多元反應變數的邏輯斯迴歸分析(Multinomial Logistic Regression Model ) 和二元邏輯斯迴歸(Binary Logistic Model)為目前分析問卷答題狀況最廣為使用的方法。利用上述兩種方法找出影響反應變數的因子中的顯著因子，並加以探討而後給予改善的意見與回饋。但在次序資料 (Ordinal data)中，上述的統計方法忽略了次序資料中反應變數彼此程度上強弱的關係。在近年次序迴歸(Ordinal Regression)也開始被廣為使用於次序資料的分析，因此本文針對 2014年健保資料庫的醫療品質滿意度和個人資料之間的關係做完整的分析，比較不同模型分析之結果。本文將不同情形下的次序迴歸做比較討論。最後分析結果顯示，在非平行的情形下累積模型搭配邏輯斯連結函數在這幾個模型比較中是比較好的；而在個人基本資料中唯有健康自覺與醫療品質滿意度有顯著的關係。

Agresti A. (1984)Analysis of ordinal Categorical Data. New York: John Wiley and Sons.
Armstrong B G, Sloan M.(1989) Ordinal regression models for epidemiologic data. Am J Epidemiol; 129: 191-204.
Ananth C V and Kleinbaum D G. (1997) Regression Models for Ordinal Responses：A Review of methods and Applications.
Alan Agresti.(2010) Modeling Ordinal Categorical Data.
Cox D R.(1972) Regression models and life tables(with discussion). J R Statis Soc Series B ; 34: 187-220.
Feinberg B. (1980) Analysis of Cross-Classified Data. Second Edition. Cambridge: Massachusetts Institute of Technology Press.
Greenland S.(1985) An application of logistic models to the analysis of ordinal responses. Biometrical J; 27: 189-97.
Greenland S.(1994). Alternative models for ordinal logistic regression. Stat Med; 13:1665-77.
Harrell , Frank E.(2015) Regression modeling strategies [electronic resource] : with applications to linear models, logistic and ordinal regression, and survival analysis.
J.Scott Long .(2012) Regression Models for Nominal and Ordinal Outcomes.
Laara E, Mathews J N S.(1985) The equivalence of two models for ordinal data. Biometrika; 72: 206-207
McCullagh P.(1980) Regression models for ordinal data(with discussion). JR Statis Soc Series B; 42:109-42.
McCullagh P, Nelder J.H.(1989) Generalized Linear Models. Second Edition. London, UK: Chapman and Hall.
T.W.Yee. (2010) VGAM Family Functions for Categoriacl Data.