„Big Data“ aus wissenschaftssoziologischer Sicht: Warum es kaum sozialwissenschaftliche Studien ohne Befragungen gibt

Editorial: Big data and machine learning in sociology

“…The positions range from “the end of theory” and “correlation supersedes causation” proclamations (e.g., Anderson, 2008 ) to the call for a strong emphasis on theoretical reasoning to counteract technical limitations, problematic assumptions, limited interpretability, and false conclusions (e.g., Radford and Joseph, 2020 ; Wolbring, 2020 ). In any case, prominent examples such as the mispredictions of Google Flu Trends (e.g., Butler, 2013 ; Olson et al, 2013 ; Lazer et al, 2014 ) illustrated the demand for a flexible methodological framework with theory, traditional data sources and methods, as well as DBD and algorithmic approaches as complementary elements to be integrated to maximize knowledge gain (Lazer et al, 2014 ; Schnell, 2019 ). Also, unsupervised ML algorithms as exploratory tools could contribute to the inductive process of theory development.…”

Capture–Recapture Techniques for Transport Survey Estimate Adjustment Using Permanently Installed Highway-Sensors

“…Rather, the resulting data sets are large, complex, and unsystematic. Finally, the data are often held by commercial agencies (Schnell, 2019). Nevertheless, the information the data hold should be utilized in the production of official statistics (Citro, 2014; Lohr & Raghunathan, 2017).…”