@incollection{Munzert, author = {Munzert, Simon}, title = {Auf dem Weg zu einer fundierten Softwareausbildung in der Politikwissenschaft}, series = {Computational Social Science. Die Analyse von Big Data}, booktitle = {Computational Social Science. Die Analyse von Big Data}, editor = {Bl{\"a}tte (et al.), Adreas}, publisher = {Nomos}, address = {Baden-Baden}, isbn = {ISBN print: 978-3-8487-4393-3, ISBN online: 978-3-8452-8655-6}, doi = {10.5771/9783845286556-379}, publisher = {Hertie School}, pages = {379 -- 402}, language = {de} } @incollection{ShikanoMunzertSchuebeletal., author = {Shikano, Susumu and Munzert, Simon and Sch{\"u}bel, Thomas and Herrmann, Michael and Selb, Peter}, title = {Eine empirische Sch{\"a}tzmethode f{\"u}r Valenz-Issues auf der Basis der Kandidatenbeurteilung am Beispiel der Konstanzer Oberb{\"u}rgermeisterwahl 2012}, series = {Jahrbuch f{\"u}r Handlungs- und Entscheidungstheorie}, booktitle = {Jahrbuch f{\"u}r Handlungs- und Entscheidungstheorie}, editor = {Linhart, Eric and Kittel, Bernhard and B{\"a}chtiger, Andr{\´e}}, publisher = {Springer}, address = {Wiesbaden}, isbn = {978-3-658-05007-8}, doi = {10.1007/978-3-658-05008-5_4}, publisher = {Hertie School}, pages = {113 -- 131}, abstract = {Bei der Entwicklung der r{\"a}umlichen Modelle des Parteienwettbewerbs spielt die Valenz eine wichtige Rolle. Trotz der theoretischen Relevanz bleibt die Mess- und Sch{\"a}tzmethode der Valenz unterentwickelt. Angesichts dieser Forschungsl{\"u}cke schl{\"a}gt dieser Beitrag ein statistisches Modell vor, das die gleichzeitige Sch{\"a}tzung der Kandidatenpositionen und der Valenz erm{\"o}glicht. Ein wichtiger Vorzug dieses Modells liegt darin, dass man nur die Kandidatenbeurteilungen per Skalometer ben{\"o}tigt, der in den meisten Umfragedaten verf{\"u}gbar ist. Dieses Modell wird auf Daten angewendet, die in Rahmen der Konstanzer Oberb{\"u}rgermeisterwahl 2012 erhoben wurden.}, language = {de} } @misc{Munzert, author = {Munzert, Simon}, title = {XML and Web Technologies for Data Sciences with R}, series = {Journal of Statistical Software}, volume = {81}, journal = {Journal of Statistical Software}, doi = {10.18637/jss.v061.b01}, language = {de} } @techreport{MunzertPapoutsiNowak, type = {Working Paper}, author = {Munzert, Simon and Papoutsi, Myrto and Nowak, Holger}, title = {Ein Jahr digitale Kontaktpersonennachverfolgung mit der Corona-Warn-App}, doi = {10.48462/opus4-3999}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:b1570-opus4-39994}, pages = {16}, abstract = {Nachdem es zwischenzeitlich ruhig um die Corona-Warn-App geworden war, nahm die Diskussion um digitale Kontaktpersonennachverfolgung im Fr{\"u}hjahr 2021 wieder Fahrt auf. Die Corona-Warn-App wurde mit lange erwarteten Zusatzfeatures zur Eventregistrierung und Erfassung von Schnelltestergebnissen ausgestattet, w{\"a}hrend die Luca-App aufgrund von Sicherheitsl{\"u}cken und Datenschutzm{\"a}ngeln zunehmend in die Kritik geriet. In unserem Bericht dokumentieren wir, wie popul{\"a}r diese Apps mittlerweile in der deutschen Bev{\"o}lkerung sind. Nach wie vor gilt: F{\"u}r die Wirksamkeit dieser Technologien ist die breitfl{\"a}chige Nutzung in der Bev{\"o}lkerung entscheidend. In unserer Erhebung, einem Smartphone-basierten Nutzertracking, das zwischen dem 19. Mai und 01. Juni 2021 durchgef{\"u}hrt wurde, zeigt sich nach wie vor deutliche Unterschiede in der Nutzung der beiden bekanntesten Apps - Corona-Warn-App und Luca-App. Im Vergleich zur vorherigen Befragung (M{\"a}rz/April 2021) sind jedoch einige Verschiebungen beobachtbar: Die Luca-App ist bekannter geworden, gleichzeitig steigt die Sorge um den Datenschutz der App allenfalls leicht an. Bei vielen Befragten besteht nach wie vor große Unklarheit {\"u}ber die Unterschiede in Funktionsweise und Zweck dieser Apps.}, language = {de} } @article{MunzertBarberAGuessetal., author = {Munzert, Simon and Barber{\´A}, Pablo and Guess, Andrew M. and Yang, JungHwan}, title = {Do Online Voter Guides Empower Citizens? Evidence from a Field Experiment with Digital Trace Data}, series = {Public Opinion Quarterly}, volume = {84}, journal = {Public Opinion Quarterly}, number = {3}, doi = {10.1093/poq/nfaa037}, pages = {675 -- 698}, abstract = {Voting Advice Applications (VAAs), which provide citizens with information on the party that best represents their political preferences, are often cited as evidence of the empowering capabilities of digital tools. Aside from the informational benefits of these voter guides, observational studies have suggested a strong effect on political participation and vote choice. However, existing impact evaluations have been limited by a reliance on convenience samples, lack of random assignment, or both. This raises questions about self-selection and the precise mechanisms underlying how voters learn about politics. Here, we provide evidence from a field experiment with survey outcomes conducted with a sample of over 1,000 German citizens in the 2017 federal election campaign. Using linked panel survey and digital trace data combined with a randomized encouragement to complete a VAA, we assess respondents' compliance with treatment and observe how the use of this tool affects political behavior, attitudes, media consumption, political knowledge, and even social media activity. Our findings reveal that the overwhelming consensus in favor of positive effects on turnout and vote choice should be treated with caution, as we find no such effects. Rather, the actual virtue of VAAs in a complex online information environment lies in increasing knowledge about parties' positions on issues—exactly the kind of information these tools were designed to provide.}, language = {en} } @article{MunzertWattsAmann, author = {Munzert, Simon and Watts, Nick and Amann, Markus}, title = {The 2020 report of The Lancet Countdown on health and climate change: responding to converging crises}, series = {The Lancet}, volume = {397}, journal = {The Lancet}, number = {10269}, pages = {129 -- 170}, language = {en} } @article{GuessBarberaMunzertetal., author = {Guess, Andrew M. and Barber{\´a}, Pablo and Munzert, Simon and Yang, JungHwan}, title = {The consequences of online partisan media}, series = {Proceedings of the National Academy of Sciences}, volume = {118}, journal = {Proceedings of the National Academy of Sciences}, number = {14}, editor = {Bail, Christopher Andrew}, doi = {10.1073/pnas.2013464118}, abstract = {What role do ideologically extreme media play in the polarization of society? Here we report results from a randomized longitudinal field experiment embedded in a nationally representative online panel survey (N = 1,037) in which participants were incentivized to change their browser default settings and social media following patterns, boosting the likelihood of encountering news with either a left-leaning (HuffPost) or right-leaning (Fox News) slant during the 2018 US midterm election campaign. Data on ≈ 19 million web visits by respondents indicate that resulting changes in news consumption persisted for at least 8 wk. Greater exposure to partisan news can cause immediate but short-lived increases in website visits and knowledge of recent events. After adjusting for multiple comparisons, however, we find little evidence of a direct impact on opinions or affect. Still, results from later survey waves suggest that both treatments produce a lasting and meaningful decrease in trust in the mainstream media up to 1 y later. Consistent with the minimal-effects tradition, direct consequences of online partisan media are limited, although our findings raise questions about the possibility of subtle, cumulative dynamics. The combination of experimentation and computational social science techniques illustrates a powerful approach for studying the long-term consequences of exposure to partisan news.}, language = {en} } @techreport{GoebelMunzert, type = {Working Paper}, author = {G{\"o}bel, Sascha and Munzert, Simon}, title = {The Comparative Legislators Database}, series = {SocArXiv Papers}, journal = {SocArXiv Papers}, doi = {10.31235/osf.io/kapfq}, abstract = {Knowledge about political representatives' behavior is crucial for a deeper understanding of politics and policy-making processes. Yet resources on legislative elites are scattered, often specialized, limited in scope or not always accessible. This article introduces the Comparative Legislators Database (CLD), which joins micro-data collection efforts on open-collaboration platforms and other sources, and integrates with renowned political science datasets. The CLD includes political, sociodemographic, career, online presence, public attention, and visual information for over 45,000 contemporary and historical politicians from ten countries. The authors provide a straightforward and open-source interface to the database through an R package, offering targeted, fast and analysis-ready access in formats familiar to social scientists and standardized across time and space. The data is verified against human-coded datasets, and its use for investigating legislator prominence and turnover is illustrated. The CLD contributes to a central hub for versatile information about legislators and their behavior, supporting individual-level comparative research over long periods.}, language = {en} } @misc{MunzertJankinetal, author = {Munzert, Simon and Jankin, Slava and et al.,}, title = {The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future}, series = {The Lancet}, journal = {The Lancet}, edition = {10311}, doi = {10.1016/S0140-6736(21)01787-6}, pages = {1619 -- 1662}, abstract = {The Lancet Countdown is an international collaboration that independently monitors the health consequences of a changing climate. Publishing updated, new, and improved indicators each year, the Lancet Countdown represents the consensus of leading researchers from 43 academic institutions and UN agencies. The 44 indicators of this report expose an unabated rise in the health impacts of climate change and the current health consequences of the delayed and inconsistent response of countries around the globe—providing a clear imperative for accelerated action that puts the health of people and planet above all else. The 2021 report coincides with the UN Framework Convention on Climate Change 26th Conference of the Parties (COP26), at which countries are facing pressure to realise the ambition of the Paris Agreement to keep the global average temperature rise to 1·5°C and to mobilise the financial resources required for all countries to have an effective climate response. These negotiations unfold in the context of the COVID-19 pandemic—a global health crisis that has claimed millions of lives, affected livelihoods and communities around the globe, and exposed deep fissures and inequities in the world's capacity to cope with, and respond to, health emergencies. Yet, in its response to both crises, the world is faced with an unprecedented opportunity to ensure a healthy future for all.}, language = {en} } @article{GschwendMuellerMunzertetal., author = {Gschwend, Thomas and M{\"u}ller, Klara and Munzert, Simon and Neunhoeffer, Marcel and Stoetzer, Lukas}, title = {The Zweitstimme Model: A Dynamic Forecast of the 2021 German Federal Election}, series = {PS: Political Science \& Politics}, volume = {55}, journal = {PS: Political Science \& Politics}, number = {1}, doi = {10.1017/S1049096521000913}, pages = {85 -- 90}, language = {en} } @article{MunzertSelb, author = {Munzert, Simon and Selb, Peter}, title = {Can we directly survey adherence to non-pharmaceutical interventions? Evidence from a list experiment conducted in Germany during the early Corona pandemic.}, series = {Survey Research Methods}, volume = {14}, journal = {Survey Research Methods}, number = {2}, doi = {10.18148/srm/2020.v14i2.7759}, pages = {205 -- 209}, abstract = {Self-reports of adherence to non-pharmaceutical interventions in surveys may be subject to social desirability bias. Existing questioning techniques to reduce bias are rarely used to monitor adherence. We conducted a list experiment to elicit truthful answers to the question whether respondents met friends or acquaintances and thus disregarded the social distancing norm. Our empirical findings are mixed. Using the list experiment, we estimate the prevalence of non-compliant behavior at 28\%, whereas the estimate from a direct question is 22\%. However, a more permissively phrased direct question included later in the survey yields an estimate of 47\%. All three estimates vary consistently across social groups. Interestingly, only the list experiment reveals somewhat higher non-compliance rates among the highly educated compared to those with lower education, yet the variance of the list estimates is considerably higher. We conclude that the list experiment compared unfavorably to simpler direct measurements in our case.}, language = {en} }