當研究者想要了解態度量表中不同組間之態度分數是否有所差異時，一個常見的分析方法為變異數分析。然而，變異數分析需要建立在資料服從常態分配之假設上，態度量表之資料類型卻很明顯地不符合此一假設。而非針對連續型資料所推導出來的 統計量，應該是較適合處理序列或是等距尺度等非常態資料之檢定方法。本研究主要之目的即為探討利用 統計量以及利用變異數分析兩者所作出之檢定結果差異為何。過去相關研究皆假設態度量表背後存在一連續潛在變數，本研究則直接由間斷型分配出發。在公式推導上，我們發現 統計量與變異數分析中之 統計量存在一對一對應之關係。雖然兩統計量近似之分配不同，但兩統計量所對應之p值卻始終非常接近。若以0.05為顯著水準， 統計量與 統計量之檢定結果幾乎完全相同。當需要檢定不同組間在多題上之看法是否具有差異時，我們比較了將屬於同一主題之各題分數加總，然後依照單變量變異數分析之方法進行檢定，以及多變量變異數分析法、羅吉斯迴歸分析法等三種方法。根據我們的模擬結果，若各組在各題之態度皆很類似，則利用ANOVA進行分析可以得到較低的型一誤差；若各組在各題之態度不太一致，且有左右偏分配互相抵銷的情形，則利用MANOVA或是羅吉斯迴歸分析法才能夠維持住很高的檢定力。 / 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.