# 106年第2學期-6458 類別資料分析 課程資訊

## 評分方式

1、 平日作業 60 (1) 四份實作作業（40%）；(2) 導讀（依修課人數調整）：（20%）
2、 學期報告 40

## 課程概述

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.

## 參考書目

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.