**Pearson product-moment correlation coefficient**, which is also known more simply as

*r*.

*r*can vary between -1 and 1 (or, in more mathematical terms, -1 <=

*r*<= 1). an

*r*value of 0 means that the two variables that you are looking at have no systematic relationship. That is, if you know a value of Variable A you still know nothing about Variable B. A positive correlation means that the two variables are related such that as one changes, the other changes in the same direction. A negative correlation means that the two variables are related such that as one goes up, the other goes down. The closer

*r*is to its limits (1 or -1), the stronger the relationship between the two variables in question.

By squaring

*r*, you get a useful measure which tells you how much variance in one variable is accounted for by variance in the other variable. So if

*r*-squared is 0.45, for example, then 45% of the variance in Variable A is accounted for by Variable B. In loose terms, if you knew a person's score for Variable A, you can guess with 45% accuracy what their score on Variable B is. When a correlation is 1 or -1, all the points fall exactly on the line of best fit, and you can predict Variable B with perfect accuracy.

Correlations are done in SPSS by choosing

**analyse > correlate > bivariate**.

Graphs are obtained with

**graphs > scatter... > simple**. To edit a graph, double-click it. To add a trendline, select

**chart > options... >**and tick "fit line - total".