Pearson correlation coefficient
Normally, measurements of how two variables which are continuous are relating including by nature and strength of the association are done through Pearson’s correlation statistics. On this note, a scatter plot of the two variables is generated to check for the linearity of the variables (Hazra, & Gogtay, 2016) as illustrated below.
It is important to note that in the existence of a linear relationship, the correlation coefficient is defined which normally ranges between +1 to -1. Also, the dependent variables are always plotted on the y-axis whereas the explanatory or independent variables are always plotted on the x-axis. According to (Zeitlin, & Auerbach, 2019), the variables with higher strength of association occur where the scatter points congregate near the straight line. In addition, negative correlation normally takes place where one variable increases as another variable decrease while positive correlation takes place when the variables are either decreasing or increasing altogether, (Rohrer, 2018).
Article review
Generally, the concept of Pearson’s r has been utilized in this study as follows.
Correlation of Quality of Life-related to Health and Physical Activity in Greek Patients with Chronic Diseases
As mentioned, the Pearson correlation (r) is a type of statistical analysis that helps to measure the strength of association between two continuous variables. The article by Maria et al. (2015) investigated the relationship between quality of Life and Physical Activity in Greek Patients with Chronic Diseases.
The population targeted by the researchers was 68 women and 65 men with renal disease, diabetes and beta-thalassemia. Thus, the sample accounted for 133 respondents. In addition, a cross-sectional, correlation study designs were used by the researchers while investigating the assessment of quality of life related to health (QLrH) as far as their level of physical activity is activity. Moreover, a statistical package for social sciences, SPSS version 18 was used for analysis.
Results in the table below show that Men had significantly higher marks on the synoptic-scale of physical health than their women counterparts. Thus, show better physical health among men compared to women. Moreover, results indicate a statistically significant difference in the marks that the participants achieved while using a synoptic-scale of physical health. To note, the variation is based on demographic characteristics including education level where both high school graduates and the graduates of Universities were included. In addition, very minimal marks were achieved by the men participants subjected to the synoptic scale of mental health.
| Correlations | |||
| Quality of Life | Physical Activity | ||
| Quality of Life | Pearson Correlation | 1 | .866** |
| Sig. (2-tailed) | .001 | ||
| N | 10 | 10 | |
| Physical Activity | Pearson Correlation | .866** | 1 |
| Sig. (2-tailed) | .001 | ||
| N | 133 | 10 | |
| **. Correlation is significant at the 0.01 level (2-tailed). |
In conclusion, better living conditions and quality of health services are determinants of human health associated with psychosocial factors and chronic diseases in modern way lifestyle.
References
Hazra, A., & Gogtay, N. (2016). Biostatistics series module 6: correlation and linear regression. Indian journal of dermatology, 61(6), 593.
Maria, C., Pantelis, K., Ioannis, T., Kyriakoula, M., & Styliani, T. G. Correlation of Quality of Life related to Health and Physical Activity in Greek Patients with Chronic Diseases.
Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science, 1(1), 27-42.
Zeitlin, W., & Auerbach, C. (2019). Basic statistics for the behavioral and social sciences using R. Oxford University Press.
