Predicting current voting intentions by Big Five personality domains, facets, and nuances – A random forest analysis approach in a German sample
Author(s) / Creator(s)
Sindermann, Cornelia
Mõttus, René
Rozgonjuk, Dmitri
Montag, Christian
Abstract / Description
To understand what was driving individual differences in voting intentions in a large German sample, we investigated the predictability of voting intentions from the Big Five personality domains, facets, and nuances, thereby tackling shortcomings of previous studies. Using random forest analyses in a dataset of N = 4,286 individuals (46.01% men), separate models were trained to predict intentions to 1) not vote versus to vote, 2) vote for a specific party, and 3) vote for a left- versus right-from-the-center party from either the Big Five personality domains, facets, or nuances (represented by individual items). Except for intentions to not vote versus to vote, balanced accuracies to predict voting intentions marginally exceeded those achieved by a baseline learner always predicting the majority class. Using nuances over facets and domains slightly increased balanced accuracies. Results indicate that additional variables should be considered to accurately predict voting intentions, at least in German samples.
Keyword(s)
Big Five personality voting intentions voting random forestPersistent Identifier
Date of first publication
2021-09-21
Journal title
Personality Science
Volume
2
Article number
Article e6017
Publisher
PsychOpen GOLD
Publication status
publishedVersion
Review status
peerReviewed
Is version of
Citation
Sindermann, C., Mõttus, R., Rozgonjuk, D., & Montag, C. (2021). Predicting current voting intentions by Big Five personality domains, facets, and nuances – A random forest analysis approach in a German sample. Personality Science, 2, Article e6017. https://doi.org/10.5964/ps.6017
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ps.v02.6017.pdfAdobe PDF - 263.25KBMD5: 2b031583750c17aaf0870c3aaf5979e8
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Author(s) / Creator(s)Sindermann, Cornelia
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Author(s) / Creator(s)Mõttus, René
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Author(s) / Creator(s)Rozgonjuk, Dmitri
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Author(s) / Creator(s)Montag, Christian
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PsychArchives acquisition timestamp2022-04-14T11:25:10Z
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Made available on2022-04-14T11:25:10Z
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Date of first publication2021-09-21
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Abstract / DescriptionTo understand what was driving individual differences in voting intentions in a large German sample, we investigated the predictability of voting intentions from the Big Five personality domains, facets, and nuances, thereby tackling shortcomings of previous studies. Using random forest analyses in a dataset of N = 4,286 individuals (46.01% men), separate models were trained to predict intentions to 1) not vote versus to vote, 2) vote for a specific party, and 3) vote for a left- versus right-from-the-center party from either the Big Five personality domains, facets, or nuances (represented by individual items). Except for intentions to not vote versus to vote, balanced accuracies to predict voting intentions marginally exceeded those achieved by a baseline learner always predicting the majority class. Using nuances over facets and domains slightly increased balanced accuracies. Results indicate that additional variables should be considered to accurately predict voting intentions, at least in German samples.en_US
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Publication statuspublishedVersion
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Review statuspeerReviewed
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CitationSindermann, C., Mõttus, R., Rozgonjuk, D., & Montag, C. (2021). Predicting current voting intentions by Big Five personality domains, facets, and nuances – A random forest analysis approach in a German sample. Personality Science, 2, Article e6017. https://doi.org/10.5964/ps.6017en_US
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ISSN2700-0710
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Persistent Identifierhttps://hdl.handle.net/20.500.12034/5723
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Persistent Identifierhttps://doi.org/10.23668/psycharchives.6327
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Language of contenteng
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PublisherPsychOpen GOLD
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Is version ofhttps://doi.org/10.5964/ps.6017
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Is related tohttps://doi.org/10.23668/psycharchives.5114
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Keyword(s)Big Fiveen_US
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Keyword(s)personalityen_US
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Keyword(s)voting intentionsen_US
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Keyword(s)votingen_US
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Keyword(s)random foresten_US
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Dewey Decimal Classification number(s)150
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TitlePredicting current voting intentions by Big Five personality domains, facets, and nuances – A random forest analysis approach in a German sampleen_US
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DRO typearticle
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Article numberArticle e6017
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Journal titlePersonality Science
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Volume2
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Visible tag(s)Version of Recorden_US