What types of errors are hiding in Google Scholar data? Methodological concerns (Preprint)

Abstract

Google Scholar (GS) is a free tool that may be used by researchers to analyze citations, to find appropriate literature or to evaluate the quality of an author or a contender for tenure, promotion, a faculty position, funding or research grants. GS has become a major bibliographic and citation database. Following the literature, databases such as PubMed, PsycINFO, Scopus or Web of Science can be used in place of GS because they are more reliable.

The aim of this study is to examine the accuracy of citation data collected from GS and provide a comprehensive description of the errors and miscounts identified.

281 documents that cited two specific works were retrieved from the Publish or Perish software and examined. This work studied the false positive issue inherent in the analysis of neuroimaging data.

The results reveal an unprecedented error rate: 99.3% of the references examined contain at least one error. Consequently, Google Scholar data not only fail to be accurate but also potentially expose those researchers who would use these data without verification to substantial biases in their analyses and results.

Google Scholar data not only fail to be accurate but also potentially expose those researchers who would use these data without verification to substantial biases in their analyses and results.