Predicting current voting intentions by Big Five personality domains, facets, and nuances – A random forest analysis approach in a German sample

Cornelia Sindermann,René Mõttus,Christian Montag,Dmitri Rozgonjuk

doi:10.5964/ps.6017

Cornelia Sindermann, René Mõttus + Show 2 more

Open Access

https://doi.org/10.5964/ps.6017

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Abstract

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.

Highlights

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
Balanced accuracies of the predictions of voting intentions for most parties as well as mean balanced accuracies across all parties exceeded the balanced accuracy of 50.00%, which would be achieved by a model always predicting the majority class
The present study aimed to investigate the predictability of voting intentions of German individuals from Big Five personality domains, facets, and nuances

Summary

Introduction

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 leftversus 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 sample

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Results

Conclusion