Chapter 2: Normalizing Digital Trace Data, Andreas Jungherr
Over the last ten years, social scientists have found themselves confronting a massive increase in available data sources. In the debates on how to use these new data, the research potential of digital trace data has featured prominently. While various commentators expect digital trace data to create a “measurement revolution,” empirical work has fallen somewhat short of these grand expectations. In fact, empirical research based on digital trace data is largely limited by the prevalence of two central fallacies. First, the n=all fallacy: second, the mirror fallacy. These fallacies can be addressed by developing a measurement theory for the use of digital trace data. For this, researchers will have to test the consequences of variations in research designs, account for sample problems arising from digital trace data, and explicitly link signals identified in digital trace data to sophisticated conceptualizations of social phenomena. This chapter outlines the two fallacies and discusses their consequences with regard to three general areas; digital ethnography, proxies, and hybrids. The chapter closes with an assessment of how these fallacies might be constructively addressed by the systematic development of a measurement theory for the work with digital trace data in the social sciences.