To what extent does perceived social support interact with individual differences in Big Five Personality traits (BFPT) to predict levels of life sa

Research Question

To what extent does perceived social support interact with individual differences in Big Five Personality traits (BFPT) to predict levels of life satisfaction?

Rationale:

Prior studies indicate that personality characteristics, namely the BFPT dimensions, substantially impact people's happiness (Malouff et al., 2010). However, more research needs to be conducted on whether perceived social support influences the relationship between personality traits and life satisfaction. Empirical research has demonstrated that social support increases good outcomes and mitigates the detrimental effects of personality characteristics on wellbeing. Bolger et al. (2007) discovered that the negative impacts of stress on psychological wellbeing are alleviated by perceived social support. Comprehending how personality qualities and perceived social support combine to impact life happiness is essential for crafting focused therapies that enhance overall health (Keyes et al., 2002). By exploring the complex interactions between personality, social support, and wellbeing, this study seeks to close a gap in the literature and further our knowledge of the variables affecting life satisfaction.

Hypothesis

The relationship between the BFPT and life satisfaction is moderated by perceived social support. Certain personality traits will show positive correlations with life satisfaction. Specifically, we hypothesize that perceived social support will strengthen the positive associations between certain personality traits and life satisfaction. Individuals with higher levels of perceived social support are expected to show stronger correlations between specific personality traits and life satisfaction than those with lower levels of perceived social support.

Materials

We evaluated several psychological dimensions in this study. Diener et al. (1985) report an article on the Satisfaction with Life Scale (SWLS), which assesses overall life satisfaction with a solid internal consistency ( = 0.87). Perceived social support (was) is measured with the Multidimensional Scale of Perceived Social Support (MSPSS) constructed by Zimet et al. (1988). It shows reliability ( = 0.89). The personality characteristics assessed by John et al. (1999) using the Big Five Inventory (BFI) consisting of Neuroticism ( = 0.85), extraversion ( = 0.79), agreeableness ( = 0.82), openness ( = 0.77), and conscientiousness ( = 0.88). All the internal consistency values are excellent. The Mental Health Continuum-Short Form (MHC-SF) evaluates the Wellbeing measurement. Keyes (2002) develops the MHC-SF, exhibiting superior reliability ( = 0.90). Lastly, the Positive and Negative Affect Schedule (PANAS) established by Watson et al. (1988) measures the affectivity with good reliability for both Positive Affect ( = 0.88) and Negative Affect ( = 0.86). These measurements are based on their reliability, validity, and relevance. They provide robust assessments of the psychological constructs under investigation.

Rationale for Statistical Analysis:

The hypothesis will be tested using hierarchical multiple regression analysis. In this technique, perceived social support and the BFPts are the independent variables, while life satisfaction is the dependent variable. Hierarchical regression allows for evaluating the unique contribution of personality traits and perceived social support in addressing the combined influence on life satisfaction. It allows the separation of social support's distinct contribution to life happiness, allowing for the effect of personality factors by first including Big Five personality qualities and then perceived social support. The goal of comprehension is to understand how these variables work together to affect life's happiness.

Results

Table 1

Hierarchical Multiple Regression Analysis Summary

Steps and Predictor variables B SE B Beta Sr Change in R2 R2 p Sr2

Step 1 Constant 18.43 4.17 -38 .17 .15 Neurototal-0.28 .05 -0.4 -.37 <.001 .13

ETVAtotal-.08 .09 -.05 -.05 .420 .0023

AGV total -.09 .09 -.07 -.07 .280 .0041

OPNtotal.17 .08 .14 .15 .021 .02

CNStotal.20 .08 .15 .15 .018 .02

Step 2 Constant 8.46 4.06 .14 .27 Neurototal-.18 .04 -.25 -.26 <.001 .05

ETVAtotal-.07 .08 -.05 .06 .379 .42

AGBtotal-.05 .08 -.03 -.04 .553 .00

OPNtotal.16 .07 .14 .16 .018 .02

CNStotal.12 .08 .09 .10 .112 .01

PSLtotal.08 .023 .40 .42 <.001 .12

Notes: sr semipartial correlation coefficient; Neuro total neuroticism, ETVA total extraversion, AGB total agreeableness, OPN total openness, CNS total conscientiousness, PSL total perceived life satisfaction

A hierarchical multiple regression was conducted to investigate the Big Five personality traits and perceived social support on life satisfaction among participants in the online survey. We aimed to assess whether perceived social support significantly predicts life satisfaction beyond the contribution of Big Five personality traits. In the initial step of hierarchical regression, we included each of the BFPT(Neuroticism, Extraversion, Openness, Conscientiousness, and Agreeableness)wereentered as separate predictors. This model was statistically significant,F(5,234) = 9.65,p<.001, and explained 17.1% of the variance in life satisfaction. Subsequently, we added perceived social support as an additional predictor in the second step. The total variance explained by the model was 31.4%, showing statistical significance,F(6,233) = 48.63,p<.001. Regarding Cohen's (1977) standards, an effect off2= 0.21 can be noted as a medium for the combined model. This indicates that the predictors in the model explain a moderate amount of variability in the outcome variable. The introduction of perceived social support as a support-related variable explained an additional 14.3% variance in life satisfaction, indicating its significant contribution beyond the Big Five personality traits (R2Change = .143;F(1, 233) = 48.6,p<.001), with regards to Cohens (1977) an effect of f2= .46 indicating a large effect size. Three out of six predictor variables emerged as statistically significant in the final model. Perceived social support demonstrated a significant predictive power (= .40,p= <.001,sr2= .14), indicating a positive relationship with life satisfaction. The second highest significant predictor was Neuroticism (= -.25,p<.001,sr2= .05), indicating a negative association with life satisfaction.Thisis followed by openness presenting significance (= .14,p= 0.018,sr2= .02), suggesting a positive influence on life satisfaction. Variance inflation factors were used to measure multicollinearity (VIFs). The predictor variables did not exhibit significant multicollinearity, as indicated by the VIF values, which varied from 1.08 to 1.23. (This) These values strengthen the validity of our analysis's conclusions and show that the regression coefficients are trustworthy. The histogram of residuals indicates a normal distribution, and examination of scatterplots suggests that the assumption of linearity is satisfied.

In this study, participants in an online survey were asked to rate their life happiness, perceived social support, and personality attributes. Our main goal was to find out if, even after accounting for the impact of individual personality characteristics, perceived social support is a significant predictor of life happiness.

The outcomes of our hierarchical multiple regression study strongly support our theory. Initially, we discovered that this model was statistically significant and explained a considerable percentage (17.1%) of the variation in life satisfaction when we input each Big Five personality characteristics (Neuroticism, extraversion, openness, conscientiousness, and agreeableness) as independent predictors.

Perceived social support as an additional predictor in the second step significantly enhanced the predictive power of our model. This stepwise inclusion returned a substantial increase in the total variance explained by the model, which provides evidence that perceived social support influences life satisfaction outcomes.

The practical relevance of our findings was highlighted by the effect size, as shown by Cohen's f. Our results indicate that the variables in our model explain a significant percentage of the variability in life satisfaction, with an effect size off= .21, indicating a modest influence.

Disseminate Statistical Results

The specific contribution of perceived social support to the model is particularly noteworthy. Its inclusion explains(ed) an additional 14.3% of the variance in life satisfaction, signifying its significant contribution beyond the influence of the Big Five personality traits. The crucial role of social support networks in promoting wellbeing is independent of individual personality characteristics. Personality traits, such as Neuroticism and openness, were significantly correlated with life satisfaction. Life satisfaction and Neuroticism were shown to be negatively correlated, suggesting that those with higher levels of Neuroticism may not be as content with their lives. Conversely, openness had a favourable effect, indicating that those who are receptive to new experiences may lead happier lives. It is important to recognise our study's limitations. These include using self-report measures, which might be biased, and the cross-sectional design, which makes it more challenging to determine the cause of an event.

References

Bolger, N., & Amarel, D. (2007). Effects of social support visibility on adjustment to stress: Experimental evidence. Journal of Personality and Social Psychology, 92(3), 458475. https://doi.org/10.1037/0022-3514.92.3.458

Cohen, J. (1977). Statistical power analysis for the behavioral sciences (Revised edition.). Academic Press.

Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The satisfaction with life scale. Journal of Personality Assessment, 49(1), 7175. https://doi.org/10.1207/s15327752jpa4901_13

Keyes, C. L. (2002). The Mental Health Continuum: From languishing to flourishing in life. Journal of Health and Social Behavior, 43(2), 207. https://doi.org/10.2307/3090197

Keyes, C. L., Shmotkin, D., & Ryff, C. D. (2002). Optimizing well-being: The empirical encounter of two traditions. Journal of Personality and Social Psychology, 82(6), 10071022. https://doi.org/10.1037/0022-3514.82.6.1007

Malouff, J. M., Thorsteinsson, E. B., Schutte, N. S., Bhullar, N., & Rooke, S. E. (2010). The five-factor model of personality and relationship satisfaction of intimate partners: A meta-analysis. Journal of Research in Personality, 44(1), 124127. https://doi.org/10.1016/j.jrp.2009.09.004

John, O. P., & Srivastava, S. (1999). The Big-Five trait taxonomy: History,

measurement, and theoretical perspectives. In L. A. Pervin & O. P. John (Eds.),

Handbook of personality: Theory and research (Vol. 2, pp. 102138). Guilford

Press

Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The panas scales. Journal of Personality and Social Psychology, 54(6), 10631070. https://doi.org/10.1037/0022-3514.54.6.1063

Zimet, G. D., Dahlem, N. W., Zimet, S. G., & Farley, G. K. (1988). The multidimensional scale of perceived social support. Journal of Personality Assessment, 52(1), 3041. https://doi.org/10.1207/s15327752jpa5201_2

Appendices

Criterion Variable

SOLtotal Satisfaction of Life

Predictor Variables

PSLtotal Perceived Social Support

Big Five Personality Traits which include:

ETVAtotal Extraversion

OPNtotal Openness

CNStotal Conscientiousness

AGBtotal Agreeableness

Neurototal Neuroticism

Calculations for sr2

Step 1

Neuro total sr2 = -.360^2 = 0.1296

ETVA total sr2 = -.048^2 = 0.0023

AGB total sr2 = -.064^2 = 0.0041

OPN total sr2 = .138^2 = 0.0190

CNS total sr2 = .142^2 = 0.0202

Step 2

Neuro total sr2 = -.224^2 = 0.0502

ETVA total sr2 = -.048^2 = 0.0023

AGB total sr2 = -.032^2 = 0.0010

OPN total sr2 = .130^2 = 0.0169

CNS total sr2 = .087^2 = 0.0076

PSL total sr2 = .378^2 = 0.1429

Cohens f2 for multiple regression

Rsquared for model one = .171

Effect size model one = .206 = .21

SPSS Output