科技期刊不同来源影响因子比较研究——以34种综合性农业科学类核心期刊为例

Comparison on Sci-tech Journal Impact Factors Based on Different Statistical Methods——Taking 34 Comprehensive Agricultural Scientific Core Journals as an Example

[目的/意义]比较不同来源期刊影响因子的差异性,为期刊评价部门合理选择期刊影响因子提供依据。[方法/过程]选择34种综合性农业科学类核心期刊,分别统计该学科和各刊2012—2014年以CNKI、CJCR和CSCD为统计源的影响因子(IF)、他引影响因子(NSC-IF)、5年影响因子(IF5)和5年他引影响因子(NSC-IF5),并采用DPS数据处理系统进行差异显著性分析。[结果/结论]研究表明,3种数据库4种类型的影响因子排序均为:CNKI>CJCR>CSCD,且三者间差异有高度统计学意义(p<0.01),IF>NSC-IF,差异有高度统计学意义(p<0.01);同一数据库来源的不同期刊IF自引率差异很大,其中CSCD IF自引率变幅达5.51%~52.82%;该学科IF5>IF,但差异无统计学意义(p>0.05),而NSC-IF5>NSC-IF,差异有高度统计学意义(p<0.01),显示该学科他引的引用高峰是在论文发表后5年内而不是2年内。由此认为基于CSCD、CJCR源刊的影响因子更具有代表性,尤其是对于该学科期刊,NSC-IF5能更好地反映期刊的学术影响力。

[ Purposes/Significance ] This paper aims to provide useful references for evaluation divisions of sci-tech journals on reasonable selection of journal impact factors. [ Methods/Process ] Based on statistical source databases of China National Knowledge Infrastructure （CNKI）, China Journal Citation Report （CJCR）, Chinese Science Citation Database （CSCD）, 4 types of impact factors of 34 comprehensive agricultural scientific core journals, including impact factor （IF）, non-self-citing impact factor （NSC-IF）, 5-year impact factor （IF5） and 5-year non-self-citing impact factor （NSC-IF5）, were analyzed by Data Processing System. [ Result/Conclusion] The results showed that 4 types of impact factors of the same journal from 3 different statistic sources followed the order of CNKI〉CJCR〉 CSCD（p〈0.01 ）, and IF〉NSC-IF（p〈0.01 ）. Highly significant differences of the self-citation rate of IF were shown among different journals in the same sources, which was 5.51% -52.82 % from CSCD. It indicated that over self-citation existed in some journals. With IF5〉IF（p〉0.05 ）, but NSC-IF5〉NSC-IF（p〈0.05 ）, it demonstrated that the citation of journals peaked within 5 years not 2 years in comprehensive agriculture area. Therefore, the impact factors based on CSCD and CJCR were more representative, and NSC-IF5 reflected the academic influence of journals more accurately in agriculture area.