Variable Decision Citations Decision Number of papers Number of pages Number of co-authors Citations (+1) Decision Number of pages Number of co-authors Model 1: Number of papers published subsequent to application (outcome variable) Model 2: Citations for papers published prior to application (outcome variable) Model 3: Citations for papers published subsequent to application (outcome variable) 12.23 13% 46.57 14% 8.26 5.73 12.30 16% 9.24 6.33 9.64 76.70 4.58 11.67 23.20 4.34 11.36 Table 3. Description of the factors that were potentially associated with quantity and impact of research publications (applicants for the YI programme). Arithmetic mean or percent Standard deviation Minimum Maximum doi:10.1371/journal.pone.0003480.t003 Regression analyses based on bibliometric data for the applicants to the YI programme We carried out the regression analyses described above for the applicants of the YI programme. Table 3 shows a description of the variables that were included in the models. The results of the analyses are presented in Table 4. For this dataset both the page number (model 2) and the number of co-authors per paper (model 3) have statistically significant effects on citation counts. With regard to the decision of the selection committee, all three regression models yield statistically significant effects. For an approved applicant, the expected scientific mean performance is increased by 31% (number of papers), by 41% (citations for papers published prior to application) and by 49% (citations for papers published subsequent to application) against a rejected applicant, holding all other variables in the models (models 2 and 3) constant. In the light of productivity and impact of research in science (paper numbers and citation counts), the EMBO selection committee is making good funding decisions for both programmes. The decisions correspond with the applicants’ subsequent scientific performance. This is also true if only first and last author Table 4. (Zero-truncated) negative binomial regression models predicting (1) number of papers published subsequent to application, (2) citations for papers published prior to application and (3) citations for papers published subsequent to application (applicants for the YI programme). Model 1: number of papers published subsequent to application Model 2: citations for papers published prior to application Model 3: citations for papers published subsequent to application Decision (1 = approved) Number of pages Number of co-authors Publication year Intercept npapers napplicants (clusters) Papers per applicant (cluster) 0.267* (2.22) 2.464*** (55.61) 297 Measuring Peer Review Quality 0 (rejected) 0 (rejected) 0 0 1 1 1 1 1 0 (rejected) 1 (approved) 68 1 (approved) 1,605 1 (approved) 119 544 525 58 438 0.343*** (3.46) 0.031*** (5.32) 0.0249 (1.58) (exposure) 24.24*** (236.73) 6,063 297 minimum = 2 mean = 20 maximum = 92 41% [16%–71%] 0.399** (3.28) 0.0194 (1.74) 0.0416*** (3.38) (exposure) 26.389*** (236.18) 3,535 2941 minimum = 1 mean = 12 maximum = 65 49% [17%–89%] Percent change in expected counts for a unit increase in ‘‘Decision’’ with 95% confidence interval 31% [3%–65%] Note. ML-point estimates (the results of the z-test in parentheses). * p,0.05, ** p,0.01, *** p,0.001. 1truncated sample. Interpretation example for the parameter estimates in the table: In model 2 the number of pages of a publication has a statistically significant effect on receiving citations with a parameter estimate of 0.031. This means that for an additional page, the odds of receiving citations increase by a factor of 1.03 ( = exp(0.031)), holding all other variables in model 2 constant. doi:10.1371/journal.pone.0003480.t004 PLoS ONE | www.plosone.org 6 October 2008 | Volume 3 | Issue 10 | e3480