Suchergebnisse

Bisherige Forschung und die öffentliche Debatte lassen vermuten, dass Menschen aus ländlichen Regionen eher rechtspopulistisch wählen als Menschen in nicht-ländlichen Regionen. Gab es diesen Stadt-Land-Unterschied auch bei der Bundestagswahl 2021? Welche Rollen spielen die wirtschaftliche Situation und die infrastrukturelle Versorgung des Wohnorts bzw. dessen Lage in West- oder Ostdeutschland? Durch die Verknüpfung von Umfragedaten und Informationen zu den Stadt- und Landkreisen der befragten Personen präsentieren wir visuelle und statistische Analysen zum Einfluss des Wohnorts auf die Wahlentscheidung. Wir zeigen, dass einzelne Merkmale des Wohnortes mit einer erhöhten Chance der AfD-Wahl einhergehen. Alle örtlichen Erklärungsfaktoren zusammengenommen, sticht der positive Zusammenhang zwischen AfD-Wahl und Wohnort in einem Kreis in Ostdeutschland heraus.

Das hier vorliegende Dokument beschreibt die strategische Ausrichtung der German Longitudinal Election Study (GLES) zur Förderung von Open Science (Offene Wissenschaft). Hierbei wird anhand von vier Grundpfeilern des Konzepts von Open Science - Open Methodology, Open Data, Open Source und Open Access - dargestellt, in welchen Bereichen die GLES derzeit gut abschneidet, in
welchen Bereichen noch Verbesserungspotentiale vorhanden sind und welche Maßnahmen eingeleitet werden sollen, um die GLES nach Open Science Grundsätzen auszurichten. Die Umsetzung dieser Maßnahmen ist als langfristiger Prozess gedacht, bei dem existierende und neue Arbeitsprozesse sich am Ideal einer offenen Wissenschaftspraxis orientieren sollen. Ziel der hier vorgestellten Strategie soll es sein, sowohl Prozesse der Datenerhebung und Datenaufbereitung offen und transparent zu gestalten, als auch Forschende aktiv beim Praktizieren einer offenen Wissenschaft zu unterstützen. Das Dokument wurde von den Autor*innen in enger Rücksprache mit dem gesamten GLES Team bei GESIS und der Koordinierungsgruppe (KG) der GLES verfasst.

The GLES Open Science Challenge 2021 was a pioneering initiative in quantitative political science. Aimed at increasing the adoption of replicable and transparent research practices, it led to this special issue. The project combined the rigor of registered reports - a new publication format in which studies are evaluated prior to data collection/access and analysis - with quantitative political science research in the context of the 2021 German federal election. This special issue, which features the registered reports that resulted from the project, shows that transparent research following open science principles benefits our discipline and substantially contributes to quantitative political science. In this introduction to the special issue, we first elaborate on why more transparent research practices are necessary to guarantee the cumulative progress of scientific knowledge. We then show how registered reports can contribute to increasing the transparency of scientific practices. Next, we discuss the application of open science practices in quantitative political science to date. And finally, we present the process and schedule of the GLES Open Science Challenge and give an overview of the contributions included in this special issue.

AbstractThe GLES Open Science Challenge 2021 was a pilot project aimed at demonstrating that registered reports are an appropriate and beneficial publication format in quantitative political science that helps to increase transparency and replicability in the research process and thus yields substantial and relevant contributions to our discipline. The project resulted in the publication of this special issue, which includes seven registered reports based on data from the German Longitudinal Election Study (GLES) collected in the context of the 2021 German federal election. This concluding article of the special issue brings together the perspectives of the participating authors, reviewers, organizers, and editors in order to take stock of the different experiences gained and lessons learned in the course of the project. We are confident that future projects of a similar nature in political science, as well as authors, reviewers, and editors of registered reports, will benefit from these reflections.

The GLES Open Science Challenge 2021 was a pilot project aimed at demonstrating that registered reports are an appropriate and beneficial publication format in quantitative political science that helps to increase transparency and replicability in the research process and thus yields substantial and relevant contributions to our discipline. The project resulted in the publication of this special issue, which includes seven registered reports based on data from the German Longitudinal Election Study (GLES) collected in the context of the 2021 German federal election. This concluding article of the special issue brings together the perspectives of the participating authors, reviewers, organizers, and editors in order to take stock of the different experiences gained and lessons learned in the course of the project. We are confident that future projects of a similar nature in political science, as well as authors, reviewers, and editors of registered reports, will benefit from these reflections.

The GLES Open Science Challenge 2021 was a pilot project aimed at demonstrating that registered reports are an appropriate and beneficial publication format in quantitative political science that helps to increase transparency and replicability in the research process and thus yields substantial and relevant contributions to our discipline. The project resulted in the publication of this special issue, which includes seven registered reports based on data from the German Longitudinal Election Study (GLES) collected in the context of the 2021 German federal election. This concluding article of the special issue brings together the perspectives of the participating authors, reviewers, organizers, and editors in order to take stock of the different experiences gained and lessons learned in the course of the project. We are confident that future projects of a similar nature in political science, as well as authors, reviewers, and editors of registered reports, will benefit from these reflections.

Die Autor*innen haben festgestellt, dass ein allgemeines Verständnis von Datenqualität für sozialwissenschaftliche Daten erforderlich ist. Bestehende Rahmenwerke bieten zwar wertvolle Orientierungshilfen für die Bewertung der Datenqualität, konzentrieren sich jedoch in der Regel auf bestimmte Dimensionen oder Datentypen. Die Autor*innen sind der Meinung, dass diese Rahmenwerke zwar von entscheidender Bedeutung sind, dass aber eine umfassendere Perspektive auf die Datenqualität erforderlich ist, um die inhärente Mehrdimensionalität der Qualitätsaspekte in sozialwissenschaftlichen Daten vollständig zu erfassen. Daher bietet dieses Positionspapier einen einheitlichen Rahmen für die Bewertung der Datenqualitätsdimensionen sozialwissenschaftlicher Daten.

This study explores how researchers' analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers' expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team's workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers' results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings.