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Academic literature on the topic '檢定統計量'
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Dissertations / Theses on the topic "檢定統計量"
Wu, Yu Yang, and 吳裕陽. "J型-發散統計量與數種適合度檢定統計量之比較." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/91411256113240996995.
Full textZENG, SHUN-XING, and 曾順興. "關於順序統計量在檢定方面的應用." Thesis, 1991. http://ndltd.ncl.edu.tw/handle/92573680309699506943.
Full text黃士峰. "檢定統計量具有主要支配項時其檢定力的逼近方法." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/23323161953168809720.
Full text林昱君. "態度量表中檢定組間差異之統計方法." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/82169752133375313924.
Full text國立政治大學
統計研究所
92
In social science literature, we frequently found that ANOVA techniques were utilized to analyze Likert-type response data. However, one of the three basic assumptions behind ANOVA is that response variable is normally distributed, and Likert-type data apparently do not share this property. In this study, we compare the performance between statistic associated with ANOVA with Mantel- Haenszel statistic, a statistic aimed at handling categorical data. We found that statistic and statistic have one-to-one relationship. Although these two statistics can be approximated by distribution and Chi-square distribution respectively, their p values are quite close to each other. At the significant level of 0.05, and statistics almost have the same testing results. In addition to analyzing a single Likert-type response question, we would also like to analyze a set of Likert-type response questions that probably represent a specific concept. We propose two alternatives here. The first one is MANOVA, and the second one is logistic regression analysis. According to the simulation results, using the ANOVA approach is slightly better in terms of the type I error rate if the responses have similar structures among questions. On the other hand, using MANOVA or logistic regression analysis would maintain higher power whenever the responses have different structures among questions.
陳松輝. "McNemar檢定中非對角線資料在檢定統計量上訊息提供的探討." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/20109526493077803189.
Full text國立政治大學
統計學系
90
This article is talking about the relationship of the diagonal and non-diagonal in the McNemar test. The data of the diagonal isn't wasted, but it consists in the test statistics and affects the critical area. When we have the stable off-diagonal figures and only change the data of the diagonal, the null hypothesis will be completely different. In other words, the explanation is influenced by the figures of the diagonal and off-diagonal no matter how the testing result is. Therefore, here is a new developed statistics to clarify the connection of the diagonal and off-diagonal.