Field-normalization of citations is bibliometric standard. Despite the observed differences in citation counts between fields, the question remains how strong fields influence citation rates beyond the effect of attributes or factors possibly influencing citations (FICs). We considered several FICs such as number of pages and number of co-authors in this study. For example, fields differ in the mean number of co-authors (pages), and – on the paper level – the number of co-authors (pages) is related to citation counts. We wondered whether there is a separate field-effect besides other effects (e.g., from numbers of pages and co-authors). To find an answer on the question in this study, we applied inverse-probability of treatment weighting (IPW) which is a variant of the “propensity score matching” approach (an approach which has been introduced for measuring causal effects). Using Web of Science data (a sample of 308,231 articles), we investigated whether mean differences among subject categories in citation rates still remain, even if the subject categories are made comparable in the field-related attributes (e.g., comparable of co-authors, comparable number of pages) by IPW. In a diagnostic step of our statistical analyses, we considered propensity scores as covariates in regression analyses to examine whether the differences between the fields in FICs vanish. The results revealed that the differences did not completely vanish but were strongly reduced. We received similar results when we calculated mean value differences of the fields after IPW representing the causal or unconfounded field effects on citations. However, field differences in citation rates remain. The results point out that field-normalization seems to be a prerequisite for citation analysis and cannot be replaced by the consideration of any set of FICs in citation analyses.

引文的场归一化是文献计量标准。尽管观察到的字段之间的引用计数存在差异，但问题仍然存在：强大的字段如何影响引用率，超出了可能影响引用（FIC）的属性或因素的影响。在本研究中，我们考虑了一些FIC，例如页数和合著者数量。例如，字段的平均共同作者数（页）不同，并且-在纸张级别上，共同作者数（页）与引用计数相关。我们想知道除其他效果（例如，来自页面和合著者的数量）以外是否还有单独的场效应。要在本研究中找到问题的答案，我们应用了治疗加权的逆概率（IPW），它是“倾向得分匹配”方法（一种用于测量因果效应的方法）的变体。利用Web of Science数据（一份308,231篇文章的样本），我们调查了即使各主题类别在领域相关属性中具有可比性（例如，共同作者，可比较的页数）。在我们的统计分析的诊断步骤中，我们在回归分析中将倾向性得分视为协变量，以检验FIC中各个字段之间的差异是否消失。结果表明，差异并未完全消失，但已大大减少。当我们计算IPW之后字段的均值差异时，我们收到了相似的结果，这些均值表示了引文的因果关系或无混淆的字段影响。但是，引文率的领域差异仍然存在。结果指出，场归一化似乎是引文分析的先决条件，并且不能用在引文分析中考虑任何一组FIC来代替。