Methodological and computational considerations for multiple correlation analysis

Gwowen Shieh*, Chien Feng Kung

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


The squared multiple correlation coefficient has been widely employed to assess the goodness-of-fit of linear regression models in many applications. Although there are numerous published sources that present inferential issues and computing algorithms for multinormal correlation models, the statistical procedure for testing substantive significance by specifying the nonzero-effect null hypothesis has received little attention. This article emphasizes the importance of determining whether the squared multiple correlation coefficient is small or large in comparison with some prescribed standard and develops corresponding Excel worksheets that facilitate the implementation of various aspects of the suggested significance tests. In view of the extensive accessibility of Microsoft Excel software and the ultimate convenience of general-purpose statistical packages, the associated computer routines for interval estimation, power calculation, and sample size determination are also provided for completeness. The statistical methods and available programs of multiple correlation analysis described in this article purport to enhance pedagogical presentation in academic curricula and practical application in psychological research.

Original languageEnglish
Pages (from-to)731-734
Number of pages4
JournalBehavior Research Methods
Issue number4
StatePublished - Nov 2007


  • Multiple correlation analysis
  • Interval estimation
  • Minimum sample size
  • Limit statistical function
  • Multiple correlation coefficient

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