Impacts and Corrections of Natural Weight on Nonlinear Sci-tech Reviews——Case Study of TOPSIS Method
Yu Liping1,3, Song Xiayun2, Wang Zuogong3()
1School of Management and e-Business, Zhejiang Gongshang University, Hangzhou 310018, China 2School of Accounting, Zhejiang University of Finance and Economics, Hangzhou 310018, China 3Finance School, Guizhou University of Finance and Economics, Guiyang 550025, China
[Objective] This paper explores the implicit natural weight issues facing the scientific and technology review indexes, and then proposes a method to address them. [Methods] First, we analyzed data from the JCR2016 mathematics journals with the help of TOPSIS method, aiming to find the influence of natural weights on the nonlinear evaluation method. Then, we proposed a method increasing the dynamic maximum mean to the standardized level, aiming to eliminate the impacts. [Results] We found that the natural weights posed significant effects to the Nonlinear Evaluation methods. For the weighted method, the design weights, the natural weights and the evaluation methods all affected the actual weights. For the non-weighted method, the natural weights and the evaluation methods affected the actual weights. Eliminating the natural weights could effectively reduce the influence of the evaluation method on the actual weights, which helps the design weights play a bigger role. The distribution of index data also affected the actual weights. [Limitations] The proposed method is still an approximation algorithm, which could not yield the exactly equal means. [Conclusions] To achieve the fair review for the science and technology products, we must pay attention to the natural weights issues, which is a systematic error.
俞立平, 宋夏云, 王作功. 自然权重对非线性科技评价的影响及纠正研究*——以TOPSIS方法评价为例[J]. 数据分析与知识发现, 2018, 2(6): 48-57.
Yu Liping,Song Xiayun,Wang Zuogong. Impacts and Corrections of Natural Weight on Nonlinear Sci-tech Reviews——Case Study of TOPSIS Method. Data Analysis and Knowledge Discovery, 2018, 2(6): 48-57.
Vinkler P.Introducing the Current Contribution Index for Characterizing the Recent,Relevant Impact of Journals[J]. Scientometrics, 2008, 79(2): 409-420.
Adler R, Ewing J, Taylor P.Citation Statistics: A Report from the International Mathematical Union (IMU) in Cooperation with the International Council of Industrial and Applied Mathematics (ICIAM) and the Institute of Mathematical Statistics (IMS)[J]. Statistical Science, 2009, 24(1): 1-14.
Seglen P O.The Skewness of Science[J]. Journal of the Association for Information Science and Technology, 1992, 43(9): 628-638.
(Yu Liping, Pan Yuntao, Wu Yishan.Comparative Research on Weight of Different Objective Evaluation Methods in Scientific and Technological Evaluation[J]. Science and Technology Management Research, 2009(7): 148-150.)
(Yu Liping, Liu Aijun.The Misunderstandings and Optimization of Principal Component and Factor Analysis in Journal Evaluation[J]. Journal of Intelligence, 2014, 33(12): 94-98.)
(Wang Huazhong, Qiang Fengjiao, Chen Xiaotun.Redetermination of Weight and Evaluation Principle in Fuzzy Comprehensive Evaluation[J]. Statistics and Decision, 2015(8): 24-27.)
Hagerty M R, Land K C.Constructing Summary Indices of Quality of Life: A Model for the Effect of Heterogeneous Importance Weights[J]. Sociological Methods and Research, 2007, 35(4): 455-496.
Kahneman D, Tversky A.Prospect Theory: An Analysis of Decision Under Risk[J]. Econometrica, 1979, 47(2): 263-292.
Edwards W, Barron F H.SMART and SMARTER: Improved Simple Methods for Multiattribute Utility Measurement[J]. Organizational Behavior and Human Decision Processes, 1994, 60(3): 306-325.
(Cao Xiuying, Liang Jingguo.The Method of Ascertaining Attribute Weight Based on Rough Sets Theory[J]. Chinese Journal of Management Science, 2002, 10(5): 98-100.)
(He Lihua, Wang Liqi, Zhang Lianying.A Method for Determining the Experts’ Weights of Multi-Attribute Group Decision-Making Based on Clustering Analysis[J]. Operations Research and Management Science, 2014, 23(6): 65-72.)