This dataset comprises scores of 8,954 first-year psychology students from the University of Amsterdam (1982-2007) on the ‘Vijf PersoonlijkheidsFactoren Test’ or 5PFT (Elshout & Akkerman, 1975), which is an instrument to measure the Big Five personality factors Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness to Experience. Selection of participants as described in Smits et al. on page 1126 and in their Table 1.This dataset formed the basis of the article "Cohort Differences in Big Five Personality Factors Over a Period of 25 Years" (See the link to the DOI in the Relationfield) authored by Iris A. M. Smits, Conor V. Dolan, Harrie C.M. Vorst, Jelte M. Wicherts, & Marieke E. Timmerman.A data paper about this data is available at: Smits, Iris A. M., Dolan, C. V., Vorst, H. C. M., Wicherts, J M., Timmerman, M. E. Data from ‘Cohort Differences in Big Five Personality Factors Over a Period of 25 Years’. Journal of Open Psychology Data 1(1). (See the link to the DOI in the Relationfield)This data is released under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data are available both in CSV format (with a txt codebook) and as a SPSS .sav file.

Currently, 2.7 billion people use at least one of the Facebook-owned social media platforms – Facebook, WhatsApp, and Instagram. Previous research investigating individual differences between users and non-users of these platforms has typically focused on one platform. However, individuals typically use a combination of Facebook-owned platforms. Therefore, we aim (1) to identify the relative prevalence of different patterns of social media use, and (2) to evaluate potential between-group differences in the distributions of age, gender, education, and Big Five personality traits. Data collection was performed using a cross-sectional design. Specifically, we administered a survey assessing participants’ demographic variables, current use of Facebook-owned platforms, and Big Five personality traits. In N = 3003 participants from the general population (60.67% females; mean age = 35.53 years, SD = 13.53), WhatsApp emerged as the most widely used application in the sample, and hence, has the strongest reach. A pattern consisting of a combined use of WhatsApp and Instagram appeared to be most prevalent among the youngest participants. Further, individuals using at least one social media platform were generally younger, more often female, and more extraverted than non-users. Small differences in Conscientiousness and Neuroticism also emerged across groups reporting different combinations of social media use. Interestingly, when examined as control variables, we found demographic characteristics partially accounted for differences in broad personality factors and facets across different patterns of social media use. Our findings are relevant to researchers carrying out their studies via social media platforms, as sample characteristics appear to be different depending on the platform used.

Dataset for: The NB5I: A full-scale Big-Five inventory with evaluatively neutralized items: Development sample

Providing good indoor environmental quality (IEQ) that satisfies building occupants is an essential component for sustainable and healthy buildings. Existing studies mainly analysed the importance of environmental factors to subject’s satisfaction, but overlooked the influence by personal factor, especially the psychological dimensions. We aims to explore the important personal factors that affect subject’s IEQ satisfaction. We conducted a cross-sectional assessment which surveyed 1162 individuals on their satisfaction with 18 IEQ parameters and their corresponding personal factors (job satisfaction, life satisfaction, Big Five personality traits, sex and age) in nine air-conditioned commercial buildings in Singapore. Analyses using proportional odds ordinal logistic regression suggested occupants with higher job and life satisfactions were, respectively, 1.8 – 3.1 and 1.4 – 2.2 times more likely satisfied with the indoor environment. In particular, the odds ratio (OR) for overall environment satisfaction in job and life satisfaction were 3.1 (95% CI: 2.4 – 4) and 2.2 (95% CI: 1.7 – 2.8). We speculate that occupant’s satisfactions with their job and the environment are coherent, meaning that better workspace could improve job satisfaction and vice versa. Overall, personality traits, sex and age groups had a small effect on IEQ satisfaction. Each of the five personality traits has some associations with different IEQ parameters, but all these impacts were small. Female were slightly more dissatisfied with the workspace temperature, humidity, stuffiness, natural light, glare sound privacy and cleanliness than male. Lastly, occupant’s age did not show consistent impacts on their satisfaction with the workspace IEQ.

Methods Nine Green Mark certified, air-conditioned office, buildings in Singapore were surveyed resulting in 1006 individual responses. Our survey scope only included staff who were performing office work and had personal workstations, and excluded any non-office spaces within the same building to maintain consistency. In each building, at least 10 % of the total occupancy were surveyed. We provided individual survey link for each building, while the facility management team distributed this link to all target occupants in the building. In this study, we collected responses from occupant’s (i) satisfaction with their workspace environment, (ii) demographics, (iii) job satisfaction, (iv) life satisfaction, and (v) personality traits.

No description was included in this Dataset collected from the OSF

The corpus contains a list of words/phrases in non-formal Indonesian language which mapped to specific traits and sub-traits of Big Five Personality traits. This list has been curated by psychologists and linguistic experts.

No description was included in this Dataset collected from the OSF

These files are AMOS v24 input files used to analyse the BFI10 inventory items.

No description was included in this Dataset collected from the OSF

Data from https://openpsychologydata.metajnl.com/articles/10.5334/jopd.e2

No description was included in this Dataset collected from the OSF

The methodology is the core component of any research-related work. The methods used to gain the results are shown in the methodology. Here, the whole research implementation is done using python. There are different steps involved to get the entire research work done which is as follows:

1. Acquire Personality Dataset

The kaggle machine learning dataset is a collection of datasets, data generators which are used by machine learning community for analysis purpose. The personality prediction dataset is acquired from the kaggle website. This dataset was collected (2016-2018) through an interactive on-line personality test. The personality test was constructed from the IPIP. The personality prediction dataset can be downloaded in zip file format just by clicking on the link available. The personality prediction file consists of two subject CSV files (test.csv & train.csv). The test.csv file has 0 missing values, 7 attributes, and final label output. Also, the dataset has multivariate characteristics. Here, data-preprocessing is done for checking inconsistent behaviors or trends.

2. Data preprocessing

After, Data acquisition the next step is to clean and preprocess the data. The Dataset available has numerical type features. The target value is a five-level personality consisting of serious,lively,responsible,dependable & extraverted. The preprocessed dataset is further split into training and testing datasets. This is achieved by passing feature value, target value, test size to the train-test split method of the scikit-learn package. After splitting of data, the training data is sent to the following Logistic regression & SVM design is used for training the artificial neural networks then test data is used to predict the accuracy of the trained network model.

3. Feature Extraction

The following items were presented on one page and each was rated on a five point scale using radio buttons. The order on page was EXT1, AGR1, CSN1, EST1, OPN1, EXT2, etc. The scale was labeled 1=Disagree, 3=Neutral, 5=Agree

        EXT1 I am the life of the party.
        EXT2  I don't talk a lot.
        EXT3  I feel comfortable around people.
        EXT4  I am quiet around strangers.
        EST1  I get stressed out easily.
        EST2  I get irritated easily.
        EST3  I worry about things.
        EST4  I change my mood a lot.
        AGR1  I have a soft heart.
        AGR2  I am interested in people.
        AGR3  I insult people.
        AGR4  I am not really interested in others.
        CSN1  I am always prepared.
        CSN2  I leave my belongings around.
        CSN3  I follow a schedule.
        CSN4  I make a mess of things.
        OPN1  I have a rich vocabulary.
        OPN2  I have difficulty understanding abstract ideas.
        OPN3  I do not have a good imagination.
        OPN4  I use difficult words.

4. Training the Model

Train/Test is a method to measure the accuracy of your model. It is called Train/Test because you split the the data set into two sets: a training set and a testing set. 80% for training, and 20% for testing. You train the model using the training set.In this model we trained our dataset using linear_model.LogisticRegression() & svm.SVC() from sklearn Package

5. Personality Prediction Output

After the training of the designed neural network, the testing of Logistic Regression & SVM is performed using Cohen_kappa_score & Accuracy Score.

Educational transitions play a pivotal role in shaping educational careers, and ultimately social inequality. Whereas parental socioeconomic status (SES) and cognitive ability have long been identified as key determinants of successful educational transitions, much less is known about the role of socio-emotional skills. To address this gap, the present study investigated whether Big Five personality traits predict success in the transition from secondary school to vocational education and training (VET) above and beyond SES, cognitive ability, and other covariates. Using data from Starting Cohort 4 of the German National Educational Panel Study (NEPS; N = 4,137), we defined seven indicators of successful transition: obtaining a VET position, number of acceptances for VET positions, starting a VET position, (the absence of) dropout intentions and actual dropout, final VET grade, and satisfaction with VET. The results revealed that some Big Five traits were incrementally associated with several indicators of transition success. Conscientiousness emerged as the single most relevant trait, predicting all the transition success indicators but 1 (dropout intentions). The other Big Five traits had much weaker and less consistent links with transition success. Extraversion predicted the final VET grade and obtaining a VET position; Agreeableness was linked to a higher risk of dropout. Openness and Emotional Stability had no incremental effects on transition success. There was also some evidence for both compensatory and synergistic interactive effects, with Openness moderating mainly the effects of parental SES (on dropout intentions, actual dropout, and number of acceptances), and Agreeableness moderating the effects of cognitive ability (on obtaining a VET position, number of acceptances, and satisfaction with VET). Although individual effect sizes were small, the Big Five’s joint contribution to transition success was non-negligible, and often larger than that of sociodemographic characteristics and cognitive ability. Our results suggest a hitherto underappreciated contribution of personality to successful transitions to VET.

R Code and data to reproduce Humor styles and personality: A systematic review and meta-analysis on the relations between humor styles and the Big Five personality traits.

In 2023/24, the Bundesliga was the highest attended league out of the Big Five, with an average match attendance of just over 39,500. This represented a slight decrease on 2022/23, with the season's newly promoted teams having relatively small stadiums. Meanwhile, the Premier League saw average crowds of around 38,557. What is the Big Five? The term 'Big Five' refers to the highest divisions of professional soccer in England, Germany, Spain, France, and Italy. Looking at the market size of professional soccer leagues in Europe reveals the dominance of the Big Five over other leagues in the continent. In 2024, the Premier League had the highest brand value of the Big Five leagues - from a financial perspective, England’s top division is certainly the biggest of the Big Five. Which team has the highest attendance in the Bundesliga? Borussia Dortmund enjoyed the highest average attendance in the Bundesliga throughout 2023/24. During that season, the Black and Yellows welcomed just over 81,000 fans to the Signal Iduna Park for home games. Borussia Dortmund’s ground, Signal Iduna Park, was also the stadium with the largest capacity in the Bundesliga in 2024/25, ranking above Klassiker rivals Bayern Munich.

No description was included in this Dataset collected from the OSF

The statistic depicts the price range of shirts on sale in the 'Big Five' European soccer leagues in the 2012/13 season. A shirt of an English Premier League club cost around 59 euros on average.

The Hawaii Personality and Health Cohort consists of teacher ratings of their students' personalities. John M. (Jack) Digman orchestrated the collection of the child personality data between 1959 and 1967, during his tenure as a professor at the University of Hawaii. Childhood data was collected on 2418 children in classrooms on the islands of Oahu and Kauai. Six waves of data collection were completed, and eighty-eight teachers provided assessments of their students. Children ranged in age from 5 to 14, and were in grades 1,2,3,5 or 6. The initial goal of this work was to generate ratings using a broad set of items to allow for research on the structure of personality in childhood. The data collection predated the acceptance of the Big Five model of personality. Items were selected to capture the entire range of observable personality, which at the time was thought to be characterized by 10 or more domains. Subsequent analysis by Dr. Digman, and later by Lewis R. (Lew) Goldberg, demonstrated a consistent five factor structure in the child personality data. In the early days of the emergence of the Big Five model of personality structure, the Hawaii child data provided initial evidence to support the acceptance of Big Five model of personality. Subsequent follow-up of the sample in adulthood has included multiple questionnaires, and assessments of objective markers of health. These follow-up data allowed for the first ever assessment of the stability in the Big Five over a span of 40 years. At average age 50, participants were recruited for a half day clinic visit. Objective markers of health collected at this time have supported work testing childhood personality as a predictor of physical health, and also research testing lifespan pathways linking childhood personality to physical health in adulthood. This initial release includes the full childhood cohort data. Also included are a set of Big Five scores that have been used in published research on the Hawaii Personality Cohort, and a number of different sets of personality scales derived from these data. Basic demographic information is also provided. Subsequent data releases will include questionnaire and clinic data collected in adulthood. For additional information about the correspondence between these datasets, please see the accompanying Excel file, which provides a table of overlapping variables across the datasets. Further information about this crosswalk file can be found in the "Item Overlap" section of the accompanying Study Description document. Demographic variables included in this study include gender, cultural identity, and year of birth.

In 2022/23, clubs in the Premier League spent more on wages than any other league in Europe's Big Five, with wage costs totaling over 4.6 billion euros. By comparison, clubs in La Liga spent a combined total of nearly 2.5 billion euros.

Etic psychometric tools work less well in non-Western than in Western cultures, whereas data collected online in the former societies tend to be of superior quality to those from face-to-face interviews. This represents a challenge to the study of the universality of models of personality and other constructs. If one wishes to uncover the true structure of personality in a non-Western nation, should one study only highly educated, cognitively sophisticated Internet users, and exclude the rest? We used a different approach. We adapted a short Big Five tool, previously tested successfully in 19 countries on all continents, to Mongolian culture. EFA and CFA analyses across a nationally representative sample of 1,500 adult Mongolians recovered the Big Five satisfactorily. A Big Two (plasticity and stability) model was also recovered reasonably well. Correlations between personality traits and age, as well as gender differences, were not different from those reported for Western samples. Respondents with higher education, or higher-than-average socioeconomic status, or urban dwellers, or Internet users, did not yield a clearer Big Five than the whole sample. Our method (tool adaptation to a local cultural context) may be preferable to exclusion of specific demographic groups in Big Five studies of non-Western populations.