Search (3 results, page 1 of 1)

0.0018909799 = product of:
  0.0037819599 = sum of:
    0.0037819599 = product of:
      0.0075639198 = sum of:
        0.0075639198 = weight(_text_:a in 4132) [ClassicSimilarity], result of:
          0.0075639198 = score(doc=4132,freq=10.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.14243183 = fieldWeight in 4132, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4132)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In a comprehensive research project, we investigated the predictive validity of selection decisions and reviewers' ratings at the open access journal Atmospheric Chemistry and Physics (ACP). ACP is a high-impact journal publishing papers on the Earth's atmosphere and the underlying chemical and physical processes. Scientific journals have to deal with the following question concerning the predictive validity: Are in fact the "best" scientific works selected from the manuscripts submitted? In this study we examined whether selecting the "best" manuscripts means selecting papers that after publication show top citation performance as compared to other papers in this research area. First, we appraised the citation impact of later published manuscripts based on the percentile citedness rank classes of the population distribution (scaling in a specific subfield). Second, we analyzed the association between the decisions (n = 677 accepted or rejected, but published elsewhere manuscripts) or ratings (reviewers' ratings for n = 315 manuscripts), respectively, and the citation impact classes of the manuscripts. The results confirm the predictive validity of the ACP peer review system.

Type

a

0.0016913437 = product of:
  0.0033826875 = sum of:
    0.0033826875 = product of:
      0.006765375 = sum of:
        0.006765375 = weight(_text_:a in 2270) [ClassicSimilarity], result of:
          0.006765375 = score(doc=2270,freq=8.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.12739488 = fieldWeight in 2270, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2270)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

It is essential for research funding organizations to ensure both the validity and fairness of the grant approval procedure. The ex-ante peer evaluation (EXANTE) of N?=?8,496 grant applications submitted to the Austrian Science Fund from 1999 to 2009 was statistically analyzed. For 1,689 funded research projects an ex-post peer evaluation (EXPOST) was also available; for the rest of the grant applications a multilevel missing data imputation approach was used to consider verification bias for the first time in peer-review research. Without imputation, the predictive validity of EXANTE was low (r?=?.26) but underestimated due to verification bias, and with imputation it was r?=?.49. That is, the decision-making procedure is capable of selecting the best research proposals for funding. In the EXANTE there were several potential biases (e.g., gender). With respect to the EXPOST there was only one real bias (discipline-specific and year-specific differential prediction). The novelty of this contribution is, first, the combining of theoretical concepts of validity and fairness with a missing data imputation approach to correct for verification bias and, second, multilevel modeling to test peer review-based funding decisions for both validity and fairness in terms of potential and real biases.

Type

a

0.0011959607 = product of:
  0.0023919214 = sum of:
    0.0023919214 = product of:
      0.0047838427 = sum of:
        0.0047838427 = weight(_text_:a in 1007) [ClassicSimilarity], result of:
          0.0047838427 = score(doc=1007,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.090081796 = fieldWeight in 1007, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1007)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Since the 1990s, with the heightened competition and the strong growth of the international higher education market, an increasing number of rankings have been created that measure the scientific performance of an institution based on data. The Leiden Ranking 2011/2012 (LR) was published early in 2012. Starting from Goldstein and Spiegelhalter's (1996) recommendations for conducting quantitative comparisons among institutions, in this study we undertook a reformulation of the LR by means of multilevel regression models. First, with our models we replicated the ranking results; second, the reanalysis of the LR data showed that only 5% of the PPtop10% total variation is attributable to differences between universities. Beyond that, about 80% of the variation between universities can be explained by differences among countries. If covariates are included in the model the differences among most of the universities become meaningless. Our findings have implications for conducting university rankings in general and for the LR in particular. For example, with Goldstein-adjusted confidence intervals, it is possible to interpret the significance of differences among universities meaningfully: Rank differences among universities should be interpreted as meaningful only if their confidence intervals do not overlap.

Type

a