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Publication Year: 2020

381 to 390 of 2,981 Results

10724_da_en_v1_0.zsav Dec 17, 2020 - AUTNES Content Analysis of Party Manifestos: Cumulative File (SUF edition) Unknown - 10.4 MB - MD5: 20dd6d0e8385775f2e14eb8806933400 DataSPSS Core data file - compressed SPSS format - 33 Variables, 77573 Observations
10724_mr_en_v1_0.pdf Dec 17, 2020 - AUTNES Content Analysis of Party Manifestos: Cumulative File (SUF edition) Adobe PDF - 774.1 KB - MD5: b813de2b5968c827a30c4f8d54779ae8 DocumentationMethod report Method report
10724_vi_en_v1_0.tab Dec 17, 2020 - AUTNES Content Analysis of Party Manifestos: Cumulative File (SUF edition) Tabular Data - 445.1 KB - 3 Variables, 11911 Observations - UNF:6:yjFhsObnuv4OUh7FcwKMng== Documentation Variable identifiers and descriptions - machine-readable
Replication Data for: Text Message (SMS) Pre-notifications, Invitations and Reminders for Web Surveys 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...
10667_da_en_v1_0.Rdata Dec 11, 2020 - Replication Data for: Text Message (SMS) Pre-notifications, Invitations and Reminders for Web Surveys Gzip Archive - 24.5 KB - MD5: 40c113ce9d2b95f7942b743f1da113bf DataR Core data file - R format
10667_sx_en_v1_0.R Dec 11, 2020 - Replication Data for: Text Message (SMS) Pre-notifications, Invitations and Reminders for Web Surveys R Syntax - 13.6 KB - MD5: 79be238885f3456aef54585a90ab77bb CodeDocumentation R code for replication
The German Political Sentiment Dictionary (SUF edition) Dec 2, 2020 - AUTNES 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...
10274_da_de_v1_0.tab Dec 2, 2020 - The German Political Sentiment Dictionary (SUF edition) Tabular Data - 399.2 KB - 8 Variables, 5001 Observations - UNF:6:fddvmzxSkM88VgrqvxOqTg== DataSTATA Core data file - STATA format - 8 Variables, 5001 Observations
10274_mr_en_v1_0.pdf Dec 2, 2020 - The German Political Sentiment Dictionary (SUF edition) Adobe PDF - 62.9 KB - MD5: 050d20c9d45e62745c30f748be8cb9ed DocumentationMethod report Method report
Training Data for German Sentiment Analysis of Political Communication (SUF edition) Nov 26, 2020 - AUTNES 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...

10724_da_en_v1_0.zsav

Dec 17, 2020 - AUTNES Content Analysis of Party Manifestos: Cumulative File (SUF edition)

Unknown - 10.4 MB -

DataSPSS

Core data file - compressed SPSS format - 33 Variables, 77573 Observations

10724_mr_en_v1_0.pdf

Dec 17, 2020 - AUTNES Content Analysis of Party Manifestos: Cumulative File (SUF edition)

Adobe PDF - 774.1 KB -

DocumentationMethod report

Method report

10724_vi_en_v1_0.tab

Dec 17, 2020 - AUTNES Content Analysis of Party Manifestos: Cumulative File (SUF edition)

Tabular Data - 445.1 KB - 3 Variables, 11911 Observations -

Documentation

Variable identifiers and descriptions - machine-readable

Replication Data for: Text Message (SMS) Pre-notifications, Invitations and Reminders for Web Surveys

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...

10667_da_en_v1_0.Rdata

Dec 11, 2020 - Replication Data for: Text Message (SMS) Pre-notifications, Invitations and Reminders for Web Surveys

Gzip Archive - 24.5 KB -

DataR

Core data file - R format

10667_sx_en_v1_0.R

Dec 11, 2020 - Replication Data for: Text Message (SMS) Pre-notifications, Invitations and Reminders for Web Surveys

R Syntax - 13.6 KB -

CodeDocumentation

R code for replication

The German Political Sentiment Dictionary (SUF edition)

Dec 2, 2020 - AUTNES

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...

10274_da_de_v1_0.tab

Dec 2, 2020 - The German Political Sentiment Dictionary (SUF edition)

Tabular Data - 399.2 KB - 8 Variables, 5001 Observations -

DataSTATA

Core data file - STATA format - 8 Variables, 5001 Observations

10274_mr_en_v1_0.pdf

Dec 2, 2020 - The German Political Sentiment Dictionary (SUF edition)

Adobe PDF - 62.9 KB -

DocumentationMethod report

Method report

Training Data for German Sentiment Analysis of Political Communication (SUF edition)

Nov 26, 2020 - AUTNES

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...

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