A simplified approach to bias estimation for correlations

Xiaofeng Steven Liu

doi:10.1515/em-2021-0015

Article

A simplified approach to bias estimation for correlations

Xiaofeng Steven Liu

Published/Copyright: September 11, 2021

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From the journal Epidemiologic Methods Volume 10 Issue 1

Abstract

Objectives

We introduce a simple and unified methodology to estimate the bias of Pearson correlation coefficients, partial correlation coefficients, and semi-partial correlation coefficients.

Methods

Our methodology features non-parametric bootstrapping and can accommodate small sample data without making any distributional assumptions.

Results

Two examples with R code are provided to illustrate the computation.

Conclusions

The computation strategy is easy to implement and remains the same, be it Pearson correlation or partial or semi-partial correlation.

Keywords: bias; bootstrap; correlation

Corresponding author: Prof. Xiaofeng Steven Liu, PhD, Department of Educational Studies, University of South Carolina, Columbia, SC, USA, E-mail: xliu@email.sc.edu

Research funding: None declared.
Author contribution: The author has accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: Author state no conflict of interest.
Informed consent: Informed consent was obtained from all individuals included in this study.
Ethical approval: The local Institutional Review Board deemed the study exempt from review.

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Received: 2021-04-26

Revised: 2021-08-20

Accepted: 2021-08-29

Published Online: 2021-09-11

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Articles in the same Issue

https://doi.org/10.1515/em-2021-0015

Keywords for this article

bias; bootstrap; correlation