Publications: Statistical Methods (KEM)
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Synthetic microdata for establishment surveys under informative sampling
Kim, H., Drechsler, J. & Thompson, K. (2021): Synthetic microdata for establishment surveys under informative sampling. In: Journal of the Royal Statistical Society. Series A, Statistics in Society, Vol. 184, No. 1, p. 255-281. DOI:10.1111/rssa.12622
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Betriebliche Ausbildung trotz Erschwernissen in der Covid-19-Krise robuster als erwartet (Serie "Corona-Krise: Folgen für den Arbeitsmarkt")
Bellmann, L., Fitzenberger, B., Gleiser, P., Kagerl, C., Kleifgen, E., Koch, T., König, C., Leber, U., Pohlan, L., Roth, D., Schierholz, M., Stegmaier, J. & Aminian, A. (2020): Betriebliche Ausbildung trotz Erschwernissen in der Covid-19-Krise robuster als erwartet (Serie "Corona-Krise: Folgen für den Arbeitsmarkt"). In: IAB-Forum No. 05.11.2020 Nürnberg, o. Sz.
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Machine learning for occupation coding - a comparison study
Schierholz, M. & Schonlau, M. (2021): Machine learning for occupation coding - a comparison study. In: Journal of survey statistics and methodology, Vol. 9, No. 5, p. 1013-1034. DOI:10.1093/jssam/smaa023
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Nurse Effects on Non-Response in Survey-Based Biomeasures
Cernat, A., Sakshaug, J., Chandola, T., Nazroo, J. & Shlomo, N. (2021): Nurse Effects on Non-Response in Survey-Based Biomeasures. In: International Journal of Social Research Methodology, Vol. 24, No. 4, p. 487-499. DOI:10.1080/13645579.2020.1832737
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How effective are enforcement measures for compliance with the minimum wage?
Bossler, M., Jaenichen, U. & Schächtele, S. (2022): How effective are enforcement measures for compliance with the minimum wage? Evidence from Germany. In: Economic and Industrial Democracy, Vol. 43, No. 2, p. 943-971. DOI:10.1177/0143831X20962193
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The R Package hmi: A Convenient Tool for Hierarchical Multiple Imputation and Beyond
Speidel, M., Drechsler, J. & Jolani, S. (2020): The R Package hmi: A Convenient Tool for Hierarchical Multiple Imputation and Beyond. In: Journal of statistical software, Vol. 95, No. 9, p. 1-48. DOI:10.18637/jss.v095.i09
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Linking Surveys with Big Data
Sakshaug, J. (2020): Linking Surveys with Big Data. Issues of Consent. In: B. Klumpe, J. Schröder & M. Zwick (Hrsg.) (2020): Qualität bei zusammengeführten Daten - Befragungsdaten, Administrative Daten, neue digitale Daten: Miteinander besser?, Wiesbaden, Springer VS p. 163-173. DOI:10.1007/978-3-658-31009-7_12
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TippingSens: An R Shiny Application to Facilitate Sensitivity Analysis for Causal Inference Under Confounding
Haensch, A., Drechsler, J. & Bernhard, S. (2020): TippingSens: An R Shiny Application to Facilitate Sensitivity Analysis for Causal Inference Under Confounding. (IAB-Discussion Paper 29/2020), Nürnberg, 39 p.
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Data linkage
Antoni, M. & Sakshaug, J. (2020): Data linkage. In: P. A. Atkinson, A. Cernat, S. Delamont, J. Sakshaug & R. A. Williams (Eds.) (2020): SAGE Research Methods foundations, p. 1-18. DOI:10.4135/9781526421036931838
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Secure Matrix Computation: A Viable Alternative to Record Linkage?
Drechsler, J. & Klein, B. (2020): Secure Matrix Computation: A Viable Alternative to Record Linkage? In: J. Domingo-Ferrer & K. Muralidhar (Hrsg.) (2020): Privacy in Statistical Databases, Cham, p. 240-254. DOI:10.1007/978-3-030-57521-2_17
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Applying data synthesis for longitudinal business data across three countries
Alam, M., Dostie, B., Drechsler, J. & Vilhuber, L. (2020): Applying data synthesis for longitudinal business data across three countries. In: Statistics in transition, Vol. 21, No. 4, p. 212-236. DOI:10.21307/stattrans-2020-039
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Combining Active and Passive Mobile Data Collection: A Survey of Concerns
Keusch, F., Struminskaya, B., Kreuter, F. & Weichbold, M. (2020): Combining Active and Passive Mobile Data Collection: A Survey of Concerns. In: C. A. Hill, P. P. Biemer, T. D. Buskirk, L. Japec, A. Kirchner, S. Kolenikov & L. E. Lyberg (Eds.) (2020): Big Data Meets Survey Science: A Collection of Innovative Methods, New York, Wiley p. 657-682. DOI:10.1002/9781118976357.ch22
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Effects of incentives in smartphone data collection
Haas, G., Kreuter, F., Keusch, F., Trappmann, M. & Bähr, S. (2020): Effects of incentives in smartphone data collection. In: C.A. Hill, P.P. Biemer, T. Buskirk, L. Japec, A. Kirchner, S. Kolenikov & L.E. Lyberg (Eds.) (2020): Big Data Meets Survey Science, p. 387-414. DOI:10.1002/9781118976357.ch13
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Measuring the Strength of Attitudes in Social Media Data
Amaya, A., Bach, R., Kreuter, F. & Keusch, F. (2020): Measuring the Strength of Attitudes in Social Media Data. In: C.A. Hill, P.P. Biemer, T. Buskirk, L. Japec, A. Kirchner, S. Kolenikov & L.E. Lyberg (Eds.) (2020): Big Data Meets Survey Science: A Collection of Innovative Methods, p. 163-192. DOI:10.1002/9781118976357.ch5
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Acceptability of app-based contact tracing for COVID-19: Cross-country survey evidence
Altmann, S., Milsom, L., Zillessen, H., Blasone, R., Gerdon, F., Bach, R., Kreuter, F., Nosenzo, D., Toussaert, S. & Abeler, J. (2020): Acceptability of app-based contact tracing for COVID-19: Cross-country survey evidence. In: JMIR mhealth and uhealth, Vol. 8, No. 8, p. 1-9. DOI:10.2196/19857
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Business Data Collection Methodology: Current State and Future Outlook
Bavdaž, M., Snijkers, G., Sakshaug, J., Brand, T., Haraldsen, G., Kurban, B., Saraiva, P. & Willimack, D. (2020): Business Data Collection Methodology: Current State and Future Outlook. In: Statistical Journal of the IAOS, Vol. 36, No. 3, p. 741-756. DOI:10.3233/SJI-200623
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Missing Data and Other Measurement Quality Issues in Mobile Geolocation Sensor Data
Bähr, S., Haas, G., Keusch, F., Kreuter, F. & Trappmann, M. (2022): Missing Data and Other Measurement Quality Issues in Mobile Geolocation Sensor Data. In: Social science computer review, Vol. 40, No. 1, p. 212-235. DOI:10.1177/0894439320944118
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Mental distress during the COVID-19 pandemic among US adults without a pre-existing mental health condition: Findings from American trend panel survey
Holingue, C., Badillo-Goicoechea, E., Riehm, K., Veldhuis, C., Thrul, J., Johnson, R., Fallin, M., Kreuter, F., Stuart, E. & Kalb, L. (2020): Mental distress during the COVID-19 pandemic among US adults without a pre-existing mental health condition: Findings from American trend panel survey. In: Preventive Medicine, Vol. 139, p. 1-8. DOI:10.1016/j.ypmed.2020.106231
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Associations Between Media Exposure and Mental Distress Among U.S. Adults at the Beginning of the COVID-19 Pandemic
Riehm, K., Holingue, C., Kalb, L., Bennett, D., Kapteyn, A., Jiang, Q., Veldhuis, C., Johnson, R., Fallin, M., Kreuter, F., Stuart, E. & Thrul, J. (2020): Associations Between Media Exposure and Mental Distress Among U.S. Adults at the Beginning of the COVID-19 Pandemic. In: American Journal of Preventive Medicine, Vol. 59, No. 5, p. 630-638. DOI:10.1016/j.amepre.2020.06.008
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The Impact of Nurse Continuity on Biosocial Survey Participation
Cernat, A. & Sakshaug, J. (2020): The Impact of Nurse Continuity on Biosocial Survey Participation. In: Survey Methods: Insights from the Field, p. 1-14. DOI:10.13094/SMIF-2020-00010