Regression Analysis of Life Satisfaction, Social Support, and Coping: Correlation and Predictive Modeling
Overview
This paper presents a correlation and regression analysis examining the relationships between life satisfaction, social support, and coping mechanisms in a sample of 24 participants. The study develops a predictive regression equation demonstrating that social support is a significant predictor of life satisfaction, while also discussing broader applications of regression methodology in healthcare research outcomes.
What this research paper covers
- Correlation analysis presenting Pearson correlation coefficients between three variables with significance testing
- Regression analysis deriving an equation predicting life satisfaction from social support and coping variables
- Discussion of healthcare spending and health outcomes research using meta-regression analysis techniques
Preview
Regression
The first step is undertaking a correlation analysis of the data, which can be seen as below. From the data, we observed that
The Pearson correlation coefficient between life satisfaction and social support was 0.678, that of social support and coping is 0.556, and that of life support and coping is 0.430.
Correlations | ||||
Life satisfaction | Social support | Coping | ||
Life satisfaction | Pearson Correlation | 1 | .678** | .430* |
Sig. (2-tailed) | .000 | .036 | ||
N | 24 | 24 | 24 | |
Social support | Pearson Correlation | .678** | 1 | .556** |
Sig. (2-tailed) | .000 | .005 | ||
N | 24 | 24 | 24 | |
Coping | Pearson Correlation | .430* | .556** | 1 |
Sig. (2-tailed) | .036 | .005 | ||
N | 24 | 24 | 24 | |
**. Correlation is significant at the 0.01 level (2-tailed). | ||||
*. Correlation is significant at the 0.05 level (2-tailed). | ||||
We will now undertake a regression analysis to understand how the coefficients are related. The document below shows the coefficients of the regression equation.
We can see that:
Life satisfaction = 7.146 + 1.172 (Social Support) + 0.188(Coping)
Coefficientsa | ||||||
Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
B | Std. Error | Beta | ||||
1 | (Constant) | 7.146 | 5.272 | 1.356 | .190 | |
Social support | 1.172 | .355 | .635 | 3.299 | .003 | |
Coping | .188 | .466 | .078 | .403 | .691 | |
a. Dependent Variable: Life satisfaction | ||||||
Discussion
Gallet, C. A., & Doucouliagos, H. (2017). The impact of healthcare spending on health outcomes: A meta-regression analysis. Social Science & Medicine, 179, 9-17.
In this study, there are numerous studies that assess the impact of healthcare spending on health outcomes. These outcomes are measured based on the mortality rates or life expectancy based on the healthcare spending. There is lack of clarity concerning the influence of healthcare spending to healthcare outcomes. In this study, the researchers undertook a regression analysis in order to establish what were the major forces of influence. Based on the analysis, it was found that the spending elasticity for mortality is -0.13 and the spending elasticity for life expectancy is +0.04.