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A multilevel modelling approach to investigating the predictive validity of editorial decisions: do the editors of a high profile journal select manuscripts that are highly cited after publication?
Author(s) -
Bornmann Lutz,
Mutz Rüdiger,
Marx Werner,
Schier Hermann,
Daniel HansDieter
Publication year - 2011
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/j.1467-985x.2011.00689.x
Subject(s) - citation , selection (genetic algorithm) , citation impact , computer science , predictive validity , percentile , psychology , library science , statistics , mathematics , artificial intelligence
Summary. Scientific journals must deal with the following questions concerning the predictive validity of editorial decisions. Is the best scientific work selected from submitted manuscripts? Does selection of the best manuscripts also mean selecting papers that after publication show top citation performance within their fields? Taking the journal Angewandte Chemie International Edition as an example, this study proposes a new methodology for investigating whether manuscripts that are most worthy of publication are in fact selected validly. First, the influence on citation of the accepted and rejected but then published elsewhere manuscripts was appraised on the basis of percentile impact classes scaled in a subfield of chemistry and, second, the association between the decisions on selection and the influence on citation of the manuscripts was determined by using a multilevel logistic regression for ordinal categories. This approach has many advantages over methodologies that were used in previous research studies on the predictive validity of editorial selection decisions.