Conference Agenda

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Session Overview
RN21_08: The Emergence of new data sources - Critical reflection
Thursday, 22/Aug/2019:
6:00pm - 7:30pm

Session Chair: Jochen Mayerl, Chemnitz University of Technology
Location: GM.326
Manchester Metropolitan University Building: Geoffrey Manton, Third Floor 4 Rosamond Street West Off Oxford Road

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Assessing Wearable Sensor Data For Small Group Research. A Benchmarking And Validation Study.

Jörg Müller1, Julio Meneses1, Anne Laure Humbert2, Elisabeth Anna Guenther3

1Universitat Oberta de Catalunya, Spain; 2Oxford Brookes, UK; 3Universität Klagenfurt, Austria

Wearable sensors are providing exciting new research opportunities for social scientists. The scholarly community has invested considerable effort to assess the validity and reliability of gathered data over the recent years (Kayhan et al., 2018; Parker et al., 2018). The grand majority of these initial studies has relied on laboratory experiments or field studies with single groups. At the same time, contributions are spread out across different strands of the social-, behavioral- and computer science literature. Findings, therefore, are scattered, and mostly limited to one specific group or field situation without means to assess the influence of wider contextual conditions on senor based data and insights based on them.

This paper addresses the problem by analyzing and comparing wearable sensor data of ten, relatively small Research and Development (R&D) teams in the context of the H2020 GEDII project (2015-2018). Inter-group variance of sensor measures are explored in the light of complementary data collected, including socio-demographics of team members, gender stereotype, personality traits, and three round-robin ratings regarding advice seeking, friendship and psychological safety. By examining how important sensor measures vary between comparable teams, a more fine-tuned picture regarding the context-sensitive nature of supposedly “objective” sensor measures starts to appear. Our research contributes to the important task of validating sociometric, sensor-based data as new, quantitative measurement tool for social scientists; a methodological proposal for research design, data pre-processing and analysis is included.

Kayhan et al. (2018). How honest are the signals? A protocol for validating wearable sensors. Behavior Research Methods, 1–27. doi:10.3758/s13428-017-1005-4.

Parker, et al (2018). Using Sociometers to Advance Small Group Research. Sociological Methods & Research, doi:10.1177/0049124118769091.

The Applicability of Big Data for Studying Human Socio-Spatial Interactions and Integration: A Systematic Literature Review

Kerli Müürisepp, Olle Järv

University of Helsinki, Finland

International migration has reached record highs in recent years. Movement of people from their familiar community to a foreign place and culture creates challenges for both migrants and host communities. The need to alleviate possible tensions and support migrants to realize their potentials puts the operationalization of current integration policies under pressure and urges researchers to strive for a better understanding of ethnic relations.

Integration is widely studied from various angles such as civic and political participation, education and labour market outcomes. Scientists concerned with the spatial aspects of integration have mainly focused on residential segregation and neighbourhood effects. Yet, meaningful social encounters and interactions take place in the extent of individuals’ entire activity spaces – in places they visit for work, education, shopping, services, socializing and leisure time. However, not much is known about those activities and interactions due to the lack of suitable data and methods.

We argue that user-generated digital data derived from mobile phones and social media platforms have the potential to narrow this gap by providing a more nuanced understanding of individuals’ complex social interactions in space and time, and therefore, open new avenues for integration research. Our research aims to (1) provide a systematic literature review on the big data approaches and methods applied for studying socio-spatial interactions; (2) assess critically the strengths, weaknesses and ethical aspects of the big data sources and methods reviewed; (3) propose a conceptual framework for implementing big data in integration research.

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