Pearson Correlation Analysis
Pearson correlation analysis is a statistical method used to quantify the strength and direction of the linear relationship between two continuous variables. It assesses how much one variable changes when the other variable changes and provides a measure of the extent to which the variables are associated.
When to Use Pearson Correlation Analysis:
Pearson correlation analysis is appropriate when:
There is an interest in examining the linear relationship between two continuous variables.
The relationship between the variables is expected to be linear.
The variables are measured on an interval or ratio scale.
Assumptions and Data Requirements:
Before conducting Pearson correlation analysis, several assumptions must be met:
Linearity: The relationship between the variables should be linear.
Normality: The variables should be approximately normally distributed.
Homoscedasticity: The variances of the variables should be approximately equal across different levels.
Additionally, the data required for Pearson correlation analysis should consist of continuous variables obtained from a random sample.
Interpreting Pearson's r:
Pearson's correlation coefficient (r) ranges from -1 to 1.
A correlation coefficient of 1 indicates a perfect positive linear relationship, while a correlation coefficient of -1 indicates a perfect negative linear relationship.
A correlation coefficient of 0 indicates no linear relationship between the variables.
The closer the correlation coefficient is to 1 or -1, the stronger the linear relationship between the variables. Conversely, values closer to 0 indicate a weaker linear relationship.
Sample Situation with Sample Data:
Suppose a researcher wants to examine the relationship between the number of hours students spend studying per week and their exam scores. The researcher collects data on study hours and exam scores for a sample of students.