Skoči do osrednje vsebine

Digital transformation of quantitative data collection in social science research: Integrating survey data collection in social science research: Integrating survey data collection with big data and paradata for identifying social behaviour




1.9.2018 - 31.8.2020

Range on year:

0.56 FTE | 2020

Project leader at FDV:

prof.dr. Vasja Vehovar

Research activity:

Social sciences

The phases of the project and their realization:

WP1: Management, reporting, dissemination WP2: Conceptual and methodological developments WP3: Paradata capturing methods in web surveys WP4: Establishing a paradata set of composite indices WP5: Establishing digital trace sets of composite indices WP6: Empirical implementations and analysis WP7: Visualisation and policy guidelines WP8: Ethical and legal aspects of integrated datasets

Research Organisation:


Citations for bibliographic records:


The proposed research aims to provide theoretical and practical underpinnings for the augmentation and triangulation of digital trace data with web survey data and with corresponding paradata. The research will focus on identifying, collecting, fusing, analysing and visualising these different data types to produce rich datasets. Within this context, topics highly relevant for general social science research methodology will be explored. This includes developing new methodological approaches to data collection in the social sciences, as well as practical contributions to increase understanding of online user behaviour (e.g. skills, literacy, use and impacts, digital divide, privacy concerns, hate speech). The broader context of policy research will be addressed as well, particularly the information needs, perception and understanding of policymakers. More specifically, the proposed research will introduce several conceptual, methodological and practical contributions for combining surveys and Big Data: (1) laying the methodological foundations for augmenting and triangulating different digital data sources; (2) creating standardised sets of composite indices of digital trace data; (3) establishing specific sets of survey questions to complement the Big Data; (4) proposing a set of paradata composite indices as a professional standard for augmenting survey responses with paradata; (5) designing practical guidelines on using the new types of datasets for policy decision-makers; (6) expanding visualisation techniques for evaluating online user behaviour based on rich datasets; and (7) compiling suggestions to address ethical and legal challenges arising from rich datasets.

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