在社會科學的分析中，我們雖然想方設法將許多現象予以數值化，以方便我們使用多元線性迴歸來進行統計分析。然而，更多社會現象是以類別的姿態呈現於真實世界，這種質性變項難以數值化，但卻是我們日常生活的常態。例如，你有多快樂？非常不快樂、不快樂、普通、快樂、非常快樂，許多研究基於方便可能將其視為連續變項，但正確來說，這是一個具有順序層級的類別變項，當它成為研究的依變項時，我們無法使用一般線性迴歸來估計。因此，處理類別資料的統計模型成為社會科學研究中重要的一個部份。本課程即在此基礎上，將帶領同學熟悉類別資料的分析。本課程除了進行統計理論的討論之外，也將透過揣摩期刊論文的操作、詮釋來幫助同學熟悉這些分析模型，更強調實際的資料分析，以期同學具有操作、分析資料、與詮釋資料的能力。
此課程的核心目標包含兩個部分，第一部分是引導學生具有閱讀與評論量化研究論文的能力；第二部分著重於量化資料的實際分析，以培養學生利用統計方法與軟體進行資料整理與比較的能力。台灣學界已經累積豐富具全國代表性的「次級資料」，這些大型資料庫將提供我們這門課程實作的分析素材，其中橫斷面資料是較容易掌握與入門的資料庫，因此本課程將以Stata統計軟體來示範分析「台灣社會變遷基本調查計畫七期二次家庭組」的資料。最後，期許修課同學完成一份具有質量的學期報告。

In social science analysis, we try our best to digitize many phenomena so that we can use multiple linear regression to conduct statistical analysis. However, more social phenomena are presented in the real world in the form of categories. Such qualitative variables are difficult to quantify, but they are the norm in our daily lives. For example, how happy are you? Very unhappy, unhappy, ordinary, happy, very happy, many studies may regard it as a continuous variable based on convenience, but to be correct, this is a categorical variable with an ordinal hierarchy. When it becomes the dependent variable of the study, we cannot use general linear regression to estimate it. Therefore, statistical models for processing categorical data have become an important part of social science research. On this basis, this course will lead students to become familiar with the analysis of category data. In addition to discussing statistical theory, this course will also help students become familiar with these analysis models by figuring out the operation and interpretation of journal articles. It also emphasizes actual data analysis, so that students can have the ability to operate, analyze, and interpret data.
The core goal of this course consists of two parts. The first part is to guide students to have the ability to read and comment on quantitative research papers; the second part focuses on the actual analysis of quantitative data to cultivate students' ability to use statistical methods and software to organize and compare data. The Taiwanese academic community has accumulated a wealth of nationally representative "secondary data." These large databases will provide us with analytical materials for the implementation of this course. Among them, cross-sectional data are easier to master and get started with. Therefore, this course will use Stata statistical software to demonstrate the analysis of data from the "Second Household Group of the Seventh Period of the Basic Survey on Social Change in Taiwan" using Stata statistical software. Finally, students taking the course are expected to complete a quality semester report.

Categorical data analysis that deals with qualitative or discrete quantitative data is one of the most important statistical tools nowadays. In recent years, this tool plays a fundamental role on analyzing polychotomous data, particularly in the social and health sciences. This course introduces statistical theories and models for analyzing categorical data. The main topics cover :
(1) likelihood-based inferences on measures of association for two-dimensional and three-dimensional contingency tables under different assumptions. (2) generalized linear (mixed) models with emphasis on binary (Poisson) regression and logit models. (3) Repeated categorical data modeling, such as generalized estimating equation approaches and quasi-likelihood methods. (4) Asymptotic results and other advanced topics.

Categorical data analysis that deals with qualitative or discrete quantitative data is one of the most important statistical tools nowadays. In recent years, this tool plays a fundamental role on analyzing polychotomous data, particularly in the social and health sciences. This course introduces statistical theories and models for analyzing categorical data. The main topics cover :
(1) likelihood-based inferences on measures of association for two-dimensional and three-dimensional contingency tables under different assumptions. (2) generalized linear (mixed) models with emphasis on binary (Poisson) regression and logit models. (3) Repeated categorical data modeling, such as generalized estimating equation approaches and quasi-likelihood methods. (4) Asymptotic results and other advanced topics.

Long, John Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks: Sage Publications.
［2002，鄭旭智、張育哲、潘倩玉、林克明譯，《類別與受限依變項的迴歸統計模式》。台北：弘智。]
Long, John Scott, and Jeremy Freese. 2014. Regression Models for Categorical Dependent Variables Using Stata, third edition. College Station, Tex.: Stata Press.

Long, John Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks: Sage Publications.
[2002, translated by Zheng Xuzhi, Zhang Yuzhe, Pan Qianyu, and Lin Keming, "Regression Statistical Model of Category and Restricted Dependent Variables". Taipei: Hongzhi. ]
Long, John Scott, and Jeremy Freese. 2014. Regression Models for Categorical Dependent Variables Using Stata, third edition. College Station, Tex.: Stata Press.