Читать книгу Innovations in Digital Research Methods - Группа авторов - Страница 20

As argued in the previous chapter, despite the availability of new sources of social data, making optimal use of more conventional data sources such as surveys remains of critical importance to social research. However, using survey research data can present major challenges for data management. For example, pursuing a particular research question may require linking different datasets, extracting variables, combining them and recoding their values before statistical analysis can start. In this chapter, Lambert argues that data management practices have failed to keep pace with these challenges and explains how e-Research can advance the state of the art, drawing on examples of working with quantitative datasets generated through social surveys taken from the DAMES (Data Management through e-Social Science) project.² He argues that enhanced facilities for file storage and linkage, for using metadata to describe data, and for the capture of data preparation routines (‘workflows’) can raise standards in data management and help researchers share their experience and expertise with one another. (Exercises illustrating each of these facilities can be found at the book’s website.)

Lambert concludes by examining the prospects for the adoption of more advanced data management tools and practices. Using an example where ‘bottom-up’ and ‘top down’ innovation processes might successfully complement one another, he notes how the push from journals and funding agencies for researchers to publish metadata about their data management is likely to have a decisive influence.