Scientists often are evaluated by metrics based on citations of scientific papers, because of a common belief that more citations equates higher quality. Is is so? A commonly used metric, the Journal Impact Factor, mainly considers the citations of other scientists' papers. Does this make sense?
In a recently published invited essay in European Science Editing, Frank Krell discusses a few crucial aspects and misunderstandings of the Journal Impact Factor as a performance indicator.
Abstract. The Journal Impact Factor is the most commonly applied metric for evaluation of scientific output. It is a journal-focused indicator that shows the attention a journal attracts. It does not necessarily indicate quality, but high impact factors indicate a probability of high quality. As an arithmetic mean of data originating from all authors of a journal with a high variance, it is inapplicable to evaluate individual scientists. For quantifying the performance of authors, author-focused citation metrics are to be used, such as the h-index, but self-citations should be excluded ("honest h-index" hh). All citation metrics suffer from the incompleteness of the databases they source their data from. This incompleteness is unequally distributed between disciplines, countries and language-groups. The Journal Impact Factor has its limitations, but if they are taken into consideration, it is still an appropriate indicator for journal performance.