I need help testing the assumptions of data collected using a Pearson Correlation Coefficient and a Post hoc analysis. ?? The five data assumptions required for a Pearson R correlatio

 I need help testing the assumptions of data collected using a Pearson Correlation Coefficient and a Post hoc analysis.

The five data assumptions required for a Pearson R correlation are as follow:

1. Level of measurement on a continuous scale (i.e., the interval or ratio level),

2. The variables that are continuous should be paired

3. Linear relationship between the two variables

4. There are no significant outliers

5. Normally distributed must be bivariate normality

The researchers plan for inferential analysis for all the research questions will use a correlational analysis using the Pearson’s r statistic. Using the Pearson’s r requires meeting all 5 data assumptions. Once the data is collected the researcher will see if the first two assumptions are met. A correlation analysis is used to aid in the understanding of the relationships between the interval, ordinal, and ration variables (Salkind, 2011). The next assumption will be tested using a scatterplot of the data collected to ensure the data is linear. The following assumption will be addressed during preparation and data clean up to make sure there are no outliers once scatterplot is conducted. This will allow the researcher to start the analysis once everything is checked for no missing data and in correct format (Chu et al., 2016). Lastly, the final assumption will be tested using the Shapiro-Wilk test of normality. The data must be compatible to use the Pearson correlation coefficient and have valid results that pass the assumptions (Laerd Statistics, 2018).