# Big Macs and Eigenfactor Scores: Don't Let Correlation Coefficients Fool You

## TL;DR

This paper clarifies that Eigenfactor Scores offer distinct and valuable information for evaluating scholarly journals, countering claims that they are redundant due to high correlation with other metrics.

## Contribution

The paper demonstrates that Eigenfactor Metrics provide statistically and economically significant information beyond Impact Factor and Total Citations, correcting a common statistical fallacy.

## Key findings

- Eigenfactor Scores differ significantly from Impact Factor and Total Citations.
- High correlation does not imply redundancy between metrics.
- Eigenfactor Metrics are a useful addition to bibliometric evaluation.

## Abstract

The Eigenfactor Metrics provide an alternative way of evaluating scholarly journals based on an iterative ranking procedure analogous to Google's PageRank algorithm. These metrics have recently been adopted by Thomson-Reuters and are listed alongside the Impact Factor in the Journal Citation Reports. But do these metrics differ sufficiently so as to be a useful addition to the bibliometric toolbox? Davis (2008) has argued otherwise, based on his finding of a 0.95 correlation coefficient between Eigenfactor score and Total Citations for a sample of journals in the field of medicine. This conclusion is mistaken; here we illustrate the basic statistical fallacy to which Davis succumbed. We provide a complete analysis of the 2006 Journal Citation Reports and demonstrate that there are statistically and economically significant differences between the information provided by the Eigenfactor Metrics and that provided by Impact Factor and Total Citations.