I just ran across an excellent article by Goodwin and Leech (in the Journal of Experimental Education) that summarizes six common factors that can influence the magnitude and interpretation of the correlation coefficient, a metric that has long been a major part of the statistical engine of individual differences research. The authors do a nice job of explaining, in one place (versus information scatterd across texts and articles), six common misunderstandings regarding the proper care and interpreation of correlational data. The abstract is below.
Kudos to the authors. This is a must reading by individuals reporting correlations as well as users of correlational data (readers of test technical manuals, journal articles, etc.) [click here to view]
- The authors describe and illustrate 6 factors that affect the size of a Pearson correlation: (a) the amount of variability in the data, (b) differences in the shapes of the 2 distributions, (c) lack of linearity, (d) the presence of 1 or more “outliers,” (e) characteristics of the sample, and (f) measurement error. Also discussed are ways to determine whether these factors are likely affecting the correlation, as well as ways to estimate the size of the influence or reduce the influence of each.