文献计量指标的数据不准确性量化与不确定性传播
Data inaccuracy quantification and uncertainty propagation for bibliometric indicators
AI总结：
本研究提出基于贝叶斯回归模型与蒙特卡洛模拟的方法，量化数据误差导致的文献计量指标不确定性并展示其传播过程，通过合成与真实数据验证，指出研究组引用影响得分平均值因数据误差需谨慎使用。
摘要：
本研究提出了一种估算由数据误差导致的文献计量指标值不确定性的方法。该方法利用从经验数据样本中估计得到的贝叶斯回归模型，来预测无误差数据。通过直接蒙特卡洛模拟——针对相同输入数据，从已估计的回归模型中抽取大量预测数据重复样本——可以得到指标值的概率分布，这些分布提供了由数据误差导致的指标不确定性信息。\n研究展示了基础量（例如某单位特定文献类型的出版物数量、某篇出版物的被引次数）的不确定性如何沿着测量模型传播至最终的指标值。研究采用合成示例对该方法进行了说明，并使用真实的文献计量研究评估数据展示了其实际应用。尽管本文仅使用了众多已知文献计量误差类别中的两种，因此仅能解释由不准确性导致的总不确定性的一部分，但后一个示例表明，研究组出版物的引用影响得分平均值需非常谨慎地使用，因为它们往往存在由数据不准确性带来的较大误差范围。
英文摘要：
This study introduces an approach to estimate the uncertainty in bibliometric indicator values that is caused by data errors. This approach utilizes Bayesian regression models, estimated from empirical data samples, which are used to predict error-free data. Through direct Monte Carlo simulation - drawing many replicates of predicted data from the estimated regression models for the same input data - probability distributions for indicator values can be obtained, which provide the information on their uncertainty due to data errors. It is demonstrated how uncertainty in base quantities, such as the number of publications of a unit of certain document types and the number of citations of a publication, can be propagated along a measurement model into final indicator values. Synthetic examples are used to illustrate the method and real bibliometric research evaluation data is used to show its application in practice. Though in this contribution we just use two out of a larger number of known bibliometric error categories and therefore can account for only some part of the total uncertainty due to inaccuracies, the latter example reveals that average values of citation impact scores of publications of research groups need to be used very cautiously as they often have large margins of error resulting from data inaccuracies.
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