Treffer 1.017
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IAB-Stellenerhebung: Betriebsbefragung zu Stellenangebot und Besetzungsprozessen, Welle 2000 bis 2021 mit Folgequartalen von 2006 bis 2022
Börschlein, Erik-Benjamin, André Diegmann, Nicole Gürtzgen, Alexander Kubis, André Pirralha, Laura Pohlan, Martin Popp & Franka Vetter (2024): IAB-Stellenerhebung: Betriebsbefragung zu Stellenangebot und Besetzungsprozessen, Welle 2000 bis 2021 mit Folgequartalen von 2006 bis 2022. (FDZ-Datenreport 06/2024 (de)), Nürnberg, 27 S. DOI:10.5164/IAB.FDZD.2406.de.v1
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PASS-Befragungsdaten verknüpft mit administrativen Daten des IAB (PASS-ADIAB) 1975-2022
Dummert, Sandra & Irakli Sauer (2024): PASS-Befragungsdaten verknüpft mit administrativen Daten des IAB (PASS-ADIAB) 1975-2022. (FDZ-Datenreport 05/2024), Nürnberg, 82 S. DOI:10.5164/IAB.FDZD.2405.de.v1
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Bridging Between Different BeH Industry Classifications via Imputation
Drechsler, Jörg & Johannes Ludsteck (2024): Bridging Between Different BeH Industry Classifications via Imputation. (FDZ-Methodenreport 04/2024 (en)), Nürnberg, 17 S. DOI:10.5164/IAB.FDZM.2404.en.v1
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Generating Synthetic Data is Complicated: Know Your Data and Know Your Generator
Latner, Jonathan P., Marcel Neunhoeffer & Jörg Drechsler (2024): Generating Synthetic Data is Complicated: Know Your Data and Know Your Generator. In: J. Domingo-Ferrer & M. Önen (Hrsg.) (2024): Privacy in Statistical Databases 2024, S. 115-128. DOI:10.1007/978-3-031-69651-0_8
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Evaluating the Pseudo Likelihood Approach for Synthesizing Surveys Under Informative Sampling
Oganian, Anna, Jörg Drechsler & Mehtab Iqbal (2024): Evaluating the Pseudo Likelihood Approach for Synthesizing Surveys Under Informative Sampling. In: J. Domingo-Ferrer & M. Önen (Hrsg.) (2024): Privacy in Statistical Databases 2024, S. 129-143. DOI:10.1007/978-3-031-69651-0_9
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An Evaluation of Synthetic Data Generators Implemented in the Python Library Synthcity
Fössing, Emma & Jörg Drechsler (2024): An Evaluation of Synthetic Data Generators Implemented in the Python Library Synthcity. In: J. Domingo-Ferrer & M. Önen (Hrsg.) (2024): Privacy in Statistical Databases 2024, S. 178-193. DOI:10.1007/978-3-031-69651-0_12
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The Complexities of Differential Privacy for Survey Data
Drechsler, Jörg & James Bailie (2024): The Complexities of Differential Privacy for Survey Data. (NBER working paper / National Bureau of Economic Research 32905), Cambridge, Mass, 18 S.
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The Impact of Mail, Web, and Mixed-Mode Data Collection on Participation in Establishment Surveys
Küfner, Benjamin, Joseph Sakshaug, Stefan Zins & Claudia Globisch (2025): The Impact of Mail, Web, and Mixed-Mode Data Collection on Participation in Establishment Surveys. In: Journal of survey statistics and methodology, Jg. 13, H. 1, S. 66-99. DOI:10.1093/jssam/smae033
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Enriching administrative data using survey data and machine learning techniques
Kunaschk, Max (2024): Enriching administrative data using survey data and machine learning techniques. In: Economics Letters, Jg. 243. DOI:10.1016/j.econlet.2024.111924
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Linked-Employer-Employee-Daten des IAB: LIAB-Längsschnittmodell (LIAB LM) 1975-2021
Panahian Fard, Dina, Alexandra Schmucker, Stefan Seth, Matthias Umkehrer & Florian Zimmermann (2024): Linked-Employer-Employee-Daten des IAB: LIAB-Längsschnittmodell (LIAB LM) 1975-2021. (FDZ-Datenreport 04/2024 (de)), Nürnberg, 76 S. DOI:10.5164/IAB.FDZD.2404.de.v1
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Linked-Employer-Employee-Data of the IAB: LIAB Longitudinal Model (LIAB LM) 1975-2021
Panahian Fard, Dina, Alexandra Schmucker, Stefan Seth, Matthias Umkehrer & Florian Zimmermann (2024): Linked-Employer-Employee-Data of the IAB: LIAB Longitudinal Model (LIAB LM) 1975-2021. (FDZ-Datenreport 04/2024 (en)), Nürnberg, 76 S. DOI:10.5164/IAB.FDZD.2404.en.v1
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Collecting Hair Samples in Online Panel Surveys: Participation Rates, Selective Participation, and Effects on Attrition
Lawes, Mario, Clemens Hetschko, Joseph Sakshaug & Michael Eid (2024): Collecting Hair Samples in Online Panel Surveys: Participation Rates, Selective Participation, and Effects on Attrition. In: Survey research methods, Jg. 18, H. 2, S. 167-185. DOI:10.18148/srm/2024.v18i2.8170
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Linking the Mannheim Enterprise Panel (MUP) with Administrative Establishment Data of IAB
Diegmann, André, Thorsten Doherr, Mirja Hälbig & Stefanie Wolter (2024): Linking the Mannheim Enterprise Panel (MUP) with Administrative Establishment Data of IAB. (FDZ-Methodenreport 03/2024), Nürnberg, 20 S. DOI:10.5164/IAB.FDZM.2403.en.v1
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Mannheimer Unternehmenspanel verknüpft mit dem Betriebs-Historik-Panel 2010–2020 (MUP-BHP 1020)
Diegmann, André, Sandra Gottschalk, Mirja Hälbig, Alexandra Schmucker & Stefanie Wolter (2024): Mannheimer Unternehmenspanel verknüpft mit dem Betriebs-Historik-Panel 2010–2020 (MUP-BHP 1020). (FDZ-Datenreport 03/2024 (de)), Nürnberg, 106 S. DOI:10.5164/IAB.FDZD.2403.de.v1
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The Mannheim Enterprise Panel linked to the Establishment History Panel of the IAB 2010–2020 (MUP-BHP 1020)
Diegmann, André, Sandra Gottschalk, Mirja Hälbig, Alexandra Schmucker & Stefanie Wolter (2024): The Mannheim Enterprise Panel linked to the Establishment History Panel of the IAB 2010–2020 (MUP-BHP 1020). (FDZ-Datenreport 03/2024 (en)), Nürnberg, 104 S. DOI:10.5164/IAB.FDZD.2403.en.v1
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NEPS-SC3-Erhebungsdaten verknüpft mit administrativen Daten des IAB (NEPS-SC3-ADIAB)
Bachbauer, Nadine (2024): NEPS-SC3-Erhebungsdaten verknüpft mit administrativen Daten des IAB (NEPS-SC3-ADIAB). (FDZ-Datenreport 02/2024 (de)), Nürnberg, 90 S. DOI:10.5164/IAB.FDZD.2402.de.v1
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NEPS-SC3 survey data linked to administrative data of the IAB (NEPS-SC3-ADIAB)
Bachbauer, Nadine (2024): NEPS-SC3 survey data linked to administrative data of the IAB (NEPS-SC3-ADIAB). (FDZ-Datenreport 02/2024 (en)), Nürnberg, 88 S. DOI:10.5164/IAB.FDZD.2402.en.v1
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Measurement error in longitudinal earnings data: evidence from Germany
Schmillen, Achim, Matthias Umkehrer & Till von Wachter (2024): Measurement error in longitudinal earnings data: evidence from Germany. In: Journal for labour market research, Jg. 58. DOI:10.1186/s12651-024-00366-x
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Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections
Bun, Mark, Marco Gaboardi, Marcel Neunhoeffer & Wanrong Zhang (2024): Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections. In: Proceedings of the ACM on Management of Data, Jg. 2, H. 2, S. 1-26. DOI:10.1145/3651595
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Whose Data Is It Anyway? Towards a Formal Treatment of Differential Privacy for Surveys
Bailie, James & Jörg Drechsler (2024): Whose Data Is It Anyway? Towards a Formal Treatment of Differential Privacy for Surveys. In: National Bureau of Economic Research (2024): Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and their Consequences, Spring 2024, Washington, S. 1-33.