@unpublished{Kohler2015,
  author    = {Kohler, Ulrich},
  title     = {Maintaining quality},
  series = {Survey research methods},
  volume    = {9},
  journal   = {Survey research methods},
  number    = {3},
  publisher = {European Survey Research Association},
  address   = {Duisburg},
  issn      = {1864-3361},
  pages     = {139 -- 140},
  year      = {2015},
  abstract  = {Survey Research Methods has slightly revised its publication policies. Firstly, starting with the publication of this Editorial, SRM will accept - under specified conditions - manuscripts that discuss experiments in non-probability samples for peer-review. Secondly, SRM will require authors to publish replication materials of their study as Online supplement to their article. Finally, Survey Research Methods will publish replication studies of articles published in the journal. This Editorial gives reasons for these changes.},
  language  = {en}
}
@misc{KeckKohlerNauenburg2016,
  author    = {Keck, Wolfgang and Kohler, Ulrich and Nauenburg, Ricarda},
  title     = {Quality of life in the european union and the candidate countries},
  publisher = {WZB - Wissenschaftszentrum Berlin f{\"u}r Sozialforschung},
  doi       = {10.7802/1209},
  year      = {2016},
  abstract  = {Harmonized data file as the basis for comparative analysis of quality of life in the Candidate Countries and the European Union member states, based on seven different data sets, one Eurobarometer survey covering 13 Candidate Countries with an identical set of variables conducted in April 2002, the other six Standard Eurobarometer of different subjects and fielded in different years, each with another set of questions identical with the CC Eurobarometer. Selected aggregate indicators of quality of life ... describing the social situation in the EU15 and Candidate Countries.},
  language  = {en}
}
@misc{ClassKohlerKrawietz2018,
  author    = {Class, Fabian and Kohler, Ulrich and Krawietz, Marian},
  title     = {The Potsdam Grievance Statistics File},
  series = {Historical Methods: A Journal of Quantitative and Interdisciplinary History},
  journal   = {Historical Methods: A Journal of Quantitative and Interdisciplinary History},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-412843},
  pages     = {24},
  year      = {2018},
  abstract  = {The newly collected "Potsdam Grievance Statistics File" (PGSF) holds data on the number and topics of grievances ("Eingaben") that were addressed to local authorities of the German Democratic Republic (GDR) in the years 1970 to 1989. The PGSF allows quantitative analyses on topics such as participation, quality of life, and value change in the German Democratic Republic. This paper introduces the concepts of the data set and discusses the validity of its contents.},
  language  = {en}
}
@article{BradyGiesselmannKohleretal.2018,
  author    = {Brady, David and Giesselmann, Marco and Kohler, Ulrich and Radenacker, Anke},
  title     = {How to measure and proxy permanent income},
  series = {The Journal of Economic Inequality},
  volume    = {16},
  journal   = {The Journal of Economic Inequality},
  number    = {3},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {1569-1721},
  doi       = {10.1007/s10888-017-9363-9},
  pages     = {321 -- 345},
  year      = {2018},
  abstract  = {Permanent income (PI) is an enduring concept in the social sciences and is highly relevant to the study of inequality. Nevertheless, there has been insufficient progress in measuring PI. We calculate a novel measure of PI with the German Socio-Economic Panel (SOEP) and U.S. Panel Study of Income Dynamics (PSID). Advancing beyond prior approaches, we define PI as the logged average of 20+ years of post-tax and post-transfer ("post-fisc") real equivalized household income. We then assess how well various household- and individual-based measures of economic resources proxy PI. In both datasets, post-fisc household income is the best proxy. One random year of post-fisc household income explains about half of the variation in PI, and 2-5 years explain the vast majority of the variation. One year of post-fisc HH income even predicts PI better than 20+ years of individual labor market earnings or long-term net worth. By contrast, earnings, wealth, occupation, and class are weaker and less cross-nationally reliable proxies for PI. We also present strategies for proxying PI when HH post-fisc income data are unavailable, and show how post-fisc HH income proxies PI over the life cycle. In sum, we develop a novel approach to PI, systematically assess proxies for PI, and inform the measurement of economic resources more generally.},
  language  = {en}
}
@article{KohlerKreuterStuart2018,
  author    = {Kohler, Ulrich and Kreuter, Frauke and Stuart, Elizabeth A.},
  title     = {Nonprobability Sampling and Causal Analysis},
  series = {Annual review of statistics and its application},
  volume    = {6},
  journal   = {Annual review of statistics and its application},
  publisher = {Annual Reviews},
  address   = {Palo Alto},
  issn      = {2326-8298},
  doi       = {10.1146/annurev-statistics-030718-104951},
  pages     = {149 -- 172},
  year      = {2018},
  abstract  = {The long-standing approach of using probability samples in social science research has come under pressure through eroding survey response rates, advanced methodology, and easier access to large amounts of data. These factors, along with an increased awareness of the pitfalls of the nonequivalent comparison group design for the estimation of causal effects, have moved the attention of applied researchers away from issues of sampling and toward issues of identification. This article discusses the usability of samples with unknown selection probabilities for various research questions. In doing so, we review assumptions necessary for descriptive and causal inference and discuss research strategies developed to overcome sampling limitations.},
  language  = {en}
}
@article{Kohler2019,
  author    = {Kohler, Ulrich},
  title     = {Possible uses of nonprobability sampling for the social sciences},
  series = {Survey methods : insights from the field},
  journal   = {Survey methods : insights from the field},
  publisher = {Swiss Found. for Research in Social Sciences},
  address   = {Lausanne},
  issn      = {2296-4754},
  doi       = {10.13094/SMIF-2019-00014},
  pages     = {13},
  year      = {2019},
  abstract  = {This paper compares the usability of data stemming from probability sampling with data stemming from nonprobability sampling. It develops six research scenarios that differ in their research goals and assumptions about the data generating process. It is shown that inferences from data stemming from nonprobability sampling implies demanding assumptions on the homogeneity of the units being studied. Researchers who are not willing to pose these assumptions are generally better off using data from probability sampling, regardless of the amount of nonresponse. However, even in cases when data from probability sampling is clearly advertised, data stemming from nonprobability sampling may contribute to the cumulative scientific endeavour of pinpointing a plausible interval for the parameter of interest.},
  language  = {en}
}
@article{HippKohlerLeumann2019,
  author    = {Hipp, Lena and Kohler, Ulrich and Leumann, Sandra},
  title     = {How to implement respondent-driven sampling in practice},
  series = {Survey methods : insights from the field},
  journal   = {Survey methods : insights from the field},
  publisher = {Swiss Found. for Research in Social Sciences},
  address   = {Lausanne},
  issn      = {2296-4754},
  doi       = {10.13094/SMIF-2019-00009},
  pages     = {1 -- 13},
  year      = {2019},
  abstract  = {This article draws on the experience from an ongoing research project employing respondent-driven sampling (RDS) to survey (illicit) 24-hour home care workers. We highlight issues around the preparatory work and the fielding of the survey to provide researchers with useful insights on how to implement RDS when surveying populations for which the method has not yet been used. We conclude the article with ethical considerations that occur when employing RDS.},
  language  = {en}
}
@misc{KrawietzGoebelAlbrechtetal.2019,
  author    = {Krawietz, Marian and Goebel, Jan and Albrecht, Sophia and Class, Fabian and Kohler, Ulrich},
  title     = {Leben in der ehemaligen DDR},
  publisher = {German Institute for Economic Research (DIW Berlin)},
  address   = {Berlin},
  doi       = {10.5684/soep.ddr18},
  year      = {2019},
  language  = {en}
}
@misc{Kohler2020,
  author    = {Kohler, Ulrich},
  title     = {Editorial: Survey Research Methods during the COVID-19 Crisis},
  series = {Survey Research Methods},
  volume    = {14},
  journal   = {Survey Research Methods},
  number    = {2},
  address   = {Konstanz},
  issn      = {1864-3361},
  doi       = {10.18148/srm/2020.v14i2.7769},
  pages     = {93 -- 94},
  year      = {2020},
  language  = {en}
}
@article{PostClassKohler2020,
  author    = {Post, Julia C. and Class, Fabian and Kohler, Ulrich},
  title     = {Unit nonresponse biases in estimates of SARS-CoV-2 prevalence},
  series = {Survey research methods},
  volume    = {14},
  journal   = {Survey research methods},
  number    = {2},
  publisher = {European Survey Research Association},
  address   = {Duisburg},
  issn      = {1864-3361},
  doi       = {10.18148/srm/2020.v14i2.7755},
  pages     = {115 -- 121},
  year      = {2020},
  abstract  = {Since COVID-19 became a pandemic, many studies are being conducted to get a better understanding of the disease itself and its spread. One crucial indicator is the prevalence of SARS-CoV-2 infections. Since this measure is an important foundation for political decisions, its estimate must be reliable and unbiased. This paper presents reasons for biases in prevalence estimates due to unit nonresponse in typical studies. Since it is difficult to avoid bias in situations with mostly unknown nonresponse mechanisms, we propose the maximum amount of bias as one measure to assess the uncertainty due to nonresponse. An interactive web application is presented that calculates the limits of such a conservative unit nonresponse confidence interval (CUNCI).},
  language  = {en}
}