Correlation and explaining variance: To square or not to square?

Research output: Contribution to journalEditorialpeer-review


Despite previous articles dating back 80 years, the questions of whether and when to square correlations continue to puzzle and confuse researchers. In this editorial. I point out that correlations can serve two independent purposes: they can be measures of effect size in themselves and their function as regression coefficients can be used to estimate proportion of variance in one measure for which another measure accounts. Using examples relevant to intelligence researchers, I show that the answer to the question of whether or not to square a correlation is 'it depends.' It depends on the purpose and it depends on the underlying theoretical model of the causal association between the variables. (C) 2011 Elsevier Inc. All rights reserved.

Original languageEnglish
Pages (from-to)249-254
Number of pages6
Issue number5
Publication statusPublished - 2011


  • Variance
  • Correlation
  • Coefficient of variation
  • Effect size


Dive into the research topics of 'Correlation and explaining variance: To square or not to square?'. Together they form a unique fingerprint.

Cite this