normalized

Bibliometric indexes are customary used in evaluating the impact of scientific research, even though it is very well known that in different research areas they may range in very different intervals. Sometimes, this is evident even within a single given field of investigation making very difficult (and inaccurate) the assessment of scientific papers. On the other hand, the problem can be recast in the same framework which has allowed to efficiently cope with the ordering of web-pages, i.e., to formulate the PageRank of Google. For this reason, we call such problem the PaperRank problem, here solved by using a similar approach to that employed by PageRank. The obtained solution, which is mathematically grounded, will be used to compare the usual heuristics of the number of citations with a new one here proposed. Some numerical tests show that the new heuristics is much more reliable than the currently used ones, based on the bare number of citations. Moreover, we show that our model ...

In order to facilitate the interpretation of raw scores, they are usually converted to scale scores. In some cases, these conversions are a series of nonlinear transformations that can affect the conditional standard error of measurement throughout the scale of score. Therefore, the purpose of this study was to introduce methods for calculating the conditional standard error of measurement based on the strong true score theory. Furthermore, comparison of normalized and equipercentile nonlinear transformations on the raw scores of the academic achievements of the graduates of mathematical sciences in 2014 and their effect on conditional standard error of measurement was also conducted. So, in order to achieve these purposes, we used a sample of 3943 high school graduates of Mathematics and Physics in 2014 who had participated in national university entrance examination in 2015 randomly selected by National Organization of Educational Testing. The conditional standard error of measurement under these transformations was estimated based on the binomial procedure of Brennan and Lee (1999) and Chang (2006) method based on the beta-binomial distribution. The results of this study indicated that the conditional standard error of measurement of the Chang was smoother than binomial procedure, but in both methods the estimated errors are larger for middle points and smaller for extreme points. Additionally, the conditional standard errors of measurement of equipercentile were always less than normalized tranformation, so the equipercentile method found to be better than normalized transformation.