Cross-Sectional Analysis of Methods of Computing Partial Correlation Coefficients: A Self-Explained Note With R Syntax

Timothy A. Ogunleye, Kehinde K. Adesanya, Oluwatosin J. Akinsola, Godwill I. Wilcox


This paper examines four different methods of computing partial correlation coefficients. These include conventional method, variance-covariance matrix approach, regression residual’s approach, and OLS method. Each of these is fully illustrated with practical examples as well as R syntax. Applicability of each of the methods is discussed in our illustrations. Strength and weakness of each method are extensively detailed. It’s, however, discovered that none of the basic assumptions of partial correlation: linearity, normality, and non-existence of outliers is violated after performing statistical checks on the datasets used. The study, therefore, recommends the best method(s) of computing partial correlation coefficients when at least one variable is held constant, thereby adding more invaluable knowledge to the existing literatures. Finally, the study further recommends the best method in each scenario with illustrative examples as evidences. 


Conventional method; OLS method; Partial correlation; Regression residual’s approach; Variance-covariance matrix method

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