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[目的/意义]针对学术期刊综合评价中多指标信息集结的问题,提出一种新的基于CRITIC-G1和Bonferroni算子的学术期刊综合评价方法。[方法/过程]首先,利用CRITIC法充分考虑指标间的对比强度和冲突性,并确定指标的客观权重;其次,引入G1法提取决策专家的群体智慧,并确定指标的主观权重;再次,基于乘法归一化法对指标的主客观权重进行有效结合,以此确定指标的组合权重;最后,在此基础上,利用Bonferroni算子计算方案的最终评价值并排序。[结果/结论]选取部分管理类期刊为对象,实证研究表明,该方法不仅能够充分考虑指标间的对比强度和冲突性,以及有效提取决策专家的群体智慧,还能够较好地消除指标间的相互影响和相互依赖,因此可使得学术期刊评价结果更为客观合理。 相似文献
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When structural equation modeling (SEM) analyses are conducted, significance tests for all important model relationships (parameters including factor loadings, covariances, etc.) are typically conducted at a specified nominal Type I error rate (α). Despite the fact that many significance tests are often conducted in SEM, rarely is multiplicity control applied. Cribbie (2000, 2007) demonstrated that without some form of adjustment, the familywise Type I error rate can become severely inflated. Cribbie also confirmed that the popular Bonferroni method was overly conservative due to the correlations among the parameters in the model. The purpose of this study was to compare the Type I error rates and per-parameter power of traditional multiplicity strategies with those of adjusted Bonferroni procedures that incorporate not only the number of tests in a family, but also the degree of correlation between parameters. The adjusted Bonferroni procedures were found to produce per-parameter power rates higher than the original Bonferroni procedure without inflating the familywise error rate. 相似文献
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