基于CRITIC-G1和Bonferroni算子的学术期刊综合评价研究

[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。     

基于HTFWBM算子的多属性关联决策方法

研究属性间相互关联环境下的犹豫三角模糊多属性决策(MADM)问题,提出一种新的算法。首先,基于犹豫三角模糊数(HTFN)的运算规则和Bonferroni平均(BM)算子定义犹豫三角模糊Bonferroni平均(HTFBM)算子和犹豫三角模糊加权Bonferroni平均(HTFWBM)算子;其次,研究HTFBM的一些性质,建立基于HTFWBM的MADM模型;最后,通过一个投资项目选择算例证明算子的有效性与可行性。     

Multiplicity Control in Structural Equation Modeling: Incorporating Parameter Dependencies

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.     

多重比较及其在销售数据分析上的应用

本文对LSD方法、Bonferrioni方法、SNK方法、Tukey方法和Duncan方法等五种多重比较方法的检验效果进行了比较研究,并以Only服装销售数据为例,用SPSS软件中的多重比较方法对该数据进行了分析和比较。比较结果表明,LSD方法比Bonferroni方法有更高的灵敏度,却不如Bonferroni方法能更好地控制FWER,而划分相似子集时Duncan方法比SNK方法和Tukey方法更灵敏。