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.
Predictability of voting intentions from personality was investigated. Big Five personality domains, facets, and nuances were examined. Random forest analyses were implemented. Voting intentions could hardly be predicted from self-reported personality.
The present work aimed to overcome limitations of previous research by 1) implementing a prediction design and 2) investigating Big Five personality facets and nuances besides domains to compare predictive accuracy of models across levels of the trait hierarchy.
Voting is a civil right in democracies around the world. By voting, citizens have the power to influence political developments. Therefore, understanding what underlies voting decisions and political preferences is an important aim in several fields of research like political science and psychology. With regard to personality psychology, previous findings indicate weak associations between the Big Five personality domains and, for example, political left- versus right-positioning and party preferences, accordingly (
Personality traits constitute a pool of hierarchically organized characteristics, with the Big Five personality domains of Openness (to Experience), Conscientiousness, Extraversion, Agreeableness, and Neuroticism (
Each of the Big Five personality domains can be split into different narrower aspects also called facets, although there is no consensus yet regarding an exhaustive set of facets (
As a result, it seems advisable to compare different levels of the personality trait hierarchy in terms of predictive accuracy when exploring the links of personality with other variables, such as voting intentions. In some cases, the Big Five personality domains may provide just as accurate predictions as lower levels of the trait hierarchy, but the reverse is more likely (
When investigating voting intentions, a first step includes examination of intentions to not vote versus to vote. The few previous studies investigating associations between Big Five personality domains and non-voting versus voting intentions as well as non-voting versus voting behavior/self-reported voter turnout have reported inconclusive results. The mixed findings might be due to differences in voting settings (e.g., countries and years) as well as specific measures used. Hence, the results might only be applicable to the specific samples under investigation (
In contrast, results on associations between Big Five personality domains and political ideology (which is likely to be associated with voting [intentions] for a specific party) are more conclusive, especially regarding the Openness and Conscientiousness domains. While Openness has been positively associated with a more left-leaning and liberal ideological self-positioning, Conscientiousness has been positively associated with a more right-leaning and conservative self-positioning (
Results of two meta-analyses on German samples support the associations of political ideology with the Openness and Conscientiousness domains. Moreover, these studies report smaller effects of Agreeableness (in one of the meta-analyses) and Neuroticism (in both meta-analyses), which were both related to higher left-leaning and lower conservative ideological self-positioning (
In Germany, there are currently six parties/party alliances included in the
Associations between the Big Five personality domains and voting intentions and attitudes towards specific parties in the German context have been investigated before. One study found highest scores in Openness in individuals who would vote for Bündnis 90/Die Grünen in comparison to putative voters of the other major German parties (
The Big Five personality domains appear to be
For the present work, we chose to implement random forest analyses. This approach differs from, for example, different kinds of regression analyses for classification problems (e.g., binomial/binary logistic, multinomial logistic) in many ways. Of importance for the present work, the non-parametric random forest algorithm 1) does not assume linear relations between predictor and criterion variables, 2) can deal with a large number of intercorrelated predictors, 3) is robust against outliers, and 4) combinations of predictor variables are automatically considered, thus, also combinations (i.e., statistical interactions) of variables not expected by the researchers are taken into account (
In conclusion, the present study aimed at investigating the aforementioned associations by means of random forest analyses in a German sample. We had no predefined hypotheses with regards to predictions of intentions to not vote versus to vote. Regarding voting intentions for a specific party (leaning), Openness and Conscientiousness were expected to be the most important predictors. No hypotheses were built for Big Five personality facets or nuances given the lack of existing literature.
By means of two online surveys, we recruited a convenience sample of
The Big Five personality traits were assessed by applying the Big Five Inventory (BFI;
Individuals stated which party they would vote for if general elections were held the following Sunday. Response options were CDU/CSU (
The statistical software R (Version 4.1.0;
Next, we trained random forest models to predict intentions 1) to not vote versus to vote and 2) to vote for specific parties (within putative voters) from either Big Five i) domains, ii) facets, or iii) items (6 models in total). More specifically, we implemented a 10-times repeated 10-fold cross-validation procedure. Thus, we split the total sample into ten different folds of equal size (and with equal distributions of the respective criterion variable) ten times; thus, a total of 100 different folds were built across repeats. Moreover, we needed to account for class imbalance in the criterion variables. Prominent methods to deal with imbalanced data are weighting and over-/under-sampling (
The results across folds and repeats were averaged. More specifically, we extracted mean accuracies across folds and repeats and compared these to the No Information Rate (NIR), which is the accuracy derived from always predicting the majority class (i.e., percentage of observations in the majority class). Additionally, we computed the mean balanced accuracies ([sensitivity + specificity] / 2) from the confusion matrices derived from the 10-times repeated 10-fold cross-validations. Balanced accuracies are of importance in this specific work given the imbalanced class distributions and because the balanced accuracy weights performance of the model for each class equally. Next, we calculated the respective misclassification errors from the confusion matrices across repeated cross-validations. Finally, variable importance scores derived from the final models were extracted and are presented in
The procedure was applied to predict the criterion variables – intentions to not vote versus to vote and intentions to vote for specific parties – by either Big Five personality domains, facets, or nuances in separate models.
We additionally ran the same analyses to predict intentions to vote for left- versus right-from-the-center parties (in individuals indicating that they would vote for one of the major German parties). Grouping of parties into left and right was implemented according to
Finally, results of binomial/binary logistic regression analyses to predict intentions to 1) not vote versus to vote, 2) vote for a specific party (one-vs.-all approach), and 3) vote for left- versus right-from-the-center parties are presented in
The mean prediction accuracies across the 10-times repeated 10-fold cross-validations to predict intentions to not vote versus to vote were 92.94% (
Mean balanced accuracies across the 10-times repeated 10-fold cross-validations were 49.87%, 49.97%, and 49.98% for the models comprising Big Five personality domains, facets, or items, respectively. Thus, all mean balanced accuracies were lower than the balanced accuracy of 50.00% which would be achieved by a model always predicting the majority class.
Confusion matrices and misclassification errors |
||
---|---|---|
Predicted | True score |
|
Non-voting | Voting | |
Domains | ||
Non-voting | 30 | 604 |
Voting | 2,420 | 39,806 |
Facets | ||
Non-voting | 7 | 141 |
Voting | 2,443 | 40,269 |
Items | ||
Non-voting | 0 | 13 |
Voting | 2,450 | 40,397 |
Total |
2,450 | 40,410 |
Misclassification errors | ||
Misclassification errors - domains | 98.78% | 1.49% |
Misclassification errors - facets | 99.71% | 0.35% |
Misclassification errors - items | 100.00% | 0.03% |
Mean prediction accuracies across the 10-times repeated 10-fold cross-validations to predict intentions to vote for a specific party were 23.40% (
Mean balanced accuracies across the 10-times repeated 10-fold cross-validations and across all classes were 51.48% for the models built based on Big Five personality domains, 51.96% for the models comprising facets, and 52.71% for the models comprising items. For each individual class and across the 10-times repeated 10-fold cross-validations, the balanced accuracies lied between 49.08% (“others”) and 53.48% (“CDU/CSU”), between 48.58% (“SPD”) and 54.16% (“DIE LINKE”), and between 50.20% (“FDP”) and 55.74% (“CDU/CSU”) for the models comprising Big Five personality domains, facets, or items, respectively. Thus, 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. More specifically, this was true for voting intentions for all parties except the SPD and “other” parties when predictions were based on domains, for all parties except the SPD when predictions were based on facets, and for all parties when predictions were based on items.
Confusion matrices and misclassification errors |
|||||||
---|---|---|---|---|---|---|---|
Predicted | True score |
||||||
DIE LINKE | SPD | Bündnis 90/ |
FDP | CDU/CSU | AfD | “others” | |
Domains | |||||||
DIE LINKE | 718 | 269 | 2,370 | 342 | 613 | 224 | 724 |
SPD | 302 | 335 | 1,958 | 301 | 601 | 172 | 525 |
Bündnis 90/Die Grünen | 1,337 | 1,173 | 5,985 | 725 | 1,614 | 346 | 1,378 |
FDP | 288 | 283 | 1,485 | 312 | 543 | 232 | 405 |
CDU/CSU | 608 | 550 | 2,978 | 588 | 1,411 | 428 | 760 |
AfD | 193 | 210 | 845 | 270 | 385 | 184 | 338 |
“others” | 674 | 490 | 2,069 | 382 | 693 | 284 | 510 |
Facets | |||||||
DIE LINKE | 808 | 351 | 2,282 | 260 | 438 | 142 | 623 |
SPD | 246 | 119 | 1,183 | 184 | 389 | 44 | 340 |
Bündnis 90/Die Grünen | 1,625 | 1,404 | 7,644 | 1,019 | 2,166 | 527 | 1,748 |
FDP | 197 | 251 | 1,056 | 252 | 399 | 175 | 342 |
CDU/CSU | 564 | 706 | 3,115 | 631 | 1,507 | 567 | 788 |
AfD | 174 | 106 | 715 | 163 | 423 | 189 | 249 |
“others” | 506 | 373 | 1,695 | 411 | 538 | 226 | 550 |
Items | |||||||
DIE LINKE | 604 | 309 | 1,663 | 264 | 346 | 115 | 546 |
SPD | 71 | 99 | 373 | 56 | 168 | 30 | 49 |
Bündnis 90/Die Grünen | 2,341 | 1,880 | 10,832 | 1,445 | 2,838 | 669 | 2,500 |
FDP | 88 | 84 | 444 | 99 | 204 | 114 | 190 |
CDU/CSU | 524 | 630 | 2,895 | 701 | 1,722 | 642 | 798 |
AfD | 65 | 90 | 308 | 109 | 231 | 162 | 79 |
“others” | 427 | 218 | 1,175 | 246 | 351 | 138 | 478 |
Total |
4,120 | 3,310 | 17,690 | 2,920 | 5,860 | 1,870 | 4,640 |
Misclassification errors | |||||||
Misclassification errors - domains | 82.57% | 89.88% | 66.17% | 89.32% | 75.92% | 90.16% | 89.01% |
Misclassification errors - facets | 80.39% | 96.40% | 56.79% | 91.37% | 74.28% | 89.89% | 88.15% |
Misclassification errors - items | 85.34% | 97.01% | 38.77% | 96.61% | 70.61% | 91.34% | 89.70% |
When predicting intentions to vote for left- versus right-from-the-center parties, mean prediction accuracies across 10-times repeated 10-fold cross-validations were 64.15% (
Balanced accuracies across the 10-times repeated 10-fold cross-validations were 56.40% for predictions from Big Five personality domains, 55.87% for predictions from facets, and 57.70% for predictions from items. Thus, balanced accuracies of these models exceeded the balanced accuracy of 50.00% achieved by a model always predicting the majority class.
Confusion matrices and misclassification errors |
||
---|---|---|
Predicted | True score |
|
Left | Right | |
Domains | ||
Left | 18,982 | 6,685 |
Right | 6,138 | 3,965 |
Facets | ||
Left | 19,804 | 7,146 |
Right | 5,316 | 3,504 |
Items | ||
Left | 21,421 | 7,442 |
Right | 3,699 | 3,208 |
Total |
25,120 | 10,650 |
Misclassification errors | ||
Misclassification errors - domains | 24.43% | 62.77% |
Misclassification errors - facets | 21.16% | 67.10% |
Misclassification errors - items | 14.73% | 69.88% |
The present study aimed to investigate the predictability of voting intentions of German individuals from Big Five personality domains, facets, and nuances (indexed by individual items).
Results showed that intentions to not vote versus to vote could not be predicted better than by a baseline learner/model always predicting the majority class (i.e., “voter”).
Regarding the prediction of intentions to vote for a specific party within the group of putative voters, mean prediction accuracies across repeated cross-validations ranged from 23.40% for the models comprising Big Five personality domains to 34.63% for the models comprising nuances. All of these accuracies were below the NIR. In this realm, it is important to note that accurate predictions of individuals’ voting intentions in multi-party systems are difficult to achieve; this has also been observed in other studies. One study using political Facebook likes of individuals, thus, variables much closer related to voting intentions than personality traits, reports a prediction accuracy of around 60% (
For predictions of intentions to vote for left- versus right-from-the-center parties within the group of putative voters of one of the major German parties, mean prediction accuracies across repeated cross-validations ranged from 64.15% for models comprising Big Five personality domains to 68.85% for models comprising nuances. Again, however, all of these accuracies were below the NIR. But balanced accuracies ranged from 55.87% for predictions from Big Five personality facets, to 57.70% for prediction models based on nuances. Hence, the balanced accuracies were exceeding the balanced accuracy of 50.00% achieved by a model always predicting the majority class.
Regarding the aim of the present study to test whether Big Five personality facets and nuances exhibit higher predictive accuracies compared to domains, the overall answer would be: no and yes. For a more elaborate discussion on this, we compare the balanced accuracies across 10-times repeated 10-fold cross-validations of models derived from Big Five personality domains, facets, and nuances. Moreover, we focus on the prediction of voting intentions for specific parties and for left- versus right-from-the-center parties since only models to predict these variables exceeded a balanced accuracy of 50.00%. As can be seen in the results section, the mean balanced accuracies were highest for models based on Big Five personality nuances. However, the balanced accuracies for models based on Big Five personality facets were roughly the same, once even lower, compared to models based on Big Five personality domains. Thus, using nuances increased balanced accuracies of predictions in comparison to using domains and facets; but the increase was only around 1.8% at most. The finding that models based on Big Five personality facets did barely exhibit higher balanced accuracies of predictions over models based on domains might be due to the following fact: in the specific questionnaire applied in this study, not all items are included in facets; but domains and single-item analyses comprise all 44 items (see
Importantly, what is understood as satisfying prediction performance is to a certain degree subjective. Similarly, whether the overall accuracy or the balanced accuracy of a model is considered when evaluating prediction performance is a subjective decision. Therefore, we transparently present both accuracy measures. Nevertheless, it is clear that the predictions of intentions to not vote versus to vote based on the present models were not better than predictions of a baseline learner/model always predicting the majority class: accuracies consistently lied below the NIR and the balanced accuracies consistently lied below 50.00%. Regarding the prediction of voting intentions for a specific party and for left- versus right-from-the-center parties, the present results (regarding accuracies and balanced accuracies) indicate that additional variables need to be taken into account in order to increase the prediction performance (see further discussion below). The fact that accuracies did not exceed the NIR but balanced accuracies exceeded the 50.00% threshold indicates that the algorithms used in the present study might overall perform better in samples with more balanced class distributions.
To judge on the effect sizes found in the present study (for voting for a specific party or left- vs. right-from-the-center parties), we would like to take into account effect sizes reported in other related studies. As such, the correlation between Big Five personality domains and voting for center-right versus center-left parties in the study by
The results have important implications for current debates. As such, it is often discussed in how far knowledge of individuals’ personality can be used to influence their opinions, for example by microtargeting, in the political as well as economic field (
Finally, some limitations of the present study need to be mentioned. First, the sample was not representative of the general German population. The distribution of current voting intentions observed in the present sample did neither reflect the election outcome from the federal elections in 2017, nor the distributions found in general population samples in the time of data collection (
This study sheds light on the predictability of voting intentions by Big Five personality domains, facets, and nuances in the German context. The differentiation between German individuals with the intention to not vote versus to vote was not possible via the random forest analysis approach applied in this work. The predictions of voting intentions for specific parties or for parties on the left versus right side of the spectrum was barely possible. The prediction accuracies might be improved by adding more variables to the models. Still, the accuracies of predictions from models based on Big Five personality nuances were slightly higher than predictions from models based on Big Five personality facets and domains.
The research data for this article is freely available (see the
For this article the following Supplementary Materials are available via PsychArchives (for access see
(A): File "datafile_sample_participants_rf_bf_voting.xlsx" contains data and codebook.
(B): File "data_and_codebook.RData" contains data and codebook.
(C): File "Rscript_rf_bf_voting.R" contains the R-code to reproduce the findings.
(D): File "Supplementary_Material_1.pdf" contains additional information.
(E): File "Supplementary_Material_2.xlsx" contains additional information.
All participants gave informed electronic consent prior to participation. Of the two studies, from which the sample of the present work is drawn, one study was approved by the local ethics committee of Ulm University, Ulm, Germany. The other study was reviewed by the same ethics committee but we received a response letter stating that no ethics approval was needed for this study.
The authors have no funding to report.
The authors have declared that no competing interests exist.
We would like to acknowledge the valuable and constructive feedback we received from the reviewers during the revision process of this manuscript.
A previous version of this manuscript is published as a preprint at the OSF. The preprint can be accessed via the following link