Reading your Results Report - Correlations

Interpreting the results of the Correlate function

The results report for your correlation contains a few pieces of information that are critical to interpreting your results report for the Correlate function. There are two different kinds of results reports, depending on whether or not you chose to create a correlation matrix of multiple variables, or a a single correlation coefficient with a scatterplot graph.

Results Report for Correlating Two Variables with a Scatterplot

First, the top row of your results report tells you which two variables were used in the correlation analysis - it is reported as Correlation of (Variable name 1) and (Variable name 2).

You will also see three statistics reported - the Pearson Correlation coefficient, the N (number of valid pairs in your data), and the P values (significance level reached).

Next, take a look at the table that EZAnalyze has created - you will see that the variable names that you correlated are along both the top of the table (row 3) and along the side of the table (column B). To find the correlation coefficient (Glossary), find the number located at the intersection of row 7 and column D next to the words Pearson Correlation. The N in the results report indicates how many cases were used in computing the correlation, and the P is the significance level.

You will also see a scatterplot showing the relationship between your two variables, and a trendline (line of best fit) if you selected that option.

Correlation Matrix Results Report

The correlation matrix results report provides the Pearson Correlation coefficient, number of valid pairs of data (N), and the significance level (P) of all possible correlations between the variables you selected.


1. The larger the correlation coefficient, the better.

2. A correlation of less than .3 is typically considered "not meaningful," a correlation between .3 and .5 is typically considered "weak," a correlation between .5 and .7 is typically considered "moderate," and a correlation that is greater than .7 is typically considered to be a strong correlation.

3. CORRELATION DOES NOT IMPLY CAUSATION. Because two variables are correlated, even if they are statistically significant, does not mean that one variable causes changes in the other - it means nothing more than "they are related."

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