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Pearson Correlation and Multiple Regressions

Both the Pearson correlation coefficient and Regression analysis are good tests for relationship between variables. The Pearson correlation coefficient is defined as a measure of the directions and strength of the linear correlation between two variables. The coefficient generally describes the direction and degree to which a variable may be related to another variable (Siegel & Castellan, 2008). The coefficient of determination (r squared) provides information regarding the proportion of variation in a dependent variable that may be associated with variation in the independent variable. The test is generally applied to statistical variables that are at least in interval or ratio scale and their distribution in normal bivariate (Siegel & Castellan, 2008).
In terms of interpretation, the Pearson correlation assumes values of between -1 and +1. A value of 1 indicates that perfect linear correlation between variables increases by increasing the relationship; on the other hand, a value of -1 indicates that the variables are perfectly related by decreasing relationship. A value of 0 indicates the lack of a linear relationship between variables. Generally, the correlation between the variables is considered to be strong when the coefficient is greater than 0.8 and weak when the coefficient measures less than 0.5 (Rogers & Hopfinger, 2009).
All the three research questions in this study seek to find out if there is a statistically significant relationship between the dependent variable and the independent variables that define it. Pearson correlation coefficient is the most suitable method for this as it provides a good opportunity to measure the relationship, particularly since the data in this study will be bivariate and will be organized in interval and ratio scale. The significance of the relationship can be measured at the 99%, 95%, or 90% levels of confidence depending on the hypotheses of the study.
While the correlation analysis is useful in determining if there are statistically significant relationships between two variables, linear regression is another statistical analysis method that can be used in making predictions based on the relationship that exists between the two variables (Anderson et al., 2010). Multiple regression is defined by Jiantschi (2008) as a statistical tool that allows the examination of how multiple independent variables are related to the dependent variable. It is a good tool to use in combination with Pearson correlation analysis as it enables further measurement of relationships than correlation can achieve. Once it has been determined how the multiple variables relate to the dependent variable, the information generated through multiple regression about each of the independent variables can then be used to make accurate predictions regarding why relationships are the way they are (Jianschi, 2008).
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