Personality and Happiness

Pishva, Nooshin; Ghalehban, Maryam; Moradi, Afsane; Hoseini, Leila

doi:10.1016/j.sbspro.2011.10.084

Cited by 39 publications

(31 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The consistency of these correlations displays the effectiveness of predicting SWB from the FFM of personality. Correlational studies suggest that individuals are sensitive to certain stimuli and thus will respond to events differently [37][38][39]. Personality type predisposes an individual to experiencing certain life events, which in turn affects an individual's level of SWB [40].…”

Section: The Relationship Between Subjective Wellbeing and The Five Fmentioning

confidence: 99%

Recreating the Relationship between Subjective Wellbeing and Personality Using Machine Learning: An Investigation into Facebook Online Behaviours

Marinucci

Kraska

Costello

2018

BDCC

View full text Add to dashboard Cite

Abstract:The twenty-first century has delivered technological advances that allow researchers to utilise social media to predict personal traits and psychological constructs. This article aims to further our understanding of the relationship between subjective wellbeing (SWB) and the Five Factor Model (FFM) of personality by attempting to replicate the relationship using machine learning prediction models. Data from the myPersonality Project was used; with observed SWB scores derived from the Satisfaction With Life Scale (SWLS) and Five Factor Model (FFM) personality profiles generated using responses on the 100-item IPIP proxy of the NEO-PI-R. After data cleaning, FFM personality traits and SWB scores were predicted by reducing Facebook Likes into 50 dimensions using SVD and then running the data through six multiple regressions (fitting the model via least squares and splitting the data via k-folds validation) with the Likes dimensions as predictors and each of the FFM traits and the SWB score as response variables. Standard multiple regression analyses were conducted for the observed and machine learning predicted variables to compare the relationships in the context of previous literature. The results revealed that in the observed model, high SWB was predicted by high extraversion, conscientiousness, and agreeableness, and low openness to experience and neuroticism as per previous research. For the machine learning model, high SWB was predicted by high extraversion, openness to experience, conscientiousness, and agreeableness, and low neuroticism. The relationships between SWB and extraversion, neuroticism, and conscientiousness were successfully replicated in the machine learning model. Openness to experience changed direction in its relationship with SWB from the observed to machine learning-derived variables due to failure to accurately recreate the variable, and agreeableness was multicollinear with SWB in the machine learning model due to the unknowing use of identical digital behaviours to replicate each construct. Implications of the results and directions for future research are discussed.

show abstract

Section: The Relationship Between Subjective Wellbeing and The Five Fmentioning

confidence: 99%

Recreating the Relationship between Subjective Wellbeing and Personality Using Machine Learning: An Investigation into Facebook Online Behaviours

Marinucci

Kraska

Costello

2018

BDCC

View full text Add to dashboard Cite

show abstract

“…The OHI had a reliable reliability with α: 0.92, and it had a strong internal consistency to measurement of happiness in the cases (P ˂ 0.001) [24]. Some investigations have indicated OHI has had enough validity and reliability [25,26,27].…”

Section: Translation Of the Questionnairesmentioning

confidence: 95%

Evaluation of Health and Happiness of Mothers of Children with Sleep Disorders

Khademloo¹

2017

EJPM

View full text Add to dashboard Cite

Abstract:Aim: The aim of this study was to evaluate the overall health and happiness of healthy mothers of children with sleep disorder. Methods and material: This study was cross sectional study. To collect data from general health questionnaire (GHQ -28) with 28 questions and four subscales (somatic disorders, anxiety, social dysfunction and depression) and Oxford happiness questionnaire on five factors (satisfaction life, positive mood, health, and self-efficacy) with 29 questions was used. To analyze the data, independent t-test was used. Result: The results showed that the overall health and happiness of mothers of children with sleep disorder were affected by their children disorder. Conclusion: Mothers' health is associated with and affected by their children and sleep disorders in children reduce the quality of life in mothers.

show abstract

“…Criteria based validation proved that the OHQ positively correlated with the extraversion personality dimensions and negatively correlated with neuroticism [1], [5]- [7]. In addition, the OHQ score positively correlated with agreeableness, conscientiousness and openness [6].…”

Section: Introductionmentioning

confidence: 93%

Psychometric Properties of Indonesian Version of the Oxford Happiness Questionnaire

Rahmawati

Saragih

Adeline

2017

Proceedings of the 1st Public Health International Conference (PHICo 2016)

View full text Add to dashboard Cite

Abstract-Oxford Happiness Questionnaire (OHQ) is an instrument constructed by Peter Hills and Argyle to measure happiness. OHQ is considered a good instrument, has been translated into several languages and has been used widely in several countries for researchs. The objective of this study is to examine the psychometric properties of the Indonesian version of OHQ. The empirical finding is expected to further validate the Classical Test Theory approach used before. Evaluation of psychometric properties was analyzed using the Item Response Theory (IRT) with Graded Response Model (GRM). A total of 450 people with the age of 18 to 40 have participated in the study. The results have showed that the items in Indonesian version of OHQ have reasonably high discriminating power and are accurate in measuring happiness. As a result, it can be stated that the Indonesian version of OHQ has good psychometric properties and can be used as a reliable measurement tool for evaluating individual happiness in Indonesia for research purpose.

show abstract

Personality and Happiness

Cited by 39 publications

References 7 publications

Recreating the Relationship between Subjective Wellbeing and Personality Using Machine Learning: An Investigation into Facebook Online Behaviours

Recreating the Relationship between Subjective Wellbeing and Personality Using Machine Learning: An Investigation into Facebook Online Behaviours

Evaluation of Health and Happiness of Mothers of Children with Sleep Disorders

Psychometric Properties of Indonesian Version of the Oxford Happiness Questionnaire

Contact Info

Product

Resources

About