Developing a framework for data quality-focused RDM to guide the data management planning process in a Social Sciences Research Organisation

Abstract

The aim of the study was to conceptualise how RDM (research data management) can facilitate best practice and the production of quality data in a social sciences research organisation, with this to be achieved by focussing on the full research data lifecycle (i.e. all the different phases) and the activities in each stage of the data lifecycle. The goal was to create a practical, quality-focused RDM framework that can be utilised to direct the data quality management planning process in a Social Sciences Research Organisation. In the first part of the study, a literature review was conducted, focussing on ten topics: research data management as a theoretical concept; data quality as a theoretical concept; data quality as documented by international RDM role-players; data quality as recognised by significant RDM initiatives; data quality as an interest for large research funders; data quality as expressed in evaluation systems; quality recommendations as expressed in books on social science methodology; quality as an element of data management planning; data quality evaluation in South African RDM initiatives; and RDM refocused to incorporate data quality. The reason for reviewing these topics was to discuss and understand how the concepts of RDM, data quality and quality assurance are perceived and addressed in the literature. The literature review chapter was a necessary point of departure for this study, making it possible to identify gaps in the knowledge in the field and bringing to light opportunities to contribute to the development of a quality-focused RDM framework. The reviewed literature facilitated the identification of measuring indicators that are used to assess how quality assurance is maintained as part of the different phases in the research data lifecycle. The criteria thus identified are social sciences data measuring criteria based on selected data quality dimensions. In the second part of the study, the framework development was outlined. The chosen strategy used to develop the quality-focused RDM framework employed a theoretical approach. The theory reviewed in the literature review chapter was used firstly as the foundation of the quality-focused RDM framework and, secondly, as the measuring criteria to assess how data is managed in the different stages of the data lifecycle. In the third part of the study, the research methodology was defined. The selected methodology involved a hybrid approach consisting of a non-empirical section comprising a literature review, and an empirical section consisting of a case study from a social sciences organisation. The case study was selected because the social sciences institution concerned already had a mature data curation system in place – one that prioritised quality assurance. Investigating how the concept of ‘data quality’ is maintained – using RDM services – was another reason why this social sciences organisation was chosen. This study is regarded as a qualitative rather than a quantitative study, as the primary aim was to establish and explore the utility for a social sciences organisation of a developed framework based on data quality. The contribution of this study lies primarily in the presentation of a revised quality process model RDM framework that includes anecdotal suggestions (from research participants) on how to better manage research data. These suggestions were used to produce a concentrated quality-focused RDM framework that is based on theoretical best practices and the refined practical measures that researchers use and trust.