Correlation is a measure of the relation between two or more variables. The measurement scales used should be at least interval scales, but other correlation coefficients are available to handle other types of data. Correlation coefficients can range from -1.00 to +1.00. The value of -1.00 represents a perfect negative correlation while a value of +1.00 represents a perfect positive correlation. A value of 0.00 represents a lack of correlation.

The most widely-used type of correlation coefficient is Pearson r, also called linear or product-moment correlation. It is the basic type of correlation that is offered in the Basic Statistics and Tables module. More specialized correlations are included in other modules; see the other correlations coefficients topic.

Arrangement of data. You can correlate any variables in your data set, but if the selected variables contain nominal data (see Elementary Concepts), you need to use a special type of correlation, such as those included in the Crosstabulation tables procedures of Basic Statistics and Tables).

For additional Correlations Overview topics, see:

Simple linear correlation (Pearson r)

How to interpret the values of correlations

Quantitative approach to outliers

Correlations in non-homogeneous groups

Nonlinear relations between variables

Exploratory examination of correlation matrices

Casewise vs. pairwise deletion of missing data

How to identify biases caused by the bias due to pairwise deletion of missing data

Pairwise deletion of missing data vs. mean substitution

Are correlation coefficients "additive?"

How to determine whether two correlation coefficients are significant