An empirical study of retractions due to honest errors: Exploring the relationship between error types and author teams

Abstract

By adopting binary logistic regression and using a dataset of retractions due to honest errors, this paper analyses the relationships between types of honest errors and the characteristics of author teams, aiming to make recommendations about research management for researchers and policy makers. The results show that (1) honest errors made by medium-sized teams are more likely to be data errors rather than other types of errors, than those made by other-sized teams; (2) overall, there is no obvious relationship between types of honest errors and collaboration patterns; (3) there is no significant difference in the probability that honest errors are data errors rather than other types of errors (called “the probability”), with or without the participation of US authors. Honest errors made by teams with the participation of Chinese authors are less likely to be data errors, than those made by teams without Chinese authors; (4) collaboration patterns moderate the relationship between types of honest errors and the participation of Chinese authors. Specifically, the probability is significantly greater for single-authored publications in China than in other countries, and the probability for domestic collaboration in China is much lower than that outside China. There is no significant difference in the probability for international collaboration publications in China and those in other countries.