A correlation is a statistical term meaning that there is an association or relationship between two or more variables (Woodbury, 2001) and the correlational statistic itself is a number that describes the type of relationship or association that exists (Gavin, 2008)). One well known correlation is that between income and education. Although not always, on average, a person's yearly income will increase with increases in his or her education (Macionis, 2007).
There are diverse reasons why people examine for correlations between variables; further, there are diverse correlational statistics that they can use when examining for correlations. Two of the most often used correlational statistics for computing the association between two variables are the Pearson correlation statistic and the Spearman correlation statistic (Woodbury, 2001). However, there are theoretical and functional differences between these two statistics (Gravetter & Wallnau, 2006). The purpose of this paper is to discuss these differences.
Appendix A presents two data sets of 20 observations each, along with a brief description of how the data were collected. Examination of these two data sets illustrates the differences that are being discussed. The first difference between the Pearson and Spearman correlations is that they have been developed for different levels of data. Specifically, the Spearman correlation is customarily used when data are ordinal data which is to say grouped into classes such as ranks. On the other hand, the Pearson correlation statistics is typically used when data are equal-interval (spaces between numerical values represent equal amounts) or ratio (have a true zero point) in nature (see: Woodbury, 2001).
As can be seen from examination of Appendix A, Data Set 1 which would be appropriate for the use of a Pearson correlation; this is because it is data derived from a test of mathematical ability and the test has a true zero...