Archive for April 11th, 2006

Finding correlations using the Pearson correlation analysis.

Zakya asked us another question. After getting his Excel data into SPSS, he wanted to find possible correlations between a couple of variables. Zakya, please find below an explanation on finding correlations using the Pearson correlation analysis.

The Pearson correlation analysis test can be used to find correlations between responses of nominal variables.

A correlation analysis is performed to quantify the strength of association between two numeric variables. In the following task we will perform Pearson correlation analysis. The variables used in the analysis are chicken, car, house, and job.
Select Analyze>Correlate>Bivariate. This opens the Bivariate Correlations dialog box. The numeric variables in your data file appear on the source list on the left side of the screen.
Select chicken, car, house, and job from the list and click the arrow box. The variables will be pasted into the selection box. The options Pearson and Two-tailed are selected by default.


Click OK.
A symmetric matrix with Pearson correlation as given below will be displayed on the screen. Along with Pearson r, the number of cases and probability values are also displayed


This is the main matrix of the Pearson’s output. Variables have been arranged in a matrix such that where their columns/rows intersect there are numbers that tell about the statistical interaction between the variables. Three pieces of information are provided in each cell — the Pearson correlation, the significance, and number of cases. When a variable interacts with itself, the correlation will obviously be 1.00. No significance is given in these cases.

Notice that the .775 has asterisks by it. As is indicated at the bottom of the output this is how SPSS indicates significant interactions for you. Notice the significance is under 0.05 (.041).

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