The journal impact factor (JIF) proposed by Garfield in the year 1955 is one of the most commonly used and prominent citation-based indicators of the performance and significance of a scientific journal. The JIF is simple, reasonable, clearly defined, and comparable over time and, what is more, can be easily calculated from data provided by Thomson Reuters, but at the expense of serious technical and methodological flaws. The paper discusses one of the core problems: The JIF is affected by bias factors (e.g., document type) that have nothing to do with the prestige or quality of a journal. For solving this problem, we suggest using the generalized propensity score methodology based on the Rubin Causal Model. Citation data for papers of all journals in the ISI subject category “Microscopy” (Journal Citation Report) are used to illustrate the proposal.
Authors:Lutz Bornmann, Rüdiger Mutz and Hans-Dieter Daniel
In the grant peer review process we can distinguish various evaluation stages in which assessors judge applications on a rating
scale. Bornmann & al.  show that latent Markov models offer a fundamentally good opportunity to model statistically peer review processes.
The main objective of this short communication is to test the influence of the applicants’ gender on the modeling of a peer
review process by using latent Markov models. We found differences in transition probabilities from one stage to the other
for applications for a doctoral fellowship submitted by male and female applicants.