October 2018 - Methoden - mailman.uni-konstanz.de

CfA ESRA 2019: Fieldwork monitoring tools for large-scale surveys: Lessons from the field
by Michael Bergmann 10 Oct '18

10 Oct '18

***Apologies for cross-posting*** Dear colleagues, We would like to invite you to submit an abstract to our session on Fieldwork monitoring tools for large-scale surveys: Lessons from the field at the ESRA Conference (July 15-19, 2019 in Zagreb). You can submit your abstract (max. 300 words) using the conference website: https://www.europeansurveyresearch.org/conferences/register The closing date for the submission of abstracts is November 18, 2018. Here is the abstract for our session: Fieldwork monitoring tools for large-scale surveys: Lessons from the field A key challenge in survey research is how to manage fieldwork to maximise sample sizes and balance response rates whilst ensuring that fieldwork is completed on time, within budget and to the highest possible standards. This is particularly demanding when monitoring fieldwork across multiple surveys simultaneously, across time, or across different countries. Effective monitoring requires access to real-time information which can be collected by and shared with multiple stakeholders in a standardised way and responded to in a timely manner. This process often involves researchers who may be several steps removed from the fieldwork, located in a different organisation and even a different country. Increasingly, fieldwork agencies and survey infrastructures have access to detailed progress indicators collected electronically in the field. Making use of this information requires streamlining the information received by means of fieldwork monitoring systems or dashboards. Developing an effective fieldwork monitoring system is not as straightforward as it may at first appear and raises both methodological and operational questions. Methodological questions include which indicators to use for monitoring, how to combine paradata from multiple sources to generate indicators, and how to use the indicators during fieldwork. Operational questions include how to most effectively present the agreed indicators, which stakeholders the monitoring system should cater for, how often updates should be received, how to implement y interventions, and how the feedback loop between the monitoring team and interviewers in the field should be managed. Thinking about these questions is crucial in ensuring that increased access to real-time data leads to improvements in monitoring and responding to issues in the field. The data should facilitate informed decision-making about how best to realise often competing goals of fieldwork delivery, quality and costs. This session aims to bring together those working with fieldwork monitoring dashboards or other tools to share their experiences and learning. We welcome presentations from different stakeholders (fieldwork agencies, research infrastructures, academic institutes, etc.) and survey types (national and cross-national, cross-sectional and longitudinal, conducted in any mode). Presentations could outline how the types of methodological and practical issues raised above have been addressed and demonstrate how the choices made have had an impact, good or bad, on capacity to monitor fieldwork. Presentations may also provide systematic reviews of one or more performance indicators results of implementing adaptive and responsive designs informed by monitoring dashboards, and experimental and simulation studies on fieldwork monitoring. Looking forward to seeing you at the ESRA conference, Michael Bergmann Sarah Butt Salima Douhou Brad Edwards Patrick Schmich Henning Silber -- Dr. Michael Bergmann Technical University of Munich (Chair for the Economics of Aging), Munich Center for the Economics of Aging (MEA) Max Planck Institute for Social Law and Social Policy Amalienstr. 33 80799 Munich, Germany Phone: +49-89-38602-322 Email: bergmann[at]mea.mpisoc.mpg.de Homepage: www.mea.mpisoc.mpg.de<http://www.mea.mpisoc.mpg.de/>

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CfA ESRA 2019: Quantitative Spatial Analysis of Micro and Macro Data
by Henning Best 10 Oct '18

10 Oct '18

Dear colleagues, Please consider submitting an abstract (300 words or fewer) to our ESRA 2019 panel, "Quantitative Spatial Analysis of Micro and Macro Data: Methodological Challenges and Solutions” (see below for details). The submission deadline is November 18. Please use the ESRA conference management system for submission: https://www.europeansurveyresearch.org/conferences/register Tobias Rüttenauer & Henning Best Quantitative Spatial Analysis of Micro and Macro Data: Methodological Challenges and Solutions The session intends to bring together methodological experiences made when working with spatial data in quantitative empirical social research. On the one hand, spatial data offer the opportunity to investigate the relationship between regional characteristics on the macro level. On the other hand, spatial data can be used to enrich survey data with structural information on a certain regional level, either to control for context effects or to explicitly analyse these effects and their interplay with mechanisms on the individual level. By using GIS, addresses of survey participants can be linked with objective measures of their neighbourhood (e.g. pollution data) or proximity to institutions (e.g. of educational institutions or workplaces). Thus, these data allow investigating the relevance of infrastructure distances for social action as well as processes of spatial spillovers and diffusion. In doing so, several methodological questions arise: What kind of regional level is adequate to what kind of question and how does the choice of administrative borders influence the derived conclusions (“MAUP”)? Can we enrich survey data by information on actual travelling times and means of transportation to account for the moving or action space of participants? What are the challenges and limitations of these approaches and how can it be done reliably? Furthermore, innovative statistical methods are necessary to adequately analyse spatial data. Various regression models (e.g. SAR, SARAR, SLX, Durbin and others) address the spatial dependence in different ways and offer alternative approaches to identify different types of spatial spillovers or spatial interdependences, in cross-sectional and longitudinal data. Which types of models are adequate for which type of questions? Which models can be used to simultaneously analyse individual and aggregate data? In sum, in this session we are especially interested in methodological and applied studies dealing with topics of: 1. Choice of adequate regional level and handling of borders when using administrative data 2. Connection of individual data and spatially aggregate as well as infrastructural data 3. Spatial analysis of time-series and cross-sectional data 4. Modelling spatial relationships (e.g. commuting flows, distances, traveling times, social interactions) 5. Modelling spatial interaction, spillover or diffusion processes 6. Further challenges and solutions when using georeferenced data -- Prof. Dr. Henning Best Fachbereich Sozialwissenschaften TU Kaiserslautern Erwin-Schrödinger-Straße 57 67663 Kaiserslautern E-Mail: best(a)sowi.uni-kl.de

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Stellenausschreibungen IBZ Uni Bern: Wissenschaftliche Leitung und Wissenschaftliche/r Mitarbeiter/in
by ben.jann＠soz.unibe.ch 08 Oct '18

08 Oct '18

Am neu gegründeten Interfakultären Zentrum für Bildungsforschung (IZB) der Philosophisch-humanwissenschaftlichen Fakultät und Wirtschafts- und Sozialwissenschaftlichen Fakultät an der Universität Bern sind per 01.01.2019 oder nach Vereinbarung folgende Stellen zu besetzen: *** Wissenschaftliche Leitung *** Diese Leitungsstelle mit einem Beschäftigungsgrad von 80% ist zunächst auf zweimal 3 Jahre befristet. Eine Verlängerung der Anstellung wird angestrebt. Die Aufgaben der Wissenschaftlichen Leitung liegen primär im Bereich der Forschung und akademischen Lehre. Dazu gehören die Leitung der Projekte in Zusammenhang mit der Überprüfung der Grundkompetenzen in der Schweiz (ÜGK), die Koordination der Aufgaben der in ÜGK involvierten regionalen Testzentren und sonstigen Akteure, Akquisition von Drittmitteln für Begleitprojekte, die wissenschaftliche Einbettung der Projekte (inklusive Publikationen), Personalführung, die regelmässige Durchführung von Lehrveranstaltungen zum Themenbereich sowie die Kommunikation mit den Fakultäten, der Universitätsleitung, der EDK, den Stakeholdern sowie den Mitgliedern des Kuratoriums und des Beirats. Voraussetzung für die Bewerbung ist eine Promotion in Erziehungswissenschaft, Psychologie, Soziologie oder Bildungsökonomie. Gefordert sind hervorragende bisherige Forschungs- und Publikationsleistungen, einschlägige Erfahrungen mit Large Scale Assessments und Survey Methodologie, gute Kenntnisse des Schweizer Bildungssystems sowie Fähigkeiten in der Führung von Mitarbeitenden. Erwünscht sind Deutsch oder Französisch als Muttersprache mit guten Kenntnissen der anderen Sprache sowie gute Englischkenntnisse. Kenntnisse der italienischen Sprache sind von Vorteil. Erfahrungen in der erfolgreichen Einwerbung von Drittmittel sind ebenfalls erwünscht. *** Wissenschaftliche/r Mitarbeiter/in (Geschäftsstelle) *** Diese Position ist mit einem Beschäftigungsgrad von 80% zunächst auf zweimal 3 Jahre befristet. Eine Verlängerung der Anstellung wird angestrebt. Aufgabe der Wissenschaftlichen Mitarbeiterin bzw. des Wissenschaftlichen Mitarbeiters ist die Führung der Geschäftsstelle des IZB. Dazu gehören die Unterstützung der Wissenschaftlichen Leitung des IZB in allen wissenschaftlichen Belangen, die Koordination der Projekte sowie die wissenschaftliche Einbettung der Projekte (inkl. Publikationen). Voraussetzung für die Bewerbung ist ein Masterabschluss oder eine abgeschlossene Promotion in Erziehungswissenschaft, Psychologie, Soziologie oder Bildungsökonomie. Erwünscht sind hervorragende bisherige Forschungs- und Publikationsleistungen, einschlägige Erfahrungen mit Large Scale Assessments und Survey Methodologie sowie gute Kenntnisse des Schweizer Bildungssystems. Ebenso erwünscht sind Deutsch oder Französisch als Muttersprache mit guten Kenntnissen der anderen Sprache sowie gute Englischkenntnisse. Kenntnisse der italienischen Sprache sind von Vorteil. Wir bieten die Einbindung in ein kompetentes, engagiertes und kooperatives Lehr- und Forschungsteam sowie gute Anstellungsbedingungen gemäss dem Personalreglement des Kantons Bern. Die Universität Bern strebt eine Erhöhung des Anteils von Frauen in leitenden akademischen Positionen an und fordert deshalb qualifizierte Wissenschaftlerinnen nachdrücklich zur Bewerbung auf. Bewerbungen (mit Curriculum Vitae und Schriftenverzeichnis) sind schriftlich in elektronischer Form (pdf) bis zum 5. November 2018 beim Dekanat der Philosophisch-humanwissenschaftlichen Fakultät der Universität Bern, Fabrikstrasse 8, CH-3012 Bern, barbara.lewis(a)humdek.unibe.ch, einzureichen.

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Training School “Evaluation procedures and their impact on SSH"
by Tobias Wolbring 06 Oct '18

06 Oct '18

Liebe Kolleginnen und Kollegen, im Anhang ein CfA für die im Betreff genannte Training School in Vilnius. Beste Grüße Tobias Wolbring --- Prof. Dr. Tobias Wolbring Chair of Empirical Economic Sociology School of Business and Economics Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Findelgasse 7/9 D-90402 Nürnberg Office: +49 911 5302 950 tobias.wolbring(a)fau.de http://www.wirtschaftssoziologie.wiso.uni-erlangen.de/ https://sites.google.com/site/tobiaswolbring/

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Second Call for Papers of the 5th Conference of the Deutsche Arbeitsgemeinschaft Statistik, (DAGStat 2019)
by Tobias Wolbring 05 Oct '18

05 Oct '18

Second Call for Papers of the 5th Conference of the Deutsche Arbeitsgemeinschaft Statistik, (DAGStat 2019) Registration is now open and we cordially invite you to register for the 5th Conference of the Deutsche Arbeitsgemeinschaft Statistik (DAGStat 2019) from March 18 - 22, 2019, at LMU Munich, under https://www.dagstat2019.statistik.uni-muenchen.de/index.html Under the motto "Statistics under one Umbrella" the planned conference program contains contributions of all areas of statistics and many applications of statistical methods. Scientific exchange between the participating societies is one main aims of the conference. This is reflected by embedding two important annual meetings, the "65. Biometrisches Kolloquium" of the German Region of the International Biometric Society and the "Spring Meeting" of the Deutsche Statistische Gesellschaft. For the conference, Sara van de Geer, Andrew Gelman, Donald B. Rubin and Michael Jordan have agreed to be plenary speakers. For special topics and invited speakers see https://www.dagstat2019.statistik.uni-muenchen.de/scientific-programs/index… Tutorials will be offered about the topics "Adaptive designs for clinical trials" (Frank Bretz, Tim Friede), "Bayesian data analysis using Stan" (Andrew Gelman), and " Spatio-temporal modeling" (Thomas Kneib), see https://www.dagstat2019.statistik.uni-muenchen.de/scientific-programs/tutor… Online registration and submission of abstracts is possible via https://www.dagstat2019.statistik.uni-muenchen.de/conference-details/news/i… Please note the following deadlines: November 4th 2018: Deadline for registration for the special session "Young Statisticians" (https://www.dagstat2019.statistik.uni-muenchen.de/scientific-programs/speci…) November 18th 2018: Abstract submission deadline January 27th 2019: Early-Bird registration deadline We are looking forward to welcoming you in Munich next year, Göran Kauermann and Helmut Küchenhoff, on behalf of the local organizing committee.

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ESRA 2019: Sampling Hard-to-Reach Populations (CfA)
by Ulrich Kohler 04 Oct '18

04 Oct '18

Dear Colleagues, on behalf of Lena Hipp (WZB), Dimitri Prandner (JK University of Linz)and Martin Weichbold (University of Salzburg) I wish to invite you to submit an abstract for our session "Sampling Hard-to-Reach Populations" at the ESRA Conference on July 15-19, 2019 in Zagreb. Session Details URL: https://www.europeansurveyresearch.org/conferences/sessionCategories?sess=27 Deadline: November 18th 2019 Submission URL: https://www.europeansurveyresearch.org/conferences/register (Registration required) For your convenience, I also copied the session details below my signature into the body of this e-mail. Many regards Ulrich Kohler -- Prof. Dr. Ulrich Kohler University of Potsdam Public interest in learning more about demographic groups that are either small, hidden, mobile, or engaged in illicit behaviors has grown in recent years. Prominent examples of these “hard-to-reach” populations are drug addicts, homeless people, or prostitutes as well as very rich people and migrants, particularly those who are not documented or who travel a lot. None of these demographic groups can be adequately sampled with probability surveys, either because of the absence of sampling frames, the small size of these groups compared to the total population, their unstable residency, or their reluctance to participate. In order to learn more about these hard-to-reach populations, researchers have employed a few different sampling methods, including network sampling, link-tracing designs (aka snowball sampling), and respondent-driven sampling. Although the use of such nonprobability sampling methods to survey hard-to-reach groups has rapidly expanded and has been employed in many different contexts (though particularly in developing countries), numerous questions regarding both the implementation of these surveys and the analyses of the collected data have not yet been (fully) resolved. o How can the target population be adequately delineated and identified in the sampling process? o How should researchers choose incentives and interview locations when surveying hard-to-reach populations? What are best practices in seed selection? o What do we know about mode differences when surveying hard-to-reach populations and asking individuals about illicit behaviors? o What challenges occur when employing nonprobability sampling in comparative studies, for example with regard to the number of initial seeds and assumptions regarding the referral o What are the best estimators when analyzing data collected from non-probability samples? o How can we best calculate the variability of the estimates from non-probability samples? o What are the ethical issues when surveying hard-to-reach populations and how can they be resolved in an acceptable way for researchers, respondents, and funding agencies? o How can nonprobability surveys be combined with other methodological approaches to assess the accuracy of their findings?

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ESRA 2019: CfA "Causality in Health Inequalities Research: New Solutions to Old Challenges?"
by Katharina Loter 01 Oct '18

01 Oct '18

Dear Colleagues, we would like to draw your attention to the call for abstracts for our session: Causality in Health Inequalities Research: New Solutions to Old Challenges? at the ESRA Conference 2019 in Zagreb, Croatia. 8th Conference of the European Survey Research Association: 15th-19th July 2019 in Zagreb, Croatia https://www.europeansurveyresearch.org/conferences/call_for_abstracts Session: Causality in Health Inequalities Research: New Solutions to Old Challenges? https://www.europeansurveyresearch.org/conferences/sessionCategories?sess=1… Deadline for Abstracts: 18th November 2018, abstract (max. 300 words) Session details: Questions that motivate most studies on the emergence and reproduction of health inequalities over the life course are longitudinal and causal in nature. Fortunately, after several decades of socio-epidemiological research that produced numerous cross-sectional (and thus associational) findings, the need for studies aiming at disentangling causal effects and mechanisms from non-causal associations is becoming increasingly recognized. Especially, attempts to estimate causal effects of social background variables (e.g., marital and family status or occupational status) and/ or life changing events (e.g., pregnancy, separation and divorce, death of a family member, retirement) on health over time – including selection and reverse causality – have to be undertaken using advanced data structures and analytical techniques suited for causal inference. Following recent developments in causal theory including directed acyclic graphs (DAG), new opportunities for innovative health research are being generated by the increasing availability of rich panel data with multiple times of observation and different types of health measures (biomarkers, fitness tracker data, results of objective medical tests, and a variety of subjective indicators). Nonetheless, causal analyses on health inequalities over time remain challenging for many reasons, and one of them is health being a strong predictor of attrition in panel studies (due to nonresponse or death). Several analytical approaches have been proposed as a remedy for the manifold challenges regarding causal inference in health research. The first one deals with health-related self-selection as a missing data problem and uses techniques for correcting causal effect estimates such as weighting or dropout models for nonignorable nonresponse (e.g., Heckman selection models). The second one focuses on reducing unobserved heterogeneity (e.g., via instrumental variables, propensity score matching, fixed-effects regression). The third one places emphasis on testing potential intervening mechanisms (mediator and suppressor effects). The fourth one corresponds with the use of agent-based modeling (e.g., with SIENA) enabling simultaneous estimation of selection and causation effects in a social network framework. Finally, the consideration of measurement error while dealing with latent variables may serve to adjust causal effect estimates for (lack of) reliability. In the light of the above challenges, we invite submissions applying any of the mentioned approaches (or other innovative analysis techniques) with the goal to improve understanding of causality and selection in (preferably longitudinal) research on health inequalities. Welcome are contributions that aim at unraveling the causal complexity between social background variables and/ or life course transitions and events, and any kind of objective or subjective health outcome. Best wishes, Katharina Loter & Oliver Arránz Becker Martin-Luther-University Halle-Wittenberg

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