Total hits 17.329
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Investigating the use of nurse paradata in understanding nonresponse to biological data collection
Pashazadeh, F., Cernat, A. & Sakshaug, J. (2020): Investigating the use of nurse paradata in understanding nonresponse to biological data collection. In: K. Olson, J. D. Smyth, J. Dykema, A. Holbrook, F. Kreuter & B. T. West (Eds.) (2020): Interviewer effects from a total survey error perspective.
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Standard and non-standard employment
Gundert, S. & Stegmaier, J. (2019): Standard and non-standard employment. Different jobs, same rights? In: Soziale Welt, Vol. 70, No. 3, p. 304-331. DOI:10.5771/0038-6073-2019-3-304
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The Effects of Biological Data Collection in Longitudinal Surveys on Subsequent Wave Nonresponse
Pashazadeh, F., Cernat, A. & Sakshaug, J. (2021): The Effects of Biological Data Collection in Longitudinal Surveys on Subsequent Wave Nonresponse. In: P. Lynn (Ed.) (2021): Advances in Longitudinal Survey Methodology, p. 100-121.
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Why should(n’t) refugees be asked about their possessions? A research-ethical and methodological reflection on my fieldwork in a refugee shelter
Höpfner, E. (2022): Why should(n’t) refugees be asked about their possessions? A research-ethical and methodological reflection on my fieldwork in a refugee shelter. In: F. Yi-Neumann, A. Lauser, A. Fuhse & P. J. Bräunlein (Hrsg.) (2022): Material Culture and (Forced) Migration, London, UCL Press p. 84-98.
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Measuring and controlling for non-consent bias in linked survey and administrative data
Sakshaug, J. (2021): Measuring and controlling for non-consent bias in linked survey and administrative data. In: A. Y. Chun, M. D. Larsen, G. Durrant & J. Reiter (Eds.) (2021): Administrative records for survey methodology, Hoboken, Wiley p. 155-178.
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Combining Scientific and Non-scientific Surveys to Improve Estimation and Reduce Costs
Sakshaug, J., Wiśniowski, A., Perez-Ruiz, D. & Blom, A. (2021): Combining Scientific and Non-scientific Surveys to Improve Estimation and Reduce Costs. In: T. Rudas & G. Péli (eds.) (2021): Pathways between social science and computational social science, Springer p. 71-93. DOI:10.1007/978-3-030-54936-7_4
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Producing Official Statistics during the COVID-19 Pandemic
Jones, J., Ryan, L., Lanyon, A., Apostolou, M., Price, T., König, C., Volkert, M., Sakshaug, J., Mead, D., Baird, H., Elliott, D. & McLaren, C. (2023): Producing Official Statistics during the COVID-19 Pandemic. In: G. Snijkers, M. Bavdaž, S. Bender, S. MacFeely, J. Sakshaug, K. J. Thompson & A. v. Delden (Hrsg.) (2023): Advances in Business Statistics, Methods and Data Collection, p. 225-264.
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Labour Mobility in German Establishments during the COVID-19 Crisis: Panel Data Analyses with Special Reference to Short-Time Work and Working from Home
Bellmann, L., Bellmann, L. & Hübler, O. (2023): Labour Mobility in German Establishments during the COVID-19 Crisis: Panel Data Analyses with Special Reference to Short-Time Work and Working from Home. (IZA discussion paper / Forschungsinstitut zur Zukunft der Arbeit 15935), Bonn, 23 p.
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Methods to Estimate Causal Effects
Collischon, M. (2022): Methods to Estimate Causal Effects. An Overview on IV, DiD and RDD and a Guide on How to Apply them in Practice. In: Soziale Welt, Vol. 73, No. 4, p. 713 -735. DOI:10.5771/0038-6073-2022-4-713



