Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

Click here for more information on Research and Evaluation in Education and Psychology, 3e

Sign In to gain access to subscriptions and/or personal tools.
Educational and Psychological Measurement
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Rupinski, M. T.
Right arrow Articles by Dunlap, W. P.
Right arrow Search for Related Content
Social Bookmarking
 Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

Approximating Pearson Product-Moment Correlations from Kendall's Tau and Spearman's Rho

Melvin T. Rupinski

Tulane University, ps70awg{at}mailhost.tcs.tulane.edu

William P. Dunlap

Tulane University

This article used Monte Carlo methods to investigate the accuracy of formulas presented by Kendall for estimating Pearson's r from X and by Pearson for estimating Pearson's r from rs. Results indicated that the formula for approximating r from r is somewhat more accurate than the formula for approximating r from rs. However, both formulas were found to be quite accurate. The largest asymptotic difference between a converted and actual r was -.005. In addition, the standard errors of the transformed X and rs coefficients were compared to the standard error of r, and the empirically derived percentage increase in standard error was compared to theoretically derived estimates. Implications of these findings for meta-analyses of correlations are discussed.

Educational and Psychological Measurement, Vol. 56, No. 3, 419-429 (1996)
DOI: 10.1177/0013164496056003004


Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?


This article has been cited by other articles:


Home page
Educational and Psychological MeasurementHome page
J. W. Bang, R. E. Schumacker, and P. L. Schlieve
Random-Number Generator Validity in Simulation Studies: An Investigation of Normality
Educational and Psychological Measurement, June 1, 1998; 58(3): 430 - 450.
[Abstract]