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Abstract

Scientific journals publish an increasing number of articles every year. To steer readers' attention to the most important papers, journals use several techniques (e.g., lead paper). Angewandte Chemie International Edition (AC), a leading international journal in chemistry, signals high-quality papers through designating them as a "very important paper" (VIP). This study aims to investigate the citation impact of Communications in AC receiving the special feature VIP, both cumulated and over time. Using propensity score matching, treatment group (VIP) and control group (non-VIP) were balanced for 14 covariates to estimate the unconfounded "average treatment effect on the treated" for the VIP designation. Out of N = 3,011 Communications published in 2007 and 2008, N = 207 received the special feature VIP. For each Communication, data were collected from AC (e.g., referees' ratings) and from the databases Chemical Abstracts (e.g., sections) and the Web of Science (e.g., citations). The estimated unconfounded average treatment effect on the treated (that is, Communications designated as a VIP) was statistically significant and amounted to 19.83 citations. In addition, the special feature VIP fostered the cumulated annual citation growth. For instance, the time until a Communication reached its maximum annual number of citations, was reduced.

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Abstract

Bibliometric data are frequently used to study the effects of events, such as the honoring of a scholar with an award, and to investigate changes of citation impact over time. However, the number of yearly citations depends upon time for multiple reasons: a) general time trends in citation data, b) changing coverage of databases, c) individual citation life-cycles, and d) selection on citation impact. Hence, it is often ill-advised to simply compare the average number of citations before and after an event to estimate its causal effect. Using a recent publication in this journal on the potential citation chain reaction of a Nobel Prize, we demonstrate that a simple pre-post comparison can lead to biased and misleading results. We propose using matched control groups to improve causal inference and illustrate that the inclusion of a tailor-made synthetic control group in the statistical analysis helps to avoid methodological artifacts. Our results suggest that there is neither a Nobel Prize effect as regards citation impact of the Nobel laureate under investigation nor a related chain reaction in the citation network, as suggested in the original study. Finally, we explain that these methodological recommendations extend far beyond the study of Nobel Prize effects in citation data.