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Thursday 16th July, 11:00 - 12:30 Room: HT-101

Using Survey Data for Spatial Analysis 2
Convenor	Professor Nina Baur (Technische Universität Berlin )
Coordinator 1	Ms Linda Hering (Technische Universität Berlin)
Coordinator 2	Ms Cornelia Thierbach (Technische Universität Berlin)

Session Details

The session aims at exploring new developments in spatial methids, seeing space either as dependent or independent variable: Researchers can ask how people think about space and construct space or they can see space as a relevant frame for social action that influences social life. Papers address one of the questions below either at a more general methodological level or using a concrete example in a specific research project:

(1) How can survey data be used for spatial analysis? Can they be used by themselves, or do they have to be mixed with other data, e.g. geodata, qualitative data?

(2) What methodological innovations concerning the spatial can be observed? (How) can traditional sociological or geographical methods be adjusted to address spatial problems within sociology?

(3) Which sampling strategies are appropriate for spatial problems?

(4) Which strategies of data analysis are appropriate for spatial analysis?

Paper Details

1. Does Context Matter? Xenophobia in Eastern Germany.
Dr Ronald Gebauer (Friedrich Schiller University of Jena)

Xenophobic attitudes are widespread among the East German population. It is, however, not clear to what extent, this is attributable to socio-demographic and socio-economic factors on an aggregate level on the East German context as such, only to mention such phenomena like ageing of the East German population, East-West migration of well-trained and highly qualified people, high unemployment rates or lower levels of contact opportunities with regard to immigrants. The contribution to the conference will address this research gap by applying advanced approaches of context or multilevel analysis to the German General Social Survey (GGSS/ALLBUS).

2. Modeling Spatial Externalities—An Application to Individual Labor Market Outcomes
Ms Alexandra Wicht (University of Siegen)
Professor Alexandra Nonnenmacher (University of Siegen)

In our presentation we focus on the significance of spatial externalities for individual labor market outcomes. In particular, we pose the question how the wider spatial context has to be operationalized in order to capture its significance for labor market outcomes. We test for different ‘zones of influence’ by comparing multilevel event history models including different spatially weighted variables.

3. Explaining preferences for co-ethnic physicians while controling for the local opportunity structure. A study conducted in in Germany using the example of Turkish migrants.
Ms Marieke Volkert (no)

Why do migrants prefer co-ethnic physicians to physicians of other ethnicities? This paper discusses mechanisms that intensify co-ethnic preferences for medical support while simultaneously accounting for the local opportunity structure of medical supply. For this purpose, two geocoded datasets were combined using GPS information. The two datasets include a survey of Turkish migrants conducted in Mannheim, Germany and a registry of physicians, identifying Turkish physicians through onomastic procedures. Central findings reveal the importance of controlling for local opportunity structures, which significantly influence the individual choice to visit a co-ethnic physician.

4. School’s ‚normative climate‘: more than students’ and parents’ mean attitudes?
Professor Alexandra Nonnenmacher (University of Siegen)
Miss Alexandra Wicht (University of Siegen)

A school’s ‘normative climate’, which can be assumed to affect school achievement, is usually measured by averages (e.g. the mean agreement to relevant items). This practice can be questioned: An average value may be inappropriate for measuring dominant attitudes, and a normative climate may (also) be defined by heterogeneity and/or extreme attitudes among students. Consequently, a normative climate would (additionally) have to be measured by distributions, minimal/maximal values etc. We analyze German NEPS data, comparing multilevel structural equation models to reveal the dimensionality and measurement of ‘school climates’.