- Methoden - mailman.uni-konstanz.de

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

1 0

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

1 1

Möglichkeit zur Datenerhebung in SOEP-IS 2024
by CCornesse＠diw.de 19 May '23

19 May '23

1 0

Ausschreibung JProf "Angewandte Soziologie"
by Henning Best 17 May '23

17 May '23

Liebe Kolleg:innen, an der RPTU Kaiserslautern ist eine W1-Juniorprofessur "Angewandte Soziologie" mit quantitativen, analytisch-empirischem Profil ausgeschrieben. Alle Details im angehängten pdf. Für Fragen stehe ich gerne zur Verfügung. Achtung: Bewerbungsfrist 24.5.! Viele Grüße, Henning Best

1 0

Stellenausschreibung: Empirisch-quantitativer Sozialwissenschaftler / Wirtschaftswissenschaftler für Akzeptanzforschung im Wärme-, Strom- und Mobilitätsbereich
by Shamon, Hawal 17 May '23

17 May '23

Liebe Kolleg:innen, wir vom Institut für Energie- und Klimaforschung Systemforschung und Technologische Entwicklung (IEK-STE) am Forschungszentrum Jülich suchen für die Akzeptanzforschung im Wärme-, Strom- und Mobilitätssektor zeitnah Verstärkung im Bereich der empirisch-quantitativen Sozial- und Wirtschaftsforschung. Empirisch-quantitativer Sozialwissenschaftler / Wirtschaftswissenschaftler (w/m/d) Die Stelle ist im Team Akteure, Akzeptanz und ökonomisches Verhalten verortet und mit der Möglichkeit einer längerfristigen Perspektive zunächst bis zum 31.03.2026 befristet. Sie bietet den Austausch mit Akteuren aus Wissenschaft, Wirtschaft und Zivilgesellschaft. Die Vergütung erfolgt in Anlehnung an TVöD-Bund, je nach Qualifikation in E13 oder E14. Details zur Ausschreibung finden sich im Anhang auf Deutsch und Englisch und unter https://www.fz-juelich.de/de/karriere/stellenangebote/2023-180. Die Ausschreibungsfrist endet bereits am 11.06.2023. Wir freuen uns, wenn Sie diese Stellenausschreibung in Ihren Netzwerken bekanntmachen. Bei Rückfragen stehe ich gerne per E-Mail zur Verfügung. Mit besten Grüßen Hawal Shamon Dr. Hawal Shamon Institut für Energie- und Klimaforschung Systemforschung und Technologische Entwicklung (IEK-STE) Forschungszentrum Jülich GmbH 52425 Jülich Telefon: +49 2461 61-3322 Telefax: +49 2461 61-2540 E-Mail: <mailto:h.shamon@fz-juelich.de> h.shamon(a)fz-juelich.de Internet: https://www.fz-juelich.de/de/iek/iek-ste ResearchGate: <https://www.researchgate.net/profile/Hawal_Shamon> Hawal Shamon on ResearchGate

1 0

CfP - Special Issue: Comparability and Measurement Invariance in Comparative Criminology
by Heinz Leitgöb 15 May '23

15 May '23

Liebe Kolleginnen und Kollegen, ich darf Sie auf den Call for Papers für eine Special Issue im Journal "International Criminology" zum Thema "Comparability and Measurement Invariance in Comparative Criminology", herausgegeben von Daniel Seddig, Dirk Enzmann und mir, hinweisen. Deadline für die Einreichung eines Abstracts für das geplante Full Paper ist der 31.08.2023. Weitere Details können Sie dem Flyer im Anhang entnehmen. Beste Grüße Heinz Leitgöb

1 0

Call for Abstracts - CARME 2023 - Extended Deadline
by Heinz Leitgöb 15 May '23

15 May '23

Liebe Kolleginnen und Kollegen, im Namen von Jörg Blasius darf ich Sie auf die Verlängerung des Call for Abstracts für die CARME 2023 im September in Bonn hinweisen. Details siehe unterhalb bzw. im Anhang. Beste Grüße Heinz Leitgöb ******************************************** CARME in Bonn Correspondence Analysis and Related Methods 26-29 September 2023 Call for papers Topics of the conference include: correspondence analysis in all its variants (simple, multiple, joint, non-symmetrical, taxicab and canonical correspondence analysis) analysis and modelling of categorical data cluster analysis of categorical data geometric data analysis and social spaces compositional data analysis multivariate data visualization history of statistical graphics Applications are welcomed in a wide spectrum of fields: sociology, marketing, psychology, finance, food research, education, linguistics, archaeology, biology, bioinformatics, biochemistry, geochemistry, networks, big data, etc. Registration is open, please submit an abstract (maximum 300 words) by May 15, 2023. Extended deadline: May 30. To submit an abstract via our conference tool at https://www.politik-soziologie.uni-bonn.de/de/carme23, you must create an account and then follow the instructions provided. Please note that it is only possible to submit one abstract per presenting author. In case of any inquiries, please mail to carme23(a)uni-bonn.de Details of previous CARME conferences and workshops can be found at http://www.carme-n.org/ Scientific organizing committee: Jörg Blasius (Bonn), Michael Greenacre (Barcelona), Alfonso Iodice d’Enza (Naples), Angelos Markos (Alexandroupuli) Local organizing committee: Jörg Blasius, Alice Barth, Rebekka Atakan, Manuela Schmidt Outline of conference program: Tuesday 26 September 2023: Conference excursion Wednesday-Friday 27-29 September 2023: Conference Friday evening 29 September 2023: Conference dinner Confirmed keynote speakers: Hervé Abdi (Dallas, USA) Paula Brito (Porto, Portugal) Vartan Choulakian (Moncton, Canada) Peter van der Heijden (Utrecht, The Netherlands) Christian Martin Hennig (Bologna, Italy) Johs. Hjellbrekke (Bergen, Norway) Ron Kenett (Haifa, Israel and Turin, Italy) Rosaria Lombardo (Naples, Italy) Sugnet Lubbe (Stellenbosch, South Africa) Mark de Rooij (Leiden, The Netherlands)

1 0

DEAS-Workshop
by Stuth, Stefan 11 May '23

11 May '23

Liebe Kolleginnen und Kollegen, liebe Datennutzende, liebe Interessierte, wir möchten Sie herzlich zur Nutzer:innenworkshop des Deutschen Alterssurvey (DEAS) einladen. Sie findet am 8. und 9. Juni am Deutschen Zentrum für Altersfragen (DZA) in Berlin statt. Der Deutsche Alterssurvey (DEAS) ist ein umfassendes Forschungsprojekt, das die Lebensbedingungen, Einstellungen und die Gesundheit von Erwachsenen in der zweiten Lebenshälfte in Deutschland untersucht. Auf dem Workshop wird es Forschungsbeiträge auf der Basis dieser Daten zu den Themenbereichen Partnerschaft, Elternschaft und Kinderlosigkeit, Spendenverhalten, Frailty, Morbidität, subjektives Altern, Covid 19 und verschiedene mehr geben. Außerdem wird die Studie "Hohes Alter in Deutschland" (D80+) vorgestellt: der Scientific Use File dieser Studie wird ab dem Sommer 2023 ebenfalls über das FDZ des DZA interessierten Forschenden zur Verfügung gestellt. Wir freuen uns auf einen regen fachlichen Austausch und reichlich Gelegenheit zum Networking und Diskutieren zwischen den Vortragenden, fachlich interessierten Teilnehmer:innen, dem DEAS-Team und dem FDZ-DZA-Team. Die Workshopsprache ist Deutsch. Das vollständige Tagungsprogramm und die Online-Anmeldung zum DEAS-Nutzer:innenworkshop finden Sie hier: https://www.dza.de/forschung/fdz/deutscher-alterssurvey/nutzerinnen-tagung Anmeldeschluss ist der 31. Mai 2023 Beste Grüße vom FDZ-Team des DZA (Anke Erdmann-Linge, Nicole Hameister, Beate Schwichtenberg, Stefan Stuth) ----- Dr. Stefan Stuth Leiter Informationssysteme und Forschungsdatenzentrum / Head of Scientific Information Systems and Research Data Centre Deutsches Zentrum für Altersfragen / German Centre of Gerontology Manfred-von-Richthofen-Str. 2 12101 Berlin, Germany Tel: +4930 260 740-75 E-Mail: stefan.stuth(a)dza.de<mailto:stefan.stuth@dza.de> Internet: www.dza.de <http://www.dza.de/> Neu erschienen: Wünsche, J., Spuling, S. M., Nowossadeck, S. & Stuth, S. (2023, online first). "Ältere Menschen im ersten Jahr der COVID-19-Pandemie: Gesundheitsrelevante Befunde aus dem Deutschen Alterssurvey (DEAS)." Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz: 1-9. https://doi.org/10.1007/s00103-023-03656-w Stefan Stuth (2021) Prekäre Beschäftigung und mögliche Ursachen. In: Statistisches Bundesamt & Wissenschaftszentrum für Sozialforschung (Hrsg.): Datenreport 2021: Ein Sozialbericht für die Bundesrepublik Deutschland. Bonn: Bundeszentrale für politische Bildung, 184-188, https://www.wzb.eu/de/publikationen/datenreport/datenreport-2021-ein-sozial…

1 0

Registration open: How to Use Smartphone Sensor Data for Social Research
by berd-academy＠stat.uni-muenchen.de 10 May '23

10 May '23

Dear social researchers, The IAB-SMART project has gathered valuable insights by combining in-app survey data with 1.3 million location observations derived from GPS and mobile network data. These observations were obtained from participants who were recruited among Android users in the Panel Study Labor Market and Social Security (PASS). To facilitate research, the collected raw geolocations have been aggregated and made available as "IAB-SMART-Activity" at the Research Data Center of the Institute for Employment Research (IAB). This dataset contains geolocation indicators that can be linked to PASS survey data and PASS-ADIAB administrative employment data, enabling researchers to gain comprehensive insights. We invite you to register for the three IAB-SMART webinars, where experts will guide you through the process of collecting, processing, and utilizing smartphone sensor data for social research. * IAB-SMART 1: June 28, 2023, 12pm noon - 1:30pm CEST The IAB-SMART Study: Collecting Behavioral Smartphone Sensor Data for Social Research https://www.berd-nfdi.de/berd-academy/iabsmart1/ * IAB-SMART 2: July 05, 2023, 1pm CEST - 2pm CEST What do geolocation smartphone data add to a survey panel? - Available indicators from the IAB-SMART Project https://www.berd-nfdi.de/berd-academy/iabsmart2/ * IAB-SMART 3: July 12, 2023, 1pm CEST - 2pm CEST How to tidy and anonymize raw smartphone geolocation data: Code and Practitioner's Examples from the IAB-SMART Project https://www.berd-nfdi.de/berd-academy/iabsmart3/ Best regards, Markus Herklotz for BERD Academy

1 0

FReDA Autumn School 18.-20.9.2023 at JGU Mainz - Registration is open!
by Gunnar Otte 09 May '23

09 May '23

Liebe Kolleg*innen, in einer Kooperation zwischen dem Bundesinstitut für Bevölkerungsforschung (BiB) und dem Forschungsschwerpunkt "Interdisciplinary Public Policy" (IPP) der JGU Mainz findet *vom 18. bis 20. September 2023 die erste FReDA Autumn School* an der Johannes Gutenberg-Universität Mainz statt. Das vorläufige Programm hängt an, den Link für die Registrierung finden Sie unten. Die Teilnahme ist kostenlos. *Eine Anmeldung ist bis 30.6.2023 möglich. * Bitte machen Sie Interessierte auf dieses Angebot aufmerksam! Beste Grüße Gunnar Otte, Natascha Nisic, C. Katharina Spieß, Martin Bujard, Reyn van Ewijk *** FReDA – The German Family Demography Panel Study in cooperation with Interdisciplinary Public Policy (IPP) Mainz will host a 3-day workshop for interested users of the FReDA data at Johannes Gutenberg University Mainz. This study collects data about the living situations of partnerships and families in Germany over a prolonged and continuous period of time. At this workshop, learn more about FReDA from members of the project team and take advantage of the chance to work directly with the data! Advanced bachelor’s and master’s students, doctoral students, and postdocs with a knowledge of Stata are welcome to participate. Participants will have to cover the cost of lunch and dinner. Register to attend via sozialstruktur.soziologie.uni-mainz.de/freda <https://sozialstruktur.soziologie.uni-mainz.de/freda/>. The deadline to apply is 30 June 2023 and places are limited. This workshop is supported by the Federal Institute for Population Research (BiB) and JGU Mainz. If you have any questions, please contact freda(a)bib.bund.de. What is FReDA? FReDA – The German Family Demography Panel Study is an interdisciplinary, long-term study about families and partnerships in Germany. Participants are between the ages of 18 and 49. Twice a year, FReDA surveys its participants and their partners. Data from the recruitment wave are now available for free for scientific and non-commercial purposes. This includes completed surveys from over 37,000 participants. Beginning in this summer, data from the complete first wave and the partner survey will also be available. More information about the study is available on the FReDA website <https://www.freda-panel.de/FReDA/DE/Startseite.html>.

1 0