITERATURE REVIEW While secondary analysis of quantitative data is a well-established practice, secondary analysis of qualitative data is far less common (Heaton 2008) . Calls to archive and reuse qualitative data are prompted primarily by the need to leverage diminishing resources available for funding research (Parry and Mauthner 2004) . Some funding organizations, such as the Economic and Social Research Council (ESRC) in the United Kingdom, for example, require all their award-holders to offer their data for collective archiving, irrespective o

FAQs

Why does the article focus on data sharing for qualitative engineering education research?

It responds to recent calls to promote data sharing in engineering education research and addresses challenges specific to qualitative approaches in interdisciplinary settings.

What quality concept guides the article’s view of communicative validation?

The article draws on a framework of interpretive research quality and uses Communicative Validation to assess what must be understood and communicated for secondary analysis.

What does the article say is needed to make tentative re-use of qualitative data credible?

It argues that thick metadata is required to safeguard continuity and robustness of the original study’s theoretical, methodological, and value commitments.

Oct 15, 2026

Uploaded by Rizky