On correlation analysis of many‐to‐many observations: an alternative to Pearson's correlation coefficient and its application to an ecotoxicological study. (24th November 2017)

• Record Type: Journal Article
• Authors: Moltchanova, Elena; Gerhard, Daniel; Mohamed, Fathimath; Gaw, Sally; Glover, Chris N.
• Is Part Of: Australian & New Zealand journal of statistics. Volume 59:Number 4(2017)
• Page Start: 371
• Page End: 387
• Publication Date: 2017-11-24
• Subjects: mixed models -- ecological science -- analysis of variance and covariance -- Pearson's correlation coefficient -- trace elements -- mussels -- toxicity -- sediment; Statistics -- Periodicals; 519.5
• DOI: 10.1111/anzs.12211
• Languages: English
• ISSNs: 1369-1473

Abstract

Summary: Correlation studies are an important hypothesis‐generating and testing tool, and have a wide range of applications in many scientific fields. In ecological studies in particular, multiple environmental variables are often measured in an attempt to determine relationships between chemical, physical and biological factors. For example, one may wish to know whether and how soil properties correlate with plant physiology. Although correlation coefficients are widely used, their properties and limitations are often imperfectly understood. This is especially the case when one is interested in correlations between, say, trace element content in sediments and in marine organisms, where no one‐to‐one correspondence exists. We show that evaluating Pearson's correlation coefficient for either site‐specific means or composite samples results in biased estimates, and we propose an alternative estimator. We use simulation studies to demonstrate that our estimator generally has a much smaller bias and mean squared error. We further illustrate its use in a case study of the correlation between trace element content in sediments and in mussels in Lyttelton Harbour, New Zealand. Abstract : Can we do better than calculate the correlation between sample means? An easy‐to‐implement method for many‐to‐many correlation analysis.