21 to 30 of 697 Results
Schober, Barbara; Lüftenegger, Marko; Spiel, Christiane, 2020, "Learning conditions during COVID-19 school context (SUF edition)", https://doi.org/10.11587/VRQL3B, AUSSDA, V3, UNF:6:HK+6hpxT0zusQ3/8AEX+PA== [fileUNF]
Full edition for scientific use. The study "Learning conditions during COVID-19 school context" of the University of Vienna consists of two phases: Phase I, conducted between April and June 2020, and phase II, conducted between November 2020 and May 2021. Phase I contains three m... |
Plescia, Carolina; Wilhelm, James; Kritzinger, Sylvia; Schüberl, Tanja; Partheymüller, Julia, 2020, "RECONNECT 2019 European Parliament Election Panel Survey (SUF edition)", https://doi.org/10.11587/MOV0EZ, AUSSDA, V2, UNF:6:rxEKy3OxcqnwCodNUrdJcQ== [fileUNF]
Full edition for scientific use. A pre- and post-campaign online survey of eligible voters in the 2019 European Parliament election in seven EU member states: Denmark, France, Germany, Hungary, Italy, Poland, Spain. |
Statistics Austria, 2020, "Microcensus Labour Force Survey / Housing Survey 2020 (SUF edition)", https://doi.org/10.11587/GXAPI7, AUSSDA, V4, UNF:6:roUO2IJsXANPn0bbM4NsBw== [fileUNF]
Full edition for scientific use. With about 20,000 households surveyed per quarter, the Microcensus is the largest regularly conducted sample survey in Austria. It is an important data source for national and international labour market indicators and regularly provides informati... |
Aichholzer, Julian; Zeglovits, Eva; Kritzinger, Sylvia, 2020, "Case study political socialization with mother-child couples in Vienna 2012 (SUF edition)", https://doi.org/10.11587/GDX8IG, AUSSDA, V1, UNF:6:7bsUWOs/Zx0JROSVRGlckw== [fileUNF]
Full edition for scientific use. A case study on political socialization. The aim of the study was to investigate the similarity in political attitudes and behavior between mothers and their children (dyads) using a standardized survey. |
Vollmer, Christian; Goreth, Sebastian; Mader, Robert; Platz, Melanie; Oberhauser, Claus; Plattner, Irmgard, 2020, "COVID-19 Distance Learning Tyrol (SUF edition)", https://doi.org/10.11587/LWDAD0, AUSSDA, V1, UNF:6:gLb+IOr1DKLpY6tKcpuGxw== [fileUNF]
Full edition for scientific use. New distance learning methods were quickly implemented in Tyrol, Austria, in March 2020 as a response to the Corona pandemic. The study „COVID-19 Distance Learning Tyrol“ surveyed 2314 Tyrolean teachers with an online questionnaire (field time 24t... |
Statistics Austria, 2020, "Microcensus Ad-Hoc-Module "Work organisation and working time arrangements" 2019 (SUF edition)", https://doi.org/10.11587/SUFZHG, AUSSDA, V1, UNF:6:YRwwe+s0wCdy5gDswmU/Og== [fileUNF]
Full edition for scientific use. In addition to the core variables of the Microcensus Labour Force Survey (LFS), the LFS also has so-called ad-hoc-modules (AHM) that can vary from year to year. The EU-LFS ad-hoc-module 2019 on ‘Work organisation and working time arrangements’ is... |
Dec 17, 2020
In this dataverse, AUSSDA-accredited self-depositors share their datasets directly with users. |
Dec 11, 2020 - Survey Methods: Insights from the Field
Andreadis, Ioannis, 2020, "Replication Data for: Text Message (SMS) Pre-notifications, Invitations and Reminders for Web Surveys", https://doi.org/10.11587/DX8NNE, AUSSDA, V1
This paper presents the findings derived from a mobile friendly web survey, during which all invitations and reminders were sent as text messages via short message service (SMS) to the mobile telephones of the target group. The web survey under study was conducted for the Nationa... |
Haselmayer, Martin; Jenny, Marcelo, 2020, "The German Political Sentiment Dictionary (SUF edition)", https://doi.org/10.11587/7PFLIU, AUSSDA, V1, UNF:6:fddvmzxSkM88VgrqvxOqTg== [fileUNF]
Full edition for scientific use. This dataset contains a German-language sentiment dictionary of 5001 negative words and their associated sentiment strength on a five-point-scale from 0 (not negative) to 4 (very strongly negative). The procedure for building the dictionary contai... |
Haselmayer, Martin; Jenny, Marcelo, 2020, "Training Data for German Sentiment Analysis of Political Communication (SUF edition)", https://doi.org/10.11587/EOPCOB, AUSSDA, V1, UNF:6:wx/QlNpI73mL4r3N+cNhBg== [fileUNF]
Full edition for scientific use. The dataset contains 125871 sentences extracted from Austrian parliamentary debates and party press releases. Press releases were collected under the auspices of the Austrian National Election Study (AUTNES) and cover 6 weeks prior to each nationa... |
