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Welcome to the Centre for Comparative Social Surveys.
The Centre for Comparative Social Surveys (CCSS) was established in 2003 and has since built a team of experts specialising in the design, implementation, and analysis of large scale and cross-national surveys. In addition, the CCSS experts engage in research on linking other data sources, e.g., administrative and geographical data to surveys. This includes technical solutions to anonymisation, micro simulations, and automatic coding.
The CCSS experts engage in research on linking other data sources, e.g., administrative and geographical data to surveys. This includes technical solutions to anonymisation, micro simulations, and automatic coding. The CCSS host a variety of externally funded research projects investigating methodological and substantive issues in large scale and comparative surveys.
Among these projects are:
Find out more about the people who are part of the Centre for Comparative Social Surveys
Not only does the Centre for Comparative Social Surveys (CCSS) engage in variety of methodological projects related to survey research, it also hosts several large European research projects.
The ESS is conducted biennial covering core questionnaire and including several special modules on to date topics. Among other things, the ESS asks question about:
All data and documentations are available on the European Social Survey's website.
The preparations for the latest ESS Round are managed by the ESS Team within the CCSS. Round 8 will be fielded in September 2016. This round’s specialist modules include a range of questions on attitudes towards welfare provision and energy usage and climate change.
The SERISS project develops strategies to ensure that Europe’s social science data infrastructures play an effective role in addressing the key challenges facing Europe today. It supports national and European policy makers by providing them with high-quality input on citizens’ attitudes, experiences, and behaviours.
SERISS is funded by the European Commission under Horizon 2020 (Project volume: €8.4million until June 2019). The project has collaborative links with the ESS, the Survey for Health Aging and Retirement in Europe (SHARE), the Consortium of European Social Science Data Archives (CESSDA), the Generations and Gender Programme (GGP), European Values Study (EVS), and the Wage Indicator Survey. With the support of these partners, the project is able to address key challenges in cross-national data collection, to overcome the barrier across research infrastructures, and to embrace the future of social science exploring new ways of data collection.
For more information see the SERISS website.
The ESS-SUSTAIN (Project volume: €2.3 million) project has been funded to develop a successful strategy to develop further strategies to significantly increase ESS membership, to lower the costs of participation in the ESS, and to enhance the quality of the ESS datasets. The final goal is to strengthen and sustain the ESS infrastructure.
The key aims of the project are as follows:
The grant will support a number of activities including an impact case study in member countries, the appointment of ESS ambassadors to promote the study, investigation about accessing structural funds to finance membership and enhanced communications to highlight the output arising from the survey.
Find out more about the ESS SUSTAIN project on the ESS website.
The ADDResponse project analyses Nonresponse Bias by looking at auxiliary data. The ESRC funded project holds strong ties with the ESS. ADDResponse matches small-area administrative, commercial, and geo-coded data to the ESS data collected in the UK in Round 6. The goals of ADDResponse are threefold:
Find out more about the ADDResponse project from their blog.
Making well-being count for policy is an ESRC funded research project build to sustain public and political interest in the use of well-being data and to explore pathways how to best employ these data for policy recommendations. The project holds strong ties with the ESS relying on well-being questions collected by its core questionnaire as well as special modules.
The project builds on four core areas of research in the field of well-being:
Find out more about Making Well-being Count for Policy on their website.
Stuck with the numbers? Need help with your research design? Stats software gives you weird error messages and you have no idea what they mean or how to solve the issue? General Error 040?
There is hope and there is help in the CCSS!
We have just launched a stats help desk for students and staff to support you with methodological advice on your research projects, stats trouble shooting, and tips and tricks for data analysis.
Drop-in sessions are held every Thursday in DG20, Rhind Building, between 2pm and 4pm.
Bring your research design, code, problem and we will do our very best to help you solving the issue.
Reece Thomas, K. (2016). The UK Supreme Court's latest look at State Immunity. Journal of International and Comparative Law, 3(1), pp. 149-161.
Eikemo, T., Bambra, C., Huijts, T. & Fitzgerald, R. (2016). The first pan-European sociological health inequalities survey of the general population: the European Social Survey (ESS) rotating module on the social determinants of health. European Sociological Review, doi: 10.1093/esr/jcw019
Harrison, E. K. & Smart, A. (2016). The under-representation of minority ethnic groups in UK medical research. Ethnicity and Health, doi: 10.1080/13557858.2016.1182126
Winstone, L., Widdop, S. & Fitzgerald, R. (2016). Constructing the Questionnaire: the Challenges of Measuring Views and Evaluations of Democracy Across Europe. In: M. Ferrin & H. Kriesi (Eds.), How Europeans View and Evaluate Democracy (Comparative Politics). (pp. 21-42). UK: Oxford University Press. ISBN 978-0-19-876690-2
Thomas, K., Johann, D., Kritzinger, S., Plescia, C. & Zeglovits, E. (2016). Estimating Sensitive Behavior: The ICT and High-Incidence Electoral Behavior. International Journal of Public Opinion Research, doi: 10.1093/ijpor/edw002
Butt, S., Lahtinen, K. & Brunsdon, C. (2016). Using geographically weighted regression to explore spatial variation in survey data. Paper presented at the GISRUK 2016, 30th March - 1st April 2016, London, UK.
Blom, A. G., Bosnjak, M., Cornilleau, A., Cousteaux, A. S., Das, M., Douhou, S. & Krieger, U. (2016). A Comparison of Four Probability-Based Online and Mixed-Mode Panels in Europe. Social Science Computer Review, 34(1), pp. 8-25. doi: 10.1177/0894439315574825
Turkay, C., Slingsby, A., Lahtinen, K., Butt, S. & Dykes, J. (2016). Enhancing a Social Science Model-building Workflow with Interactive Visualisation. Paper presented at the The European Symposium on Artificial Neural Networks (ESANN 2016), 27-29 Apr 2016, Bruges, Belgium.
Schnell, R. & Noack, M. (2016). Stichproben, Nonresponse und Gewichtung für Viktimisierungsstudien. In: N. Guzy, C. Birkel & R. Mischkowitz (Eds.), Viktimisierungsbefragungen in Deutschland. (pp. 8-75). Germany: Bundeskriminalamt (BKA).
Kroll, M. & Schnell, R. (2016). Anonymisation of geographical distance matrices via Lipschitz embedding. International Journal of Health Geographics, 15(1), doi: 10.1186/s12942-015-0031-7
Schnell, R. (2015). Privacy-preserving Record Linkage. In: K. Harron, H. Goldstein & C. Dibben (Eds.), Methodological Developments in Data Linkage. (pp. 201-225). UK: John Wiley & Sons. ISBN 1118745876
Fitzgerald, R. (2015). Striving for quality, comparability and transparency in cross-national social survey measurement: illustrations from the European Social Survey (ESS). (Unpublished Doctoral thesis, City University London)
Reece Thomas, K. (2015). Enforcing against state assets:the case for restricting private creditor enforcement and how judges in England have used "context" when applying the "commercial purposes" test. Journal of International and Comparative Law, 2(1),
Geurs, K. T., Thomas, T., Bijlsma, M. & Douhou, S. (2015). Automatic trip and mode detection with move smarter: First results from the Dutch Mobile Mobility Panel. Transportation Research Procedia, 11, pp. 247-262. doi: 10.1016/j.trpro.2015.12.022
Lahtinen, K., Slingsby, A., Dykes, J., Butt, S. & Fitzgerald, R. (2015). Informing Non-Response Bias Model Creation in Social Surveys with Visualisation. Paper presented at the VIS 2015, 25-10-2015 - 30-10-2015, Chicago, USA.
Schnell, R. & Borgs, C. (2015). Building a national perinatal database without the use of unique personal identifiers. Paper presented at the IEEE International Conference on Data Mining, 14-11-2015 - 17-11-2015, Atlantic City, USA.
Niedermeyer, F., Steinmetzer, S., Kroll, M. & Schnell, R. (2014). Cryptanalysis of Basic Bloom Filters Used for Privacy Preserving Record Linkage. Journal of Privacy and Condentiality, 6(2), pp. 59-79.
Fitzgerald, R., Winstone, L. & Prestage, Y (2014). A Versatile tool? Applying the Cross-national Error Source Typology (CNEST) to triangulated pre-test data. Lausanne: FORS.
Braghiroli, S. & Salini, L. (2014). How Do the Others See Us? An Analysis of Public Opinion Perceptions of the EU and USA in Third Countries. Transworld(33), pp. 1-19.
Callegaro, M., Villar, A., Krosnick, J. & Yeager, D. (2014). A Critical Review of Studies Investigating the Quality of Data Obtained With Online Panels. In: M. Callegaro, R. Baker, J. Bethlehem, A. Goritz, J. Krosnick & P. Lavrakas (Eds.), Online Panel Research: A Data Quality Perspective. (pp. 23-53). UK: John Wiley & Sons. ISBN 978-1-119-94177-4
Schoua-Glusberg, A. & Villar, A. (2014). Assessing Translated Questions via Cognitive Testing. In: K. Miller, S. Willson, V. Chepp & J. L. Padilla (Eds.), Cognitive Interviewing Methodology. (pp. 51-67). Hoboken, USA: John Wiley & Sons. ISBN 9781118383544
Fitzgerald, R., Winstone, L. & Prestage, Y. (2014). Searching For Evidence of Acculturation: Attitudes Toward Homosexuality Among Migrants Moving From Eastern to Western Europe. International Journal of Public Opinion Research, 26(3), pp. 323-341. doi: 10.1093/ijpor/edu021
Schnell, R. (2014). The Accuracy of Pre-Election Polling of German General Elections. MDA - Methods, Data, Analysis, 8(1), pp. 5-24. doi: 10.12758/mda.2014.001
Schnell, R. (2014). An efficient Privacy-Preserving Record Linkage Technique for Administrative Data and Censuses. Statistical Journal of the IAOS, 30(3), pp. 263-270. doi: 10.3233/SJI-140833
Schnell, R., Trappmann, M. & Gramlich, T. (2014). A Study of Assimilation Bias in Name-Based Sampling of Migrants. Journal of Official Statistics, 30(2), pp. 231-249. doi: 10.2478/jos-2014-0015
Villar, A., Callegaro, M. & Yang, Y. (2013). Where Am I? A Meta-Analysis of Experiments on the Effects of Progress Indicators for Web Surveys. Social Science Computer Review, 31(6), pp. 744-762. doi: 10.1177/0894439313497468
Ryan, L., Cooper, P. & Drey, N. (2013). University Research Ethics Committees as learning communities: Identifying and utilising collaboratively produced knowledge in decision-making. Research Ethics, 9(4), pp. 166-174. doi: 10.1177/1747016112437688
Douhou, S. & van Soest, A. (2013). Explaining subjective well-being: The role of victimization, trust, health, and social norms. Applied Econometrics, 31(3), pp. 52-78.
Schnell, R. (2013). Efficient private record linkage of very large datasets. Paper presented at the 59th World Statistics Congress of the International Statistical Institute, 25-30 Aug 2013, Hong Kong.
Schnell, R. (2013). Privacy-Preserving Record Linkage and Privacy-Preserving Blocking for Large Files with Cryptographic Keys using Multibit Trees. Paper presented at the Joint Statistical Meeting, 3-8 Aug 2013, Montreal, Canada.
Schnell, R., Gramlich, T., Bachteler, T., Reiher, J., Trappmann, M., Smid, M. & Becher, I. (2013). Ein neues Verfahren für namensbasierte Zufallsstichproben von Migranten. MDA - Methoden, Daten, Analysen, 7(1), pp. 5-33. doi: 10.12758/mda.2013.001
Ryan, L. (2012). "You must be very intelligent...?": Gender and Science Subject Uptake. International Journal of Gender, Science and Technology, 4(2), pp. 167-190.
Jackson, J., Bradford, B., Hough, M., Kuha, J., Stares, S., Widdop, S., Fitzgerald, R., Yordanova, M. & Galev, T. (2011). Developing European indicators of trust in justice. European Journal of Criminology, 8(4), pp. 267-285. doi: 10.1177/1477370811411458
Fitzgerald, R., Widdop, S., Gray, M. & Collins, D. (2011). Identifying sources of error in cross-national questionnaires: Application of an error source typology to cognitive interview data. Journal of Official Statistics, 27(4), pp. 569-599.
Barnes, M., Butt, S. & Tomaszewski, W. (2010). The Duration of Bad Housing and Living Standards of Children in Britain. Housing Studies, 26(1), pp. 155-176. doi: 10.1080/02673037.2010.512749
Broom, M., Crowe, M. L., Fitzgerald, M. R. & Rychtar, J. (2010). The stochastic modelling of kleptoparasitism using a Markov process. Journal of Theoretical Biology, 264(2), pp. 266-272. doi: 10.1016/j.jtbi.2010.01.012
Low, N., Butt, S., Ellis, P. & Davis Smith, J. (2007). Helping out: a national survey of volunteering and charitable giving. London: Cabinet Office.
Butt, S. & Lahtinen, K. Using auxiliary data to model nonresponse bias The challenge of knowing too much about nonrespondents rather than too little?. Paper presented at the International Workshop on Household Nonresponse 2015, 02 Sep 2015 - 04 Sep 2015, Leuven, Belgium.
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