Revisiting Qualitative Data Reuse

Secondary analysis of qualitative data entails reusing data created from previous research projects for new purposes. Reuse provides an opportunity to study the raw materials of past research projects to gain methodological and substantive insights. In the past decade, use of the approach has grown rapidly in the United Kingdom to become sufficiently accepted that it must now be regarded as mainstream. Several factors explain this growth: the open data movement, research funders’ and publishers’ policies supporting data sharing, and researchers seeing benefits from sharing resources, including data. Another factor enabling qualitative data reuse has been improved services and infrastructure that facilitate access to thousands of data collections. The UK Data Service is an example of a well-established facility; more recent has been the proliferation of repositories being established within universities. This article will provide evidence of the growth of data reuse in the United Kingdom and in Finland by presenting both data and case studies of reuse that illustrate the breadth and diversity of this maturing research method. We use two distinct data sources that quantify the scale, types, and trends of reuse of qualitative data: (a) downloads of archived data collections held at data repositories and (b) publication citations. Although the focus of this article is on the United Kingdom, some discussion of the international environment is provided, together with data and examples of reuse at the Finnish Social Science Data Archive. The conclusion summarizes the major findings, including some conjectures regarding what makes qualitative data attractive for reuse and sharing. </jats:p

Social scientists’ data reuse behaviors: Exploring the roles of attitudinal beliefs, attitudes, norms, and data repositories.

Many disciplines within the social sciences have a dynamic culture of sharing and reusing data. Because social science data differ from data in the hard sciences, it is necessary to explicitly examine social science data reuse. This study explores the data reuse behaviors of social scientists in order to better understand both the factors that influence those social scientists' intentions to reuse data and the extent to which those factors influence actual data reuse. Using an integrated theoretical model developed from the theory of planned behavior (TPB) and the technology acceptance model (TAM), this study provides a broad explanation of the relationships among factors influencing social scientists' data reuse. A total of 292 survey responses were analyzed using structural equation modeling. Findings suggest that social scientists' data reuse intentions are directly influenced by the subjective norm of data reuse, attitudes toward data reuse, and perceived effort involved in data reuse. Attitude toward data reuse mediated social scientists' intentions to reuse data, leading to the indirect influence of the perceived usefulness and perceived concern of data reuse, as well as the indirect influence of the subjective norm of data reuse. Finally, the availability of a data repository indirectly influenced social scientists' intentions to reuse data by reducing the perceived effort involved

Role of Communication in Data Reuse

In acknowledging the potentials of existing data, researchers’ interests in sharing and reusing data have recently emerged. However, sharing and reusing data is not a simple one-step process for researchers. Because data reusers build their work on other researchers’ findings, the process of data reuse involves various interactions and communications with other relevant parties. Exploring the nature of communications around data is thus important to fully understand data reuse practices and to support smoother processes of data reuse. This study investigates communications occurring around data during data reusers’ experiences through qualitative interview studies involving this group. This study’s results show that the communications with different stakeholders mainly support data reuse in three areas: searching, learning, and problem solving. The findings provide valuable insights into the domain of scholarly communication, data reuse, and data services

Does Data Splitting Improve Prediction?

Data splitting divides data into two parts. One part is reserved for model selection. In some applications, the second part is used for model validation but we use this part for estimating the parameters of the chosen model. We focus on the problem of constructing reliable predictive distributions for future observed values. We judge the predictive performance using log scoring. We compare the full data strategy with the data splitting strategy for prediction. We show how the full data score can be decomposed into model selection, parameter estimation and data reuse costs. Data splitting is preferred when data reuse costs are high. We investigate the relative performance of the strategies in four simulation scenarios. We introduce a hybrid estimator called SAFE that uses one part for model selection but both parts for estimation. We discuss the choice to use a split data analysis versus a full data analysis

Factors of trust in data reuse

Purpose The purpose of this paper is to quantitatively examine factors of trust in data reuse from the reusers’ perspectives. Design/methodology/approach This study utilized a survey method to test the proposed hypotheses and to empirically evaluate the research model, which was developed to examine the relationship each factor of trust has with reusers’ actual trust during data reuse. Findings This study found that the data producer (H1) and data quality (H3) were significant, as predicted, while scholarly community (H3) and data intermediary (H4) were not significantly related to reusers’ trust in data. Research limitations/implications Further disciplinary specific examinations should be conducted to complement the study findings and fully generalize the study findings. Practical implications The study finding presents the need for engaging data producers in the process of data curation, preferably beginning in the early stages and encouraging them to work with curation professionals to ensure data management quality. The study finding also suggests the need for re-defining the boundaries of current curation work or collaborating with other professionals who can perform data quality assessment that is related to scientific and methodological rigor. Originality/value By analyzing theoretical concepts in empirical research and validating the factors of trust, this study fills this gap in the data reuse literature