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.
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Research funding: None declared.
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Author contribution: The author has accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: Author state no conflict of interest.
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Informed consent: Informed consent was obtained from all individuals included in this study.
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Ethical approval: The local Institutional Review Board deemed the study exempt from review.
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© 2021 Walter de Gruyter GmbH, Berlin/Boston
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- Reviewer Acknowledgment
- Reviewer acknowledgment
- Tutorial
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Articles in the same Issue
- Research Articles
- Determinants of birth-intervals in Algeria: a semi-Markov model analysis
- A simplified approach to bias estimation for correlations
- Gamma frailty model for survival risk estimation: an application to cancer data
- Analysis for transmission of dengue disease with different class of human population
- Quantifying the influence of location of residence on blood pressure in urbanising South India: a path analysis with multiple mediators
- Mixed methods to assess the use of rare illicit psychoactive substances: a case study
- Reliability of fetal–infant mortality rates in perinatal periods of risk (PPOR) analysis
- Sampling from networks: respondent-driven sampling
- Reviewer Acknowledgment
- Reviewer acknowledgment
- Tutorial
- A guide to value of information methods for prioritising research in health impact modelling