Methoden

methoden@mailman.uni-konstanz.de

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1861 discussions

GESIS Courses
by Ernst, André 29 Jun '23

29 Jun '23

***Apologies for cross-posting*** Dear colleagues, We want to highlight a selection of GESIS courses about various topics on causal identification: Introduction to Methods of Causal Analysis, Directed Acyclic Graphs, Causal Inference Using Survey Data, Causal Mediation Analysis, and Propensity Score Matching. You will find a complete list of all GESIS Training courses online<https://training.gesis.org/?lang=en_US> and attached to this email. 05. - 07.07.2023 Einführung in die Methoden der modernen Kausalanalyse<https://bit.ly/ModerneKausalanalyse> (Mannheim) Michael Gebel 02. - 04.08.2023 Causal Inference with Directed Acyclic Graphs (DAGs)<https://bit.ly/GSS23_SCD> (Online) Paul Hünermund 07. - 11.08. 2023 Causal Inference Using Survey Data<https://bit.ly/GSS23_C3> (Cologne) Heinz Leitgöb, Tobias Wolbring 11. - 15.12. 2023 Causal Mediation Analysis<https://bit.ly/CausalMediation> (Online) Felix Thoemmes 20. - 21.02.2024 Propensity Score Matching: Computation and Balance Estimation for two and more groups in R<https://bit.ly/Propensity_Score_Matching> (Online) Julian Urban All the best, GESIS Training ------- July 05. - 07.07. Interactive Data Analysis with Shiny<https://bit.ly/IDA_Shiny> (Online) Jonas Lieth, Paul C. Bauer 05. - 07.07. Einführung in die Methoden der modernen Kausalanalyse<https://bit.ly/ModerneKausalanalyse> (Mannheim) Michael Gebel 17. - 19.07. Logistic Regression and Beyond: Modelling Categorical Dependent Variables<https://bit.ly/LogRegression> (Cologne) Sophie Suda August 02. - 04.08. Pretesting Survey Questions<https://bit.ly/GSS23_SCA> (Online) Cornelia Neuert, Timo Lenzner 02. - 04.08. Mixed-Mode Surveys<https://bit.ly/GSS23_SCB> (Online) Sven Stadtmüller, Henning Silber, Yannick Diehl, Peter Schmidt 02. - 04.08. Introduction to Stata for Data Management & Analysis<https://bit.ly/GSS23_SCC> (Online) Irina Bauer, Annika Stein 02. - 04.08. Causal Inference with Directed Acyclic Graphs (DAGs)<https://bit.ly/GSS23_SCD> (Online) Paul Hünermund 07. - 11.08. Designing, Implementing, and Analyzing Longitudinal Surveys<https://bit.ly/GSS23_C1> (Cologne) Tarek Al Baghal, Alexandru Cernat 07. - 11.08. Applied Systematic Review and Meta-Analysis<https://bit.ly/GSS23_C2> (Cologne) Jessica Daikeler, Sonila Dardha 07. - 11.08. Causal Inference Using Survey Data<https://bit.ly/GSS23_C3> (Cologne) Heinz Leitgöb, Tobias Wolbring 07. - 11.08. (Non-)Probability Samples in the Social Sciences<https://bit.ly/GSS23_C4> (Online) Carina Cornesse, Olga Maslovskaya 07. - 08.08. Qualitative Interviews - Theorie und Praxis<https://bit.ly/QualitativeInterviews> (Mannheim) Günter Mey, Paul S. Ruppel 10. - 11.08. Grounded-Theory-Methodologie<https://bit.ly/23-08-Grounded_Theory_Methodologie> (Mannheim) Günter Mey, Paul S. Ruppel 14. - 18.08. Advanced Survey Design<https://bit.ly/GSS23_C5> (Cologne) Bella Struminskaya, Angelo Moretti 14. - 18.08. Advanced Questionnaire Design<https://bit.ly/GSS23_C6> (Cologne) Marek Fuchs 14. - 18.08. Introduction to R for Data Analysis<https://bit.ly/GSS23_C7> (Cologne) Jan Schwalbach, Dennis Abel 14. - 18.08. Missing Data and Multiple Imputation<https://bit.ly/GSS23_C8> (Online) Florian Meinfelder, Angelina Hammon 21. - 25.08. Collecting and Analyzing Longitudinal Social Network Data<https://bit.ly/GSS23_C9> (Cologne) Lars Leszczensky, Sebastian Pink 21. - 25.08. Data Science Techniques for Survey Researchers<https://bit.ly/GSS23_C10> (Cologne) Anna-Carolina Haensch 29. - 31.08. Qualitative Netzwerkanalyse<https://bit.ly/23-08-QualitativeNetzwerkanalyse> (Cologne) Markus Gamper, Laura Behrmann 30.08. - 01.09. Mehrebenenanalyse in Stata<https://bit.ly/MehrebenenanalyseStata> (Cologne) Hermann Dülmer September 04. - 06.09. Introduction to Python<https://bit.ly/23-09-Intro_Python> (Online) Hannah Béchara, Paulina Garcia Corral 05. - 07.09. Introduction to R<https://bit.ly/23-09-Intro_R> (Online) Natalia Umansky, Christian Pipal 11. - 15.09. Introduction to Computational Social Science with R<https://bit.ly/IntroCSSwithR> (Mannheim) Aleksandra Urman, Max Pellert 11. - 15.09. Introduction to Computational Social Science with Python<https://bit.ly/IntroCSSwithPython> (Mannheim) Milena Tsvetkova, Patrick Gildersleve 11. - 15.09. Big Data and Computation for Social Data Science<https://bit.ly/BigDataComputation> (Mannheim) Akitaka Matsuo, David (Yen-Chieh) Liao 18. - 22.09. Automated Web Data Collection with R<https://bit.ly/WebDatawithR> (Mannheim) Allison Koh, Hauke Licht 18. - 22.09. Automated Web Data Collection with Python<http://bit.ly/WebDatawithPython> (Mannheim) Felix Soldner, Jun Sun, Leon Fröhling 18. - 22.09. Automated Image and Video Analysis with Python<http://bit.ly/ImageVideoAnalysis> (Mannheim) Andreu Casas, Felicia Loecherbach 25. - 29.09. Social Network Analysis with R<http://bit.ly/NetworkAnalysiswithR> (Mannheim) Michał Bojanowski 25. - 29.09. Introduction to Machine Learning for Text Analysis with Python<http://bit.ly/IntroTextAnalysis> (Mannheim) Damian Trilling, Anne Kroon 25. - 29.09. From Embeddings to Transformers: Advanced Text Analysis with Python<http://bit.ly/AdvancedTextAnalysis> (Mannheim) Hauke Licht, Jennifer Victoria Scurrell 28. - 29.09. Einführung in die qualitative Inhaltsanalyse<https://bit.ly/23-08-QualitativeInhaltsanalyse> (Cologne) Markus Janssen, Christoph Stamann October 30. - 31.10.<https://training.gesis.org/?site=pICS&pID=0xBB4D8590048C49EAA035610865B3354F> Fundamentals and Advanced Topics in Modeling Interaction Effects<https://bit.ly/ModelingInteractionEffects> (Mannheim) Janina Beiser-McGrath, Liam F. Beiser-McGrath November 06. - 08.11. Sequence Analysis in the Social Sciences<https://bit.ly/SequenceAnalysis> (Online) Marcel Raab, Emanuela Struffolino 13. - 14.11. Questionnaires for Cross-Cultural and Cross-National Surveys<https://bit.ly/QuestionnairesCCandCNSurveys> (Mannheim) Dorothée Behr, Cornelia Neuert, Lydia Repke 13. - 14.11. Power Analysis Through Simulation in R<https://bit.ly/PowAnalysis> (Online) Niklas Johannes 13. - 15.11. Synthetic Data: Generation and Evaluation<https://bit.ly/SynthData> (Online) Thom Volker 16. - 17.11. Workflows for Reproducible Research with R & Git<https://bit.ly/WfRRwRGit> (Online) Johannes Breuer, Bernd Weiß, Arnim Bleier 22. - 24.11. Introduction to Bayesian Statistics<https://bit.ly/IntroBayStat> (Online) Denis Cohen 28. - 30.11. Research Data Management and Open Science<https://bit.ly/23-11-RDM> (Online) Anja Perry, Sebastian Netscher 29.11. - 01.12.<https://training.gesis.org/?site=pICS&pID=0x18BDFB0927354262800D009C2BB8A371> Einführung in die Strukturgleichungsmodellierung<https://bit.ly/Strukturgleichungsmodellierung> (Cologne) Marie-Ann Sengewald December 06. - 08.12. Going Cross-Lingual: Computational Methods for Multilingual Text Analysis<https://bit.ly/MultilingualTextAnalysis> (Cologne) Hauke Licht, Fabienne Lind 07. - 15.12. Introduction to Event History Analysis<https://bit.ly/EventHis2023> (Online) Jan Skopek 11. - 15.12. Causal Mediation Analysis<https://bit.ly/CausalMediation> (Online) Felix Thoemmes 13. - 15.12. Introduction to Stata<https://bit.ly/23-12-Intro_Stata> (Online) Alexandra Asimov, Katrin Firl ** 2024 ** February 20. - 21.02. Propensity Score Matching: Computation and Balance Estimation for two and more groups in R<https://bit.ly/Propensity_Score_Matching> (Online) Julian Urban 21. - 23.02. Durchführung qualitativer Interviews<https://bit.ly/QualInterviews24> (Cologne) Nicole Bögelein, Katharina Leimbach March 20. - 22.03. Applied Multiverse Analysis<https://bit.ly/App-multiverse-analysis> (Online) Reinhard Schunck --- Dr. André Ernst [he / him] Scientific Coordinator of GESIS Workshops GESIS - Leibniz Institute for the Social Sciences

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Zeitreihen als Thema der Methoden-Reihe des AK Historische Soziologie
by Arne Dressler 26 Jun '23

26 Jun '23

Liebe Liste, ein aktueller Veranstaltungshinweis: Am Mittwoch, den 28.06.23, steht auf der Tagesordnung der *Methoden-Reihe des AK Historische Soziologie* das Thema *Zeitreihen*. Dafür stellen Sebastian Kohl (FU-Berlin) und Volker Müller-Benedict (Europa-Universität Flensburg) ihr *methodisches Vorgehen* vor: von der Zusammenstellung des Datensatzes bis zu seiner Analyse. Inhaltlich werden mit privaten Versicherungsdaten *wirtschaftssoziologische und* mit universitären Abschlussnoten *bildungssoziologische Forschungsinteressen* angeschnitten. *Einwahl-Link*: https://lmu-munich.zoom.us/j/69942696962?pwd= MjNTbXV6MjR6S3JMZE5RWit3d0UvQT09 *Beginn*: 18 Uhr (s.t.) *Ende*: 19.30 Uhr Mit den besten Grüßen Arne Dreßler

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CfP - Nachwuchspreis der DGS-Sektion für soziologische Netzwerkforschung
by Repke, Lydia 22 Jun '23

22 Jun '23

Liebe Kolleg*innen, die Sektion "Soziologische Netzwerkforschung" der Deutschen Gesellschaft für Soziologie prämiert Nachwuchswissenschaftler*innen für herausragende deutsch- oder englischsprachige Aufsätze im Bereich der soziologischen Netzwerkforschung oder in angrenzenden Fachgebieten aus den Jahren 2021-2023. Es werden Eigen- und Fremdnominierungen entgegengenommen. Die Jury besteht aus * Prof. Dr. Beate Völker * Prof. Dr. Thomas Grund * Prof. Dr. Isabel Borucki Deadline ist der 15.09.2023. Weitere Informationen entnehmen Sie bitte der beigefügten Ausschreibung. Mit besten Grüßen Lydia Repke --------------------------------- Dr. Lydia Repke Angebotsmanagerin des Survey Quality Predictors (SQP) Mitglied des Sprecherkreises der DGS-Sektion für Soziologische Netzwerkforschung GESIS - Leibniz-Institut für Sozialwissenschaften | Survey Design and Methodology Postfach 12 21 55 | 68072 Mannheim Tel.: +49 (0) 621 1246-581 www.gesis.org<http://www.gesis.org/> Lust mehr darüber zu erfahren, wie man Daten erhebt und analysiert, damit man mit ihnen gesellschaftliche Phänomene sinnvoll interpretieren kann? "Die Fakten dicke! Der GESIS Podcast" bietet Antworten dazu: podcast.gesis.org<https://podcast.gesis.org/>. (mit Begleitmaterial)

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Stellenausschreibungen Promotion & Postdoc, LMU München, Lehrstuhl für Soziale Ungleichheit und Soziale Strukturen
by Laila Schmitt 16 Jun '23

16 Jun '23

Liebe Kolleg:innen, der Lehrstuhl für Soziale Ungleichheit und Soziale Strukturen an der LMU München (Prof. Dr. Fabian Pfeffer) sucht auf den 1. Oktober 2023 neue Mitarbeitende, sowohl zur Promotion also auch als Postdocs (3 Jahre mit möglicher Verlängerung). Bewerbungsschluss ist der 5. Juli 2023. - Promotion: https://job-portal.lmu.de/jobposting/b07f5f4fbc353507face0d063c951eb0261a9b… - Postdoc: https://job-portal.lmu.de/jobposting/6ca7daf88c8fbd370ea34eabd757bd4e3fc46d… Bei Fragen wenden Sie sich gerne direkt an Prof. Pfeffer unter Fabian.Pfeffer(a)lmu.de. Beste Grüße Laila Schmitt ~~~ [ENGLISH VERSION FOLLOWS] ~~~ Dear colleagues, the Chair of Social Inequality and Social Structures at the LMU Munich (Prof. Dr. Fabian Pfeffer) is looking for new team members to start October 1 2023, both at the doctoral and postdoctoral level (3 year initial contract with the option of extension). Application due date is July 5, 2023 - PhD position: https://job-portal.lmu.de/jobposting/48cac30b46097b99c21f946f5ba7e38e53a4d2… - Postdoc position: https://job-portal.lmu.de/jobposting/a6dff860293580d309e16613bf99b9c48c41e3… For questions, please contact Prof. Pfeffer directly at Fabian.Pfeffer(a)lmu.de. Best wishes, Laila Schmitt -- Laila Schmitt, M.A. Department of Sociology LMU Munich Konradstr. 6 80801 Munich +49 (0) 89 2180 3219 <www.ls4.soziologie.lmu.de/schmitt>www.ls4.soziologie.lmu.de/schmitt

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Workshop "Theoriebildung und Theorieprüfung in der analytisch-empirischen Soziologie" - Programm
by Gunnar Otte 16 Jun '23

16 Jun '23

Liebe Kolleg:innen, die Akademie für Soziologie hat einen Arbeitskreis „Methodologie der Sozialwissenschaften“ eingerichtet, um methodologische Grundlagen der Theoriebildung und empirischen Forschung zu diskutieren. Der Auftaktworkshop findet am 6. und 7. September 2023 an der Johannes Gutenberg-Universität Mainz statt und beschäftigt sich mit der Theoriebildung und Theorieprüfung in der analytisch-empirischen Soziologie. Grundlegende methodologische Prinzipien und aktuelle Herausforderungen sollen gleichermaßen zur Sprache kommen. Keynote-Vorträge halten Karl-Dieter Opp, Andreas Diekmann, Andreas Flache und Catherine Herfeld. Neben weiteren Fachvorträgen gibt es zwei Roundtables zur Relevanz von replikativer Forschung für die Prüfung und Weiterentwicklung von Theorien sowie zur Vermittlung analytisch-empirisch ausgerichteter soziologischer Theorie und sozialwissenschaftlicher Methodologie im Studium. Das Programm finden Sie im Anhang. Die Teilnahme am Workshop ist kostenlos. Wir freuen uns über zahlreiche Interessierte! Anmeldefrist ist der 31.7.2023. Beste Grüße Gunnar Otte, Natascha Nisic, Nico Sonntag, Hartmut Esser, Werner Raub

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Online-Reihe zu den Methoden und analytischen Perspektiven der Historischen Soziologie im SS 2023
by Arne Dressler 09 Jun '23

09 Jun '23

Liebe Liste, aus dem aktuellen Semesterprogramm der *Online-Reihe zu den Methoden und analytischen Perspektiven der Historischen Soziologie* dürften zwei Veranstaltungen für die Sektion Methoden der empirischen Sozialforschung relevant sein: 14. Juni 2023 *Historische Sozialforschung* Heinrich Best (Friedrich-Schiller-Universität Jena) 28. Juni 2023 *Zeitreihen* Sebastian Kohl (Freie Universität Berlin) Volker Müller-Benedict (Europa-Universität Flensburg) Zeit: 18-19.30 Uhr Link: https://lmu-munich.zoom.us/j/69942696962?pwd=MjNTbXV6MjR6S3JMZE5RWit3d0UvQT… (Kenncode: 959258) Detaillierte Informationen und eine Übersicht über die weiteren Termine, Themen und Gäste in diesem Semester gibt das beigefügte Poster. Mit den besten Grüßen Arne Dreßler

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MASS Keynotes by Helen Nissenbaum (Cornell Tech) and Kathleen Cagney (University of Michigan) – June 22 & 23, 2023
by Florian Keusch 31 May '23

31 May '23

Dear colleagues, We are happy to announce that we are able to live-stream the keynotes from this year’s Mobile Apps and Sensors in Surveys <https://massworkshop.org/2023-workshop-latest-information/>(MASS) workshop. You can join the keynotes in person at the University of Manchester <https://www.socialsciences.manchester.ac.uk/social-statistics/>or online for free but registration is required. 22^nd of June 2023, 15:30-16:30 (BST)/16:30-17:30 (CEST)/10:30am-11:30am (EST) Contextual Integrity in Theory and Empirical Application Helen Nissenbaum <https://nissenbaum.tech.cornell.edu/>(Cornell Tech) Sponsored by the Centre for Digital Trust and Society <https://www.socialsciences.manchester.ac.uk/dts/> Link to registration page: https://www.eventbrite.co.uk/e/contextual-integrity-in-theory-and-empirical… <https://www.eventbrite.co.uk/e/contextual-integrity-in-theory-and-empirical…> 23^th of June 2023, 13:00-14:00 (BST)/14:00-15:00 (CEST)/8:00am-9:00am (EST) The Promise of Activity Space Approaches: Urban/Rural Comparisons and Implications for Research on Context Kathleen Cagney <https://lsa.umich.edu/soc/people/faculty/kcagney.html>(University of Michigan) Sponsored by the National Centre for Research Methods <https://www.ncrm.ac.uk/> Link to registration page: https://www.eventbrite.co.uk/e/the-promise-of-activity-space-approaches-tic… <https://www.eventbrite.co.uk/e/the-promise-of-activity-space-approaches-tic…> Details of the talks below. Best regards, Florian Keusch On behalf of the MASS workshop organizers/ / / / /Details of the talks/ *Contextual Integrity in Theory and Empirical Application**/ 22^rd of June, 15:30 – 16:30 (BST)/**/ /* */Abstract:/*The theory of contextual integrity (CI) defines privacy as appropriate flow of personal information, answering the need for a conception of privacy that is meaningful to ordinary people, explains privacy's ethical claim, and underscores why privacy deserves protection through regulation and technology. In the 2010 book, I argued that CI meets all three benchmarks, while releasing privacy from the grips of one-dimensional definitions -- control over information about ourselves, stoppage of flow (secrecy), and fetishization of specific, “sensitive” attributes (e.g. identity, health.) Since then, our understanding of CI has been further shaped and informed by studies based on social scientific methods and, in turn, has informed and shaped them. My talk briefly describes key ideas defining contextual integrity, focusing on its interconnections with empirical social scientific studies of privacy/./ */Bio:/*Helen Nissenbaum is a professor of Information Science and founding director of the Digital Life Initiative at Cornell Tech, NYC. Her work on ethical and political dimensions of digital technologies spans issues of privacy, bias, trust online, design, and accountability in computational and algorithmic systems. Prof. Nissenbaum’s publications, which include the books,Obfuscation: A User's Guide for Privacy and Protest <https://mitpress.mit.edu/books/obfuscation>, with Finn Brunton (MIT Press, 2015), Values at Play in Digital Games <http://valuesatplay.org/vap-the-book>, with Mary Flanagan (MIT Press, 2014), and Privacy in Context: Technology, Policy, and the Integrity of Social Life <https://www.sup.org/books/title/?id=8862> (Stanford, 2010), have been translated into seven languages, including Polish, Chinese, and Portuguese. Recipient of the 2014 Barwise Prize of the American Philosophical Association and the IACAP Covey Award for computing, ethics, and philosophy, Prof. Nissenbaum has contributed to privacy-enhancing free software, <https://www.trackmenot.io/>TrackMeNot (against profiling of Web search histories) and AdNauseam <http://adnauseam.io/> (against profiling based on ad clicks). She holds a Ph.D. in philosophy from Stanford University and a B.A. (Hons) in Philosophy and Mathematics from the University of the Witwatersrand <http://www.wits.ac.za/>, South Africa. Prior to joining Cornell Tech, she directed NYU’s Information Law Institute. */Link to registration page: /*https://www.eventbrite.co.uk/e/contextual-integrity-in-theory-and-empirical-application-tickets-643026508597 <https://www.eventbrite.co.uk/e/contextual-integrity-in-theory-and-empirical…>* * * * *The Promise of Activity Space Approaches: Urban/Rural Comparisons and Implications for Research on Context/ 23^th of June, 13:00 – 14:00 (BST)/**/ /* */Abstract/*: Characteristics of the places where we age have profound consequences for our ability to adapt to change and maintain independence. Novel social science theory and data collection can bring insight into the content and structure of older adult lives. The Chicago Health and Activity Space in Real Time (/CHART/) study provides one example of the use of novel technology to address fundamental questions in urban sociology and the life course. CHART employs innovative smartphone-based methods for the identification of older adults’ activity spaces. Analyses from 450 adults from ten Chicago neighborhoods who carried smartphones for GPS tracking and Ecological Momentary Assessments over seven days are used to assess, for instance, how the span, characteristics, and experiences of activity spaces vary across socioeconomic status and racial/ethnic groups. Applicability and extensions of this approach to rural contexts, with pilot data from the Appalachian region of North Carolina, will be described. */Bio/*: Kathleen Cagney was named the Director of the University of Michigan Institute for Social Research effective September 1, 2021. Professor Cagney's work examines social inequality and its relationship to health with a focus on neighborhood, race, and aging, and the life course. She has developed a series of papers on neighborhood social capital and its relationship to outcomes such as self-rated health, asthma prevalence, physical activity, and mortality during the 1995 Chicago heatwave. She also focuses on the validity of such measures and the development of new neighborhood-based metrics that reflect the perceptions and experiences of older residents. She holds research professorships in ISR's Survey Research Center and Population Studies Center. */Link to registration page:/*https://www.eventbrite.co.uk/e/the-promise-of-activity-space-approaches-tickets-641197227167 <https://www.eventbrite.co.uk/e/the-promise-of-activity-space-approaches-tic…> -- Florian Keusch Professor of Social Data Science and Methodology University of Mannheim School of Social Sciences Professorship for Social Data Science and Methodology | A5, 6 | 68131 Mannheim Germany Phone: +49 (0)621 181-3214 E-mail:f.keusch@uni-mannheim.de

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Stelle (75%, TV-L 13) in zwei Forschungsprojekten zu internationalen Hochschulabsolvent*innen, HSZG
by Knut Petzold 26 May '23

26 May '23

Liebe Liste, in zwei Forschungsprojekten zum regionalen Verbleib und Arbeitsmarkterfolg internationaler Hochschulabsolvent*innen ist eine Stelle (75%, TV-L 13, befristet) ausgeschrieben. Es besteht die Möglichkeit zur Promotion. Details sind in der Ausschreibung im Anhang oder unter folgendem Link zu finden: https://www.hszg.de/news/wissenschaftlicher-mitarbeiter-m-w-d-kennwort-regi… Bewerbungsschluss ist der 29.5. Rückfragen können jederzeit an mich gerichtet werden. Mit den besten Grüßen Knut Petzold Prof. Dr. Knut Petzold Methods of Empirical Social Research Faculty of Social Sciences HSZG University of Applied Sciences

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Stellenausschreibung: empirisch-quantitative Alternsforschung / Deutscher Alterssurvey (DEAS) / (m/w/d; 100% TVöD Bund E13, 1 Jahr Elternzeitvertretung)
by Weinhardt, Michael 25 May '23

25 May '23

Liebe Kollegen*innen, das Deutsche Zentrum für Altersfragen (DZA) sucht eine Elternzeitvertretung im Team des Deutschen Alterssurveys (DEAS). Die Stelle ist befristet auf 1 Jahr (m/w/d; 100% TVöD Bund E13, Teilzeitgeeignet). Interessen und Kenntnisse in den Bereichen quantitative Sozialforschung und/oder Alternsforschung; soziale Beziehungen; Survey-Methodologie erwünscht. Wir freuen uns über Bewerbungen von Personen, die Erfahrung in der Erhebung und Auswertung von umfragebasierten Paneldaten haben und gerne in einem interdisziplinären und gleichzeitig politiknahen Umfeld zu arbeiten. Die Ausschreibungsfrist endet bereits am *31.05.2023*. Die Stellenausschreibung finden Sie unter dem folgenden Link: https://www.dza.de/fileadmin/dza/Dokumente/Stellenausschreibungen/Ausschrei… Wir freuen uns über die Weiterleitung der Ausschreibung an interessierte Personen. Mit den besten Grüßen --- Dr. Michael Weinhardt Wissenschaftlicher Mitarbeiter/Senior Researcher Deutscher Alterssurvey (DEAS) Deutsches Zentrum für Altersfragen/German Centre of Gerontology Manfred-von-Richthofen-Str. 2 12101 Berlin, Germany Tel: +4930 260 740-34 E-Mail: michael.weinhardt(a)dza.de<mailto:michael.weinhardt@dza.de> Internet: www.dza.de<http://www.dza.de/> _________________________________________ Jüngste Veröffentlichungen / Recent publications: Weinhardt, M., Lärm, A., Boos, B., Tesch-Römer, C. (2022). Einstellungen zu Menschen mit Demenz in Deutschland [DZA Aktuell 03/2022]. Berlin: Deutsches Zentrum für Altersfragen: https://nbn-resolving.org/urn:nbn:de:0168-ssoar-80950-0

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20th Stata Conference, Berlin June 10th 2023 (Announcement and Program)
by Ulrich Kohler 24 May '23

24 May '23

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ GERMAN STATA CONFERENCE 2023 Announcement and Program ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ June 16, 2023 Overview ════════ Date/Venue/Cost ─────────────── ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Date: Conference: June 16, 2023, 8:45am-6pm Workshop: June 15, 2023, 9am-5pm ───────────────────────────────────────────────────────────────── Venue: Jacob-und-Wilhelm-Grimm-Zentrum Geschwister-Scholl-Straße 1/3 10117 Berlin (S+U Friedrichstraße) ───────────────────────────────────────────────────────────────── Costs: Conference only: 44.99 EUR (students: 35 EUR) Workshop only: 65 EUR (students: 50 EUR) Conference and workshop: 85 EUR (students: 70 EUR ───────────────────────────────────────────────────────────────── Web: <https://www.stata.com/meeting/germany23/> <https://dpc-software.de/2023-german-stata-conference/> ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Deadline for Registration: June 14 2023 Meeting ─────── The 20th German Stata Conference will be held on Friday, 16th June 2023 in Berlin the Humbold University's Jacob-and-Wilhelm-Grimm-Zentrum. We would like to invite everybody from everywhere who is interested in using Stata to attend this meeting. The academic program of the meeting is being organized by Johannes Giesecke (Humboldt University Berlin), and Ulrich Kohler (University of Potsdam). The conference language will be English due to the international nature of the meeting and the participation of non-German guest speakers. The logistics of the conference are being organized by DPC Software GmbH, distributor of Stata in several countries including Germany, The Netherlands, Austria, Czech Republic and Hungary (<http://www.dpc-software.de>). Workshop ──────── On the day before the conference, there will be a one-day workshop on "Stata meets Python". It introduces how to use Python from within Stata, and how to use Stata from within Python. Time table ────────── ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 8:15–8:45 Registration ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 8:45–9:00 Welcome Johannes Giesecke ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 9:00–10:00 Drivers of COVID-19 deaths in the United States: A two-stage modelling approach Christopher F. Baum, Kit Baum, Andrés Garcia-Suaza, Miguel Henry, Jesús Otero 10:00–10:30 Discrete-Time Multistate Regression Models in Stata Daniel C. Schneider 10:30–10:45 mfcurve: Visualizing Results From Multifactorial Designs Daniel Krähmer ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 10:45–11:15 Coffee ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 11:15–11:45 Estimating the Price Elasticity of Gasoline Demand in Correlated Random Coefficient Models with Endogeneity Michael Bates and Seolah Kim 11:45–12:15 Influence Analysis with Panel Data using Stata Annalivia Polselli 12:15–12:45 nopo: An implementation of a matching-based decomposition technique with postestimation commands Maik Hamjediers and Maximilian Sprengholz ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 12:45–13:45 Lunch Break ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 13:45–14:45 Linking frames in Stata Jeff Pitblado, StataCorp 14:45–15:45 Causal inference and treatment-effect decomposition with Stata Joerg Luedicke, StataCorp ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 15:45–16:15 Coffee ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 16:15–16:45 lgrgtest: Lagrange-Multiplier Test after Constrained Maximum-Likelihood Estimation using Stata Harald Tauchmann 16:45–17:00 Assessing the fit of Generalized Structural Equation Models with Stata? Theory and practical application Wolfgang Langer 17:00–17:30 Power boost or source of bias? Monte Carlo evidence on ML covariate adjustment in randomized trials in education Lukas Fervers 17:30–18:00 Open panel discussion with Stata developers ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 18:00 End of meeting ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Conference venue ════════════════ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Jacob-und-Wilhelm-Grimm-Zentrum Geschwister-Scholl-Straße 1/3 10117 Berlin ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ How to get to the venue ─────────────────────── The venue is within walking distance from the station "Friedrichstraße", which is one of the main central train stations in Berlin. You can't miss it. Registration and accommodations ═══════════════════════════════ Participants are asked to travel at their own expense. The conference fee covers costs for coffee, tea, and lunch. There will also be an optional informal meal at additional cost on Friday evening. You can enroll by emailing Natascha Hütter (natascha.huetter(a)dpc-software.de) by writing, phoning, or faxing to ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Natascha Hütter DPC Software GmbH Prinzenstraße 2 42697 Solingen Germany Tel: +49 (0)212 / 22 47 16 -21 www.dpc-software.de ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Workshop ════════ Date and Place ────────────── ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Thursday, June 15, 2023 9am–5pm Jacob-und-Wilhelm-Grimm-Zentrum Geschwister-Scholl-Straße 1/3 10117 Berlin Room: Westpool ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Presenter ───────── ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Nikos Asiktas Institute of Labor Economics (IZA) Bonn ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Nikos Askitas is coordinator of data and technology at the Institute of Labor Economics (IZA) in Bonn. He has a scientific bacground in background in mathematics and economics and publised in journals such as Manuscripta Mathematica, Knot Theory and Its Ramifications, International Journal of Manpower, Journal of Forecasting, PLoS ONE, Journal of Biological Dynamics, and many others. Fees ──── Workshop only: 65 EUR (students 50 EUR) Workshop and Meeting: 85 EUR (students 70 EUR) Registration ──────────── Please register for the conference and/or the workshop by writing an E-mail to Natasch Hütter (natascha.huetter(a)dpc-software.de) Abstracts ═════════ 9:00–10:00 Drivers of COVID-19 deaths in the United States: A two-stage modeling approach ───────────────────────────────────────────────────────────────────────────────────────── Kit Baum (Boston College), Andrés Garcia-Suaza (University del Rosario), Miguel Henry (Greylock McKinnon Associates), Jesús Otero (Universidad del Rosario) /Abstract:/ We offer a two-stage (time-series and cross-section) econometric modeling approach to examine the drivers behind the spread of COVID-19 deaths across counties in the United States. Our empirical strategy exploits the availability of two years (January 2020 through January 2022) of daily data on the number of confirmed deaths and cases of COVID-19 in the 3,000 U.S. counties of the 48 contiguous states and the District of Columbia. In the first stage of the analysis, we use daily time-series data on COVID-19 cases and deaths to fit mixed models of deaths against lagged confirmed cases for each county. Because the resulting coefficients are county specific, they relax the homogeneity assumption that is implicit when the analysis is performed using geographically aggregated cross-section units. In the second stage of the analysis, we assume that these county estimates are a function of economic and sociodemographic factors that are taken as fixed over the course of the pandemic. Here we employ the novel one-covariate-at-atime variable-selection algorithm proposed by Chudik et al. (2018) to guide the choice of regressors. 10:00–10:30 Discrete-Time Multistate Regression Models ────────────────────────────────────────────────────── Daniel C. Schneider (MPI for Demographic Research, Rostock) /Abstract:/ Multistate life tables (MSLTs), or multistate survival models, have become a widely used analytical framework among epidemiologists, social scientists, and demographers. MSLTs can be cast in continuous time or discrete time. While the choice between the two approaches depends on the concrete research question and available data, discrete-time models have a number of appealing features: They are easy to apply; the computational cost is typically low; and today's empirical studies are frequently based on regularly spaced longitudinal data, which naturally suggests modelling in discrete time. Despite these appealing features, Stata add-on packages have so far only been developed for continuous- time models (Crowther and Lambert, 2017; Metzger and Jones, 2018) or for traditional demographic life table calculations that do not allow for covariate adjustment (Muniz 2020). This presentation introduces the recently published Stata package -dtms- which seeks to fill the gap in software availability for discrete-time multistate model estimation. The -dtms- package provides a well- documented and easy-to-apply set of commands that cover a large set of discrete-time MSLT techniques that currently exist in the literature. It also features inference based on newly derived asymptotic covariance matrices as well as inference on group contrasts. Crowther, M.J., Lambert, P.C., 2017. Parametric multistate survival models: Flexible modelling allowing transition-specific distributions with application to estimating clinically useful measures of effect differences. Statistics in Medicine 36, 4719–4742. <https://doi.org/10.1002/sim.7448> Metzger, S.K., Jones, B.T., 2018. mstatecox: A package for simulating transition probabilities from semiparametric multistate survival models. The Stata Journal 18, 533–563. Muniz, J.O., 2020. Multistate Life Tables Using Stata. The Stata Journal 20(3):721–45. doi: 10.1177/1536867X20953577. 10:30–10:45 mfcurve: Visualizing Results From Multifactorial Designs ──────────────────────────────────────────────────────────────────── Daniel Krähmer (Ludwig-Maximilians-University, Munich) /Abstract/ Multifactorial designs are used to study the (joint) impact of two or more factors on an outcome. They typically occur in conjoint, choice, and factorial survey experiments but have recently gained increasing popularity in field experiments, too. Technically, they allow researchers to investigate moderation as an instance of treatment heterogeneity by crossing multiple treatments. Naturally, multifactorial designs quickly spawn a spiraling number of distinct treatment combinations: Even a moderately complex design of two factors with three levels each yields 32 unique combinations. For more elaborate setups, full factorials can easily produce dozens of distinct combinations, rendering the visualization of results difficult. This presentation introduces the new Stata command mfcurve as a potential remedy. Mimicking the ap- pearance of a specification curve, mfcurve produces a two-part chart: The graph’s upper panel displays average effects for all distinct treatment combinations; its lower panel indicates the presence or absence of any level given the respective treatment condition. Unlike existing visualization techniques, this enables researchers to plot and inspect results from multifactorial designs much more comprehensively. Highlighting potential applications, the presentation will demonstrate mfcurve’s most important fea- tures and options, which currently include replacing point estimates by box plots, and testing results for statistical significance. 11:15–11:45 Estimating the Price Elasticity of Gasoline Demand in Correlated Random Coefficient Models with Endogeneity ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── Michael Bates (University of California, Riverside) and Seolah Kim (University of California, Riverside) We propose a per-cluster instrumental variables approach (PCIV) for estimating correlated random coefficient models in the presence of contemporaneous endogeneity and two-way fixed effects. We use variation across clusters to estimate coefficients with homogeneous slopes (such as time effects) and within-cluster variation to estimate the cluster-specific heterogeneity directly. We then aggregate them to population averages. We demonstrate consistency, showing robustness over standard estimators, and provide analytic standard errors for robust inference. Basic implementation is straightforward using standard software such as Stata. In Monte Carlo simulation, PCIV performs relatively well against pooled 2SLS and fixed effects IV (FEIV) with a finite number of clusters or finite observations per-cluster. We apply PCIV in estimating the price elasticity of gasoline demand using state fuel taxes as instrumental variables. PCIV estimation allows for greater transparency of the underlying data. In our setting, we provide evidence of correlation between heterogeneity in the first and second stages, violating a key assumption underpinning consistency of standard estimators. We see significant divergence in the implicit weighting when applying FEIV from the natural weights applied in PCIV. Overlooking effect heterogeneity with standard estimators is consequential. Our estimated distribution of elasticities reveals significant heterogeneity and meaningful differences in estimated averages. 11:45–12:15 Influence Analysis with Panel Data using Stata ────────────────────────────────────────────────────────── Annalivia Polselli (Essex University) /Abstract/ The presence of anomalous cases in a data set (i.e., vertical outliers, good and bad leverage points) can severely affect least squares estimates (coefficients and/or standard errors) that are sensitive to extreme cases, by construction. Cook (1979)’s distance is usually used to detect such anomalies in cross- sectional data. This metrics may fail to flag multiple atypical cases (Atkison, 1985; Chatterjee and Hadi, 1988: Rousseeuw and Van Zomeren, 1990) while a local approach overcomes this limit (Lawrance,1995). I formalise statistical measures to quantify the degree of leverage and outlyingness of units in a panel data framework. I hence develop a unit-wise method to visually detect the type of anomaly, quantify its joint and conditional influence, and the direction of the enhancing and masking effects. I conduct the proposed influence analysis using two user-written commands. First, xtinfluence calculates the joint and conditional influence of unit i on unit j, and the relative enhancing and masking effects. A twoway scatter plot or the ssc heatplot can be used to visualize the influence exerted by each unit in the sample. Second, xtlvr2plot (a panel-data version for lvr2plot) produces unit-wise plots displaying the average individual influence and the average normalised squared residual of unit i. Atkinson, A. C. (1985). Plots, transformations and regression; an introduction to graphical methods of diagnostic regression analysis. Technical report. Chatterjee, S. and Hadi, A. S. (1988). Impact of simultaneous omission of a variable and an observation on a linear regression equation. Computational Statistics & Data Analysis, 6(2):129–144. Cook, R. D. (1979). Influential observations in linear regression. Journal of the American Statistical Association, 74(365):169–174. Lawrance, A. (1995). Deletion influence and masking in regression. Journal of the Royal Statistical Society: Series B (Methodological), 57(1):181–189. Rousseeuw, P. J. and Van Zomeren, B. C. (1990). Unmasking multivariate outliers and leverage points. Journal of the American Statistical association, 85(411):633–639 12:15–12:45 nopo: An implementation of a matching-based decomposition technique with postestimation commands ──────────────────────────────────────────────────────────────────────────────────────────────────────────── Maik Hamjediers (HU-Berlin) and Maximilian Sprengholz (HU-Berlin) /Abstract/ Ñopo (2008) proposed a non-parametric decomosition technique based on matching, which decomposes the observed gap in an outcome between groups into four components. Among the matched sample, the (1) explained component is the part of the gap attributed to compositional differences between groups in predictors of the outcome, and the (2) unexplained component is the part of the gap which would remain if these compositional differences were eliminated. Two additional components capture how unmatched individuals in (3) group A and (4) group B contribute to the gap in the outcome. Ñopo’s technique directly addresses the issue of lacking common support between groups that can bias linear-regression-based decompositions, exhibits a general robustness against functional form mis-specification, and allows to evaluate gaps over the full distribution of the outcome. However, high dimensionality means that there is always a trade-off between the detail of the matching set (to achieve balance between groups) and common support (the share of matches), particularly in small samples. Extending the user written Stata command nopomatch (Atal et al., 2010), our command nopo provides a comprehensive implementation of Ñopo’s matching, including different matching procedures. Postestimation commands allow to investigate the balance after matching, to explore the lack of common support, and to visualize the unexplained component over the outcome distribution. We highlight the merit of this approach and our command by comparing matching to regression-based techniques using a simulation and observational data. Ñopo, H. (2008). Matching as a Tool to Decompose Wage Gaps. The Review of Economics and Statistics, 90, 290–299. Atal, J. P., Hoyos, A., Ñopo, H. (2010). NOPOMATCH: Stata module to implement Nopo's decomposition. Statistical Software Components S457157, Boston College Department of Economics. 13:45–14:45 Linking Frames in Stata ─────────────────────────────────── Jeff Pitblado (Executive Director of Statistical Software at StataCorp) /Abstract/ Forthcoming 14:45–15:45 Causal inference and treatment-effect decomposition with Stata ────────────────────────────────────────────────────────────────────────── Joerg Luedecke, (Senior Social Scientist and Software Developer at StataCorp) /Abstract/ Forthcoming 16:15–16:45 Lagrange-Multiplier Test after Constrained Maximum-Likelihood Estimation using Stata ──────────────────────────────────────────────────────────────────────────────────────────────── Harald Tauchmann (FAU Erlangen-Nürnberg) /Abstract/ Besides the Wald and the likelihood-ratio test, the Lagrange-multiplier test (Rao, 1948; Aitchison and Silvey, 1958; Silvey, 1959)—also known as score test—is the third canonical approach to testing hypotheses after maximum-likelihood estimation. While the Stata commands test and lrtest implement the former two, real Stata does not comprise a general command for imple- menting the latter. This paper introduces the new community-contributed Stata postestimation command lgrgtest that allows for straightforwardly using Lagrange-multiplier test after con- strained maximum-likelihood estimation. lgrgtest is intended to be compatible with all Stata estimation commands that use maximum likelihood and allow for the options constraints(), iterate(), and from(), and obey Stata's standards for the syntax of estimation commands. lgrgtest can also be used after cnsreg. lgrgtest draws on Stata’s constraint command and the accompanying option constraints(), which only allows for imposing linear restrictions on a model. This results in the limitation of lgrgtest being confined to testing linear constraints only. A (partial) replication of Egger et al. (2011) illustrates the use of lgrgtest in applied empirical work. Aitchison, J., and S. D. Silvey (1958): Maximum-Likelihood Estimation of Parameters Subject to Restraints. The Annals of Mathematical Statistics 29(3): 813–828. Egger, P., M. Larch, K. E. Staub, and R. Winkelmann (2011): The Trade Effects of Endogenous Preferential Trade Agreements. American Economic Journal: Economic Policy 3(3): 113–43. Rao, C. R. (1948): Large Sample Tests of Statistical Hypotheses concerning Several Parameters with Applications to Problems of Estimation. Mathematical Proceedings of the Cambridge Philosophical Society 44(1): 50–57. Silvey, S. D. (1959): The Lagrangian Multiplier Test. The Annals of Mathematical Statistics 30(2): 389–407. 16:45–17:00 Assessing the fit of Generalized Structural Equation Models with Stata? Theory and practical application ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────── Wofgang Langner (University of Halle-Wittenberg) /Abstract/ nlike Structural Equation Model implementation Stata does not provide any measure of practical significance for the Generalized Structural Equation Model implementated with the gsem command. I propose a proportional reduction coefficient of determination for each structural equation. Using a single factor comparison model I estimate the variance of the latent factor which correspondes to the error set one. For each dependent factor Stata estimates its residual variance corresponding to the error set two. Using the formula of the proportional reduction of error I calculate the coefficient of determination, the PRE-R2. It has a very clear interpretation: How many percentage of the variance of the dependent factor can be explained by the exogenous variables? I demonstrate this with an empirical example and discuss this application for the Rasch and Partial Credit Models. 17:00–17:30 Power boost or source of bias? Monte Carlo evidence on ML covariate adjustment in randomized trials in education ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── Lukas Fervers (University of Cologne and Leibniz-Centre for Life-Long Learning, Bonn) Statistical theory makes ambiguous predictions about covariate adjustment in randomized trials. While proponents highlight possible efficiency gains, opponents point to possible finite sample bias, a loss of precision in case of many and/or weak covariates as well as the increasing danger of false-positive results due to repeated model specification. This theoretical reasoning suggests that machine-learning (variable selection) methods may be a promising tool to keep the advantages of covariate adjustment, at the same time protecting against its downsides. In this paper, I rely on recent developments of machine learning methods for causal effects and their implementation in STATA to assess the performance of ML methods in randomized trials. I rely on real-world data and simulate treatment effects on a wide range of different data structures, including different outcomes and sample sizes. (Preliminary) results suggests that ML adjusted estimates are unbiased and show considerable efficiency gains compared to unadjusted analysis. The results are fairly similar between different data structures used, as well as robust to the choice of tuning parameters of the ML estimators. These results tend to support the more optimistic view on covariate adjustment and highlight the potential of ML methods in this field. 17:30–18:00 Open panel discussion with Stata developers ─────────────────────────────────────────────────────── /Abstract/ Contribute to the Stata community by sharing your feedback with StataCorp's developers. From feature improvements to bug fixes and new ways to analyze data, we want to hear how Stata can be made better for you. Scientific Organizers ═════════════════════ The academic program of the conference is being organized by Johannes Giesecke (HU Berlin) and Ulrich Kohler (University of Potsdam) Logistics organizers ════════════════════ The logistics are being organized by DPC Software GmbH, the distributor of Stata in several countries including Germany, The Netherlands, Austria, Czech Republic and Hungary (<http://www.dpc.de>). -- Prof. Dr. Ulrich Kohler Universität Potsdam Wirtschafts- und Sozialwissenschaftliche Fakultät Professur für Methoden der empirischen Sozialforschung August-Bebel-Straße 89 14482 Potsdam E-Mail: ukohler(a)uni-potsdam.de Web: www.uni-potsdam.de/soziologie-methoden/index.html

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