A Pearson's 'r' correlation compares the relationships between two variables. Common examples include height and weight, IQ and Test Score, and Strength and Speed.
This test may also be referred to as the Pearson's product moment correlation coefficient.
It is considered a parametric test and therefore is only suitable for parametric data. To check if your data is parametric, please check out the dedicated guide: Parametric or Not Guide (PDF)
If either one of the variables that you would like to test is non-parametric you could consider the Spearman's 'rho' Correlation
1. To carry out a correlation, the two columns of data that you are correlating against each other need to be identified. These will form a variable each:
2. Correlations are housed under the Regression options in Jamovi. Select "Regression", drop down the menu and select "correlation matrix".
3.The Correlation Matrix options will open. Using the arrow button, move the variables you would like to test across into the right hand box. Look through the options in this view for additional information to the default that you may want. Here we have selected "Flag significant correlations" and the "N".
Jamovi will produce just one table with all the results in, this is the Correlation Matrix table:
This table shows the statistics needed when reporting a Pearson's 'r' correlation; the Correlation Coefficient (Pearson's r), the significance/ p-value (p-value), the degrees of freedom (df) and the sample size (N). It is usual to report either the N or the df, not usually both.
Alongside interpreting the p-value for significance, you will also need to interpret the strength and direction of the relationship. To do this you would use a correlation strength table. We have an example of this in the PDF below, however, we recommend using a table from a textbook (check either your reading list or the book recommendations we have on our site).
The relationship between an ostrich's height and speed was assessed. A Pearson's Correlation indicated a moderately strong, positive, significant relationship, r = .65, p < .001, N = 174.
Pearsons's 'r' Correlation may also be reported with the degrees of freedom rather than the sample size. The degrees of freedom are reported in brackets such as the example below:
The relationship between an ostrich's height and speed was assessed. A Pearson's Correlation indicated a moderately strong, positive, significant relationship, r (172) = .65, p < .001.
