ANALYSE-IT 2.20 > USER GUIDE
You are viewing documentation for the old version 2.20 of Analyse-it. If you are using version 3.00 or later we recommend you go to the Pearson correlation
This procedure is available in both the Analyse-it Standard and the Analyse-it Method Evaluation edition
Pearson correlation is a test to determine the degree of correlation (association) between two variables.
The requirements of the test are:
- Two variables measured on a continuous scale.
- Variables are from a population with a bivariate normal distribution.
Arranging the dataset
Data in existing Excel worksheets can be used and should be arranged in a List dataset layout. The dataset must contain two continuous scale variables.
When entering new data we recommend using New Dataset to create a new 2 variables dataset ready for data entry.
Using the test
To start the test:
- Excel 2007:
Select any cell in the range containing the dataset to analyse, then click Correlation on the Analyse-it tab, then click Pearson.
Excel 97, 2000, 2002 & 2003:
Select any cell in the range containing the dataset to analyse, then click Analyse on the Analyse-it toolbar, click Correlation then click Pearson.
- Click Variable X and Variable Y and select the variables.
- Click Alternative hypothesis and select the alternative hypothesis to test.
|r ≠ 0 to test if the variables are correlated.
|r > 0 to test if the variables are positively correlated, where observations of the variables tend to increase together.
|r < 0 to test if the variables are negatively correlated, where observations of one variable tend to increase as observations in the other variable decrease.
- Enter Confidence interval to calculate around the Pearson r statistic. The level should be entered as a percentage between 50 and 100, without the % sign.
- Click OK to run the test.
The report shows the number of observations analysed, and, if applicable, how many missing cases were pairwise deleted.
The Pearson r correlation statistic and confidence interval are shown.
METHOD The confidence interval is calculated using the Fisher's Normal transformation (see  or ).
The hypothesis test is shown. The p-value is the probability of rejecting the null hypothesis, that the variables are independent, when it is in fact true. A significant p-value implies that the two variables are correlated.
METHOD The p-value is calculated using the t approximation (see ).
The scatter plot (see below) shows a visual assessment of the strength of association.
Further reading & references
- Handbook of Parametric and Nonparametric Statistical Procedures (3rd edition)
David J. Sheskin, ISBN 1-58488-440-1 2003; 945.
- Statistics with Confidence (2nd edition)
Gardner M.J., Altman D.G.
ISBN 0-7279-1375-1 2000; 89-90.