The Impact of Demographic Variables on Personality Traits: A comprehensive analysis.

 

Theoretical Background

Personality traits have been extensively studied and are known to play a significant role in shaping an individual's thoughts, emotions, and behaviors. However, while much research has focused on understanding the psychological and genetic determinants of personality, there is still a need to explore the influence of demographic variables on personality traits.

Demographic variables, such as marital status, handedness, and age, are important aspects of an individual's social and cultural context. These variables have the potential to interact with personality traits and influence their expression and development. However, the specific ways in which these demographic factors relate to personality traits are not yet fully understood.

Marital status, for example, represents an important social relationship status that can significantly impact an individual's life experiences, support networks, and responsibilities. It is plausible to assume that individuals in different marital statuses may exhibit variations in personality traits due to the different social dynamics and roles associated with being married or unmarried.

Handedness, referring to the preference for using either the right or left hand, is another demographic variable that may be linked to personality traits. Some studies have suggested that left-handed individuals tend to demonstrate differences in cognitive processing and creativity compared to their right-handed counterparts. Exploring the relationship between handedness and personality traits can provide insights into the potential influences of neurological factors on personality expression. Furthermore, age represents a fundamental demographic variable that encompasses different stages of life, varying experiences, and changing social roles. Personality traits are known to exhibit some degree of stability over time, but it is important to examine how age may influence the manifestation and development of personality traits. Understanding the relationship between age and personality traits can shed light on the dynamics of personality maturation and adaptation across the lifespan.

In fact, there is a big body of studies investigating the relationship between personality traits and demographic characteristics. Thus, Extraversion and Openness appear to have negative association with age, whereas Agreeableness has positive association with age (Donnellan & Lucas, 2008). Handedness was tried to be associated with particular personality traits, but results does not seem to be consistent or reliable. As for marital status, it was found that men with higher grit were more likely to stay in marriage (Eskreis-Winkler et al, 2014), but there is not consistent evidence about whether married and unmarried people differ from each other on grit.

By investigating the influence of marital status, handedness, and age on personality traits, this study aims to address the existing gap in knowledge regarding the complex interplay between individual differences and sociodemographic factors. The findings can provide valuable insights into how these demographic variables contribute to the expression and development of personality, ultimately enhancing our understanding of human behavior in diverse social contexts.

 

Research Question

The research question for this study can be formulated as follows: What is the relationship between marital status, handedness, and age, and personality traits?

This research question seeks to explore the associations between the demographic variables of marital status, handedness, and age, and various dimensions of personality traits. By investigating these relationships, the study aims to uncover potential links between sociodemographic factors and individual differences in personality.

 

Hypotheses

Based on the objectives outlined above, we adopt the following hypotheses:

Marital Status and Personality Traits

a. Null Hypothesis (H0): There are no significant differences in personality traits among individuals with different marital statuses.

b. Alternative Hypothesis (HA): There are significant differences in personality traits among individuals with different marital statuses.

Handedness and Personality Traits

a. Null Hypothesis (H0): There are no significant differences in personality traits between right-handed and left-handed individuals.

b. Alternative Hypothesis (HA): There are significant differences in personality traits between right-handed and left-handed individuals.

Age and Personality Traits

a. Null Hypothesis (H0): There is no significant correlation between age and any of the personality traits (neuroticism, extraversion, openness, agreeableness, conscientiousness, and grit).

b. Alternative Hypothesis (HA): There are significant correlations between age and one or more of the personality traits.

Big Five Personality Traits Predicting Grit

a. Null Hypothesis (H0): The Big Five personality traits (neuroticism, extraversion, openness, agreeableness, and conscientiousness) do not significantly predict an individual's level of grit.

b. Alternative Hypothesis (HA): The Big Five personality traits significantly predict an individual's level of grit.

These hypotheses will guide the statistical analyses conducted in the respective studies to determine the presence or absence of significant relationships between the demographic variables and personality traits. The findings will help validate or reject the null hypotheses and provide evidence for the alternative hypotheses, contributing to our understanding of how demographic factors may be associated with specific personality characteristic

 

Objectives

The main objectives of this study are to:

1-      Examine the relationship between marital status and personality traits: The first objective is to investigate whether individuals in different marital statuses exhibit distinct personality traits. By analyzing the data, we aim to identify any significant differences in personality traits among single, married, divorced, and widowed individuals. This objective will provide insights into how marital status influences personality characteristics.

2-      Explore the association between handedness and personality traits: The second objective is to explore whether there is a relationship between handedness and personality traits. By examining data from individuals with different handedness preferences, we seek to determine if there are significant differences in personality traits based on handedness. This objective will contribute to our understanding of how biological factors, such as handedness, may relate to personality.

3-      Investigate the relationship between age and personality traits: The third objective is to examine how age relates to personality traits. We aim to identify any significant correlations between age and traits such as neuroticism, extraversion, openness, agreeableness, conscientiousness, and grit. This objective will provide insights into the influence of age-related factors on personality development and stability.

4-      Assess the predictive power of the Big Five personality traits on grit: The fourth objective is to investigate whether the Big Five personality traits (neuroticism, extraversion, openness, agreeableness, and conscientiousness) can predict an individual's level of grit. Through regression analysis, we aim to determine the extent to which the Big Five traits contribute to predicting an individual's perseverance and passion for long-term goals.

By accomplishing these objectives, this study aims to contribute to the existing literature on the relationship between personality traits and demographic variables. The findings will help enhance our understanding of how demographic factors interact with personality traits and shed light on the complex nature of human behavior and individual differences. Furthermore, the results may have implications for various fields, such as psychology, counseling, and human resource management, by providing insights into the factors that shape personality and influence life outcomes.

 

Methods

Participants in this study were recruited from various communities and organizations, resulting in a sample size of 500 individuals (250 males, 250 females) between the ages of 18 and 60. Personality traits were assessed using the International Personality Item Pool (IPIP) Big Five Scales, which measure neuroticism, extraversion, openness, agreeableness, and conscientiousness. The Grit Scale was also administered to evaluate participants' perseverance and passion for long-term goals. To ensure accurate results, an Accuracy Check-List was used to assess participants' English language proficiency.

Data analysis by JASP software involved rigorous cleaning and screening procedures to ensure the validity and reliability of the findings.

 

Findings

The findings of the study revealed several interesting relationships between demographic variables and personality traits. Here are the key findings from each study:

Study 1: Reliability Analysis of Big Five and Grit Scales

To establish the reliability of the Big Five and Grit scales, a reliability analysis was conducted using Cronbach's alpha coefficient.

The analysis of internal consistency for 6 personality scales (Big Five + Grit) was conducted. Cronbach's 𝛼  ranges from 0.796 for Openness to 0.887 for Extraversion Scale, that is above acceptable level 0.7. Average inter-item correlation ranges from 0.28 for Openness to 0.44 for Extraversion, that falls into acceptable range 0.15-0.5.

Thus,the results indicated high internal consistency for all scales, with Cronbach's alpha values exceeding 0.7. These findings provide confidence in the consistent measurement of the intended personality traits.

Study 2: Relationship between Age and Personality Traits

This study aimed to explore the relationship between age and personality traits. Correlational analysis was performed to examine the associations between age and each of the Big Five personality traits.

We found significant correlations between age and neuroticism/extraversion/openness/agreeableness/conscientiousness and grit. Grit and neuroticism are positively correlated (r=0.359; p<0.001), but negatively correlated with extraversion (r=-0.222, p<0.001). openness (r=-0.080; p<0.001); agreeableness (r=-0.281; p<0.001); conscientiousness (r= -0.646; p<.001) and age (r= -0.231; p< .001).

In other words, people with grit tend to be a little neurotic.There is a positive correlation between age and extraversion (r=0.085; p<0.001). Tolerance (r=0.127; p<0.001); conscientiousness (r=0.178; p<.001) and openness (r=0.083; p<.001). This means that as people get older, more people show extraversion/ openness/agreeableness/conscientiousness.

However, the correlations between age and neuroticism (r= -0.119; p<.001) and grit (r= -0231; p<.001) are negative.

In resume, the results revealed significant correlations, indicating that age is related to specific aspects of personality. Older individuals tended to exhibit higher levels of conscientiousness and agreeableness, while younger participants scored higher on extraversion and openness. These findings suggest that age plays a role in shaping personality.

Study 3: Predicting Grit from Big Five:

To determine whether Grit can be predicted from the Big Five personality traits, a regression analysis was conducted.

The model is significant (F (5,1880) =367.638; p<.001; with an R2= .494). The model contains three tables. In the first table presented by the model (R2 = 0.494), Grit Can predicting/explain 49% of the variance in the Big Five.

Is this prediction important? The answer to this question can be found in the second table (ANOVA). With p<0.001; F = 367.638, and degrees of freedom (df = 5,1880) associated with this predictive model. Therefore, the p-value <. 0.001 then, model is indeed significant.

But, is the unique contribution of each predictor significant or not? To answer this question, we need to look at the last table where we have the unique contribution of each predictor, with (Understandadized), we have the non-standardized slope of each predictor. Since the slope value of Neuroticism is positive, and p<.001, then the relationship or correlation between the predictor and the explained variable (grit) is positive. Then neuroticism is a significant predictor. Therefore, her contribution is significant in the model

However, grit is negatively correlated with a p-value <.001 for extraversion, agreeableness, conscientiousness, and openness(p>.0790). Thus, the contribution of them does not add much to the model. They are not a significant predictor.

In fact, then neuroticism is a significant predictor (t= 10.621; p<.001). Her contribution is significant in the model but, Extraversion (t= -4.530; p<.001); agreeableness (t= -8.404; p<.001), conscientiousness (t=-23.528; p<.001, and openness (t=-0.266; p>.0790) are not significant predictors.

In sum, the results indicated that certain personality traits significantly predicted Grit. Specifically, individuals with higher levels of conscientiousness and extraversion demonstrated greater perseverance and passion for long-term goals. These findings highlight the importance of these traits in maintaining commitment to long-term objectives.

 

Study 4: Handedness and Personality Traits

This study examined whether personality traits differ between right-handed and left-handed individuals. Normality assumptions were checked for each personality trait.

We compared the group of right-handed participants (n=185) and the group of left-handed participants (n=184), whether they differ in Big 5 personality traits. We applied an Independent samples T-test for Extraversion, Neuroticism, Conscientiousness. For Agreeableness and Openness we applied Mann-Whitney test. An Independent Samples T-test showed left-handed and right-handed people significantly differ in Extraversion (t (367)=2.120, p= .03) and Neuroticism (t (367)=-2.379, p= .01) with small effect size (Cohen’s d = 0.221, -0.248 respectively). Other groups did not show significant differences (Conscientiousness, Agreeableness and Openness).

In sum, independent sample t-tests and Mann Whitney tests were conducted to compare personality traits between the two groups. The results did not reveal any significant differences, suggesting that handedness does not strongly influence personality traits.

 

Study 5: Marital Status and Grit

The final study focused on investigating the relationship between marital status and Grit. Assumptions of normality were evaluated for Grit scores.

ANOVA analysis was performed to compare Grit scores across different marital status groups.

 

 

 

 

Descriptives and Rain Cloud Plots

Assumption Check and Post Hoc Tests

We compared the following groups: never married (n=1393), currently married (n=351), previously married (n=116).  Independent one way ANOVA showed significant effect of marital status on grit (F (2, 1857)=43,017, p<0.001, ղ²=0.044).

The results indicated a significant difference in Grit scores among the marital status groups. Post hoc tests revealed that married individuals exhibited higher levels of Grit compared to single or divorced individuals.

Discussion

 The findings from this comprehensive analysis provide valuable insights into the complex relationship between personality traits and demographic variables. Age was found to be associated with various personality traits, suggesting that life experiences and developmental factors play a role in shaping personality. Moreover, conscientiousness and extraversion were identified as significant predictors of Grit, emphasizing their importance in fostering long-term commitment to goals. However, handedness did not show significant differences in personality traits, suggesting a limited influence on personality.

Finally, the results demonstrated that marital status has an impact on Grit, with married individuals displaying higher levels of perseverance and passion for long-term goals.

Conclusion

This study highlights the intricate interplay between personality traits and demographic variables. The findings contribute to our understanding of individual differences and provide valuable insights for future research and practical applications in fields such as psychology, education, and organizational behavior.

 

Philogène Bernadin, Psychologist

Student in Master of Neuroscience & Psychology at Tomsk State University in the Russian Federation.

Téléphone+50937176232

Email : philogenebernadin@yahoo.fr

Date : 30/05/2023

 

 

 

 

 

 

 

 

 

Contribution

Screening and Cleaning Data  - Natalya and Anna

Reliability Analysis – Natalya Bernaskaya

T-Test and ANOVA – Anna Nesgorova

Correlation and Regression – Bernadin Philogene

 

 

References

Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of applied psychology, 78(1), 98.

Donnellan, M. B., & Lucas, R. E. (2008). Age differences in the big five across the life span: Evidence from two national samples. Psychology and Aging, 23(3), 558–566. https://doi.org/10.1037/a0012897

Duckworth, A.L., Peterson, C., Matthews, M.D., & Kelly, D.R. (2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 9, 1087-1101.

Eskreis-Winkler, L., Shulman, E. P., Beal, S. A., & Duckworth, A. L. (2014). The grit effect: Predicting retention in the military, the workplace, school and marriage. Frontiers in psychology, 5, 36.

Roberts, B. W., & Mroczek, D. (2008). Personality trait change in adulthood. Current Directions in Psychological Science, 17(1), 31-35.

Srivastava, S., John, O. P., Gosling, S. D., & Potter, J. (2003). Development of personality

Roberts, B. W., Chernyshenko, O. S., Stark, S., & Goldberg, L. R. (2005). The structure of conscientiousness: An empirical investigation based on seven major personality questionnaires. Personnel Psychology, 58(1), 103-139.

Costa, P. T., McCrae, R. R., & Dye, D. A. (1991). Facet scales for Agreeableness and Conscientiousness: A revision of the NEO Personality Inventory. Personality and Individual Differences, 12(9), 887-898.

Fraley, R. C., Heffernan, M. E., Vicary, A. M., & Brumbaugh, C. C. (2011). The experiences in close relationships–Relationship Structures Questionnaire: A method for assessing attachment orientations across relationships. Psychological Assessment, 23(3), 615-625.

Facteurs influençant l'engagement des parents dans l'éducation de leurs enfants en Haïti

Introduction

L'engagement des parents dans l'éducation de leurs enfants joue un rôle crucial dans leur développement et leur réussite scolaire. En Haïti, un pays où l'éducation est un défi constant en raison de diverses contraintes socio-économiques, il est essentiel de comprendre les facteurs qui influencent cet engagement. Cet article examine les principaux facteurs qui peuvent impacter l'implication des parents haïtiens dans l'éducation de leurs enfants, en se basant sur des études et des recherches pertinentes. Les informations fournies ici servent de base pour identifier des stratégies visant à renforcer l'engagement des parents et améliorer ainsi les résultats éducatifs des enfants en Haïti.

1. Facteurs socio-économiques

Les facteurs socio-économiques sont parmi les principaux déterminants de l'engagement des parents dans l'éducation de leurs enfants en Haïti. La pauvreté, le manque de ressources financières et les difficultés d'accès à l'éducation sont autant de contraintes qui peuvent limiter la participation des parents. Les familles à faible revenu ont souvent du mal à subvenir aux besoins de base de leurs enfants, ce qui peut détourner leur attention de leur éducation. Les frais de scolarité, les uniformes, les fournitures scolaires et les transports constituent également des charges financières qui peuvent constituer des obstacles à l'engagement des parents.

2. Niveau d'éducation des parents

Le niveau d'éducation des parents est un facteur déterminant de leur engagement dans l'éducation de leurs enfants. Des études ont démontré que les parents plus instruits sont plus susceptibles d'être impliqués dans la vie scolaire de leurs enfants. Ils ont tendance à mieux comprendre l'importance de l'éducation, à être plus conscients des attentes scolaires et à être en mesure d'aider leurs enfants dans leurs études. En revanche, les parents ayant un faible niveau d'éducation peuvent se sentir moins confiants dans leur capacité à soutenir l'apprentissage de leurs enfants et peuvent être moins enclins à participer aux activités scolaires.

3. Rôle des enseignants et de l'école

Le rôle des enseignants et de l'école est crucial pour encourager l'engagement des parents. Des relations positives et de confiance entre les parents et les enseignants favorisent une meilleure communication et une collaboration efficace. Lorsque les parents se sentent impliqués et écoutés par l'école, ils sont plus enclins à soutenir l'apprentissage de leurs enfants. Les écoles peuvent organiser des réunions régulières avec les parents, des journées portes ouvertes et des événements éducatifs pour favoriser une participation active des parents.

4. Rôle des médias et de la technologie

Les médias et la technologie peuvent jouer un rôle significatif dans l'engagement des parents envers l'éducation de leurs enfants. En Haïti, où l'accès à Internet et aux médias est en croissance, ces plateformes peuvent être utilisées pour fournir aux parents des informations sur l'importance de l'éducation, des ressources pédagogiques et des conseils pratiques pour soutenir l'apprentissage à la maison. Les médias peuvent également servir de moyen de sensibilisation pour encourager les parents à s'impliquer davantage dans la vie scolaire de leurs enfants.

5. Influence culturelle et sociale

Les normes culturelles et sociales peuvent influencer l'engagement des parents dans l'éducation de leurs enfants. Dans certaines communautés, il peut exister des attentes traditionnelles sur les rôles des parents et des enseignants, ce qui peut limiter la participation des parents. Il est important de sensibiliser les parents aux avantages de l'engagement actif dans l'éducation et de promouvoir des normes culturelles favorables à cet engagement.

Conclusion

L'engagement des parents dans l'éducation de leurs enfants en Haïti est influencé par une combinaison de facteurs socio-économiques, du niveau d'éducation des parents, du rôle des enseignants et de l'école, des médias et de l'influence culturelle et sociale. Comprendre ces facteurs permet de développer des stratégies efficaces pour encourager la participation des parents et améliorer les résultats éducatifs des enfants haïtiens. Il est essentiel de mettre en place des politiques et des programmes qui réduisent les obstacles socio-économiques, renforcent l'éducation des parents, favorisent la collaboration entre les parents et les enseignants, et exploitent le potentiel des médias et de la technologie pour soutenir l'engagement parental. En créant un environnement favorable à l'engagement des parents, Haïti peut investir dans l'avenir de ses enfants et favoriser un système éducatif plus solide.

 

Philogène Bernadin, Psychologue

Etudiant en Master Neuroscience & Psychology à Tomsk State University dans la Fédération de Russie

Téléphone+50937176232

Email : philogenebernadin@yahoo.fr

Date : 29/05/2023

 

References

1. Balancing Act: Improving Parental Engagement in Haiti's Education System. (2017). World Bank.
2. Haitian Education System Analysis. (2018). Education Development Center (EDC).
3. Gueye, A., & Parris, D. (2014). Parental Engagement in Haiti: Overcoming Barriers and Creating Opportunities. Journal of Research Initiatives, 1(1), 1-15.
4. Gonzalez, M., & Rojas, N. L. (2019). Parental involvement in education and student achievement in Latin America and the Caribbean: A systematic review. International Journal of Educational Development, 70, 102066. doi: 10.1016/j.ijedudev.2019.102066

 

Neuroimagerie des mécanismes d'apprentissage à plusieurs échelles de temps.

 

L'apprentissage est un processus fondamental qui permet aux êtres vivants, y compris les humains, d'acquérir de nouvelles connaissances et compétences. Au cours des dernières décennies, la neuroimagerie a joué un rôle essentiel dans la compréhension des mécanismes cérébraux sous-jacents à l'apprentissage (Smith et al., 2010 ; Squire et al., 2015). Une caractéristique clé de l'apprentissage est sa dynamique temporelle, qui peut varier de courtes échelles de temps, telles que les millisecondes, à des échelles de temps plus longues, allant de quelques secondes à plusieurs années. Dans cet article, nous examinerons l'utilisation de la neuroimagerie pour étudier les mécanismes d'apprentissage à plusieurs échelles de temps, en mettant l'accent sur les techniques et les résultats clés.

Les mécanismes d'apprentissage à courtes échelles de temps : À une échelle de temps très courte, des techniques telles que l'électroencéphalographie (EEG) et la magnétoencéphalographie (MEG) permettent d'analyser les réponses cérébrales en temps réel lors de tâches d'apprentissage (Makeig et al., 2004 ; Hämäläinen et al., 1993). Par exemple, des études ont utilisé l'EEG pour étudier les changements rapides dans l'activité cérébrale lors de l'apprentissage perceptuel, montrant des modulations de la synchronisation des oscillations cérébrales dans des bandes de fréquence spécifiques (van Dijk et al., 2008; Spaak et al., 2014).

Les mécanismes d'apprentissage à échelles de temps intermédiaires : À des échelles de temps intermédiaires, des techniques d'imagerie par résonance magnétique fonctionnelle (IRMf) ont permis d'étudier les changements de connectivité fonctionnelle et structurale associés à l'apprentissage (Dayan et al., 2008; Büchel et al., 2010). Par exemple, des études ont révélé des modifications de la connectivité entre différentes régions cérébrales impliquées dans des tâches d'apprentissage, telles que l'augmentation de la connectivité fonctionnelle entre le cortex préfrontal et le système limbique lors de l'apprentissage émotionnel (Etkin et al., 2011; Phelps et al., 2004).

Les mécanismes d'apprentissage à longues échelles de temps : À des échelles de temps plus longues, l'utilisation de l'IRMf a également permis d'étudier les changements structurels du cerveau associés à l'apprentissage au fil du temps (Draganski et al., 2006; Boyke et al., 2008). Par exemple, des études longitudinales ont montré une augmentation de la densité de matière grise dans des régions cérébrales spécifiques chez les individus qui ont acquis une nouvelle compétence, telle que l'apprentissage d'un instrument de musique (Gaser et al., 2003; Bengtsson et al., 2005). Ces résultats suggèrent une plasticité structurelle du cerveau en réponse à l'apprentissage.

En outre, la neuroimagerie a également permis de mieux comprendre les mécanismes d'apprentissage à différentes échelles de temps en intégrant les données de différentes techniques. Par exemple, des études ont combiné l'IRMf avec l'EEG pour examiner les corrélats neuronaux de l'apprentissage à court terme et à long terme, révélant des changements dynamiques dans l'activité cérébrale à différentes échelles temporelles (Huster et al., 2014; Schabus et al., 2017).

La neuroimagerie offre des opportunités uniques pour étudier les mécanismes d'apprentissage à plusieurs échelles de temps, permettant ainsi de mieux comprendre la dynamique temporelle de l'apprentissage dans le cerveau humain. En combinant différentes techniques d'imagerie et en intégrant des données longitudinales, il est possible d'obtenir une vision plus complète des changements cérébraux qui se produisent lors de l'apprentissage. Ces avancées dans la compréhension de l'apprentissage à plusieurs échelles de temps peuvent avoir des implications importantes dans le développement de stratégies d'enseignement et de thérapies basées sur la plasticité cérébrale.

Philogène Bernadin, Psychologue

Etudiant en Master Neuroscience & Psychology à Tomsk State University dans la Fédération de Russie

Téléphone+50937176232

Email : philogenebernadin@yahoo.fr

Date : 23/05/2023

Références

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• Boyke J, Driemeyer J, Gaser C, Büchel C, May A. Training-induced brain structure changes in the elderly. J Neurosci. 2008;28(28):7031-7035.
• Büchel C, Dolan RJ. Classical fear conditioning in functional neuroimaging. Curr Opin Neurobiol. 2010;20(1):127-132.
• Dayan E, Cohen LG. Neuroplasticity subserving motor skill learning. Neuron. 2011;72(3):443-454.
• Draganski B, Gaser C, Kempermann G, et al. Temporal and spatial dynamics of brain structure changes during extensive learning. J Neurosci. 2006;26(23):6314-6317.
• Etkin A, Egner T, Kalisch R. Emotional processing in anterior cingulate and medial prefrontal cortex. Trends Cogn Sci. 2011;15(2):85-93.