統計是社會學量化研究中不可或缺的方法基礎，也廣泛應用於市場調查與民意調查等領域。本課程的核心目標不在於單純教授統計技術，而是引導學生理解統計分析背後的研究邏輯與方法思考，培養將研究問題轉化為可分析資料的能力。

課程內容將從變項的概念化與測量出發，介紹描述統計與統計推論的基本原理，包括資料比較、圖形呈現、集中趨勢與離散趨勢等，並進一步說明常態分配、抽樣與抽樣分配、估計與假設檢定等推論統計概念。在此基礎上，課程亦涵蓋兩變項與多變項的分析方法，如兩樣本假設檢定、卡方檢定、相關分析（適用於類別、等級與等距變項）、線性迴歸、變異數分析、多元迴歸與羅吉斯迴歸等，強調不同方法的適用條件與研究意涵，而非僅止於操作層面。

本課程亦搭配統計軟體 R 作為分析工具，但重點不在於軟體本身，而在於透過實作訓練學生的資料處理與分析流程。課程將特別強調資料整理的重要性，說明從原始資料到可分析資料所需的各項步驟，並引導學生理解資料整理如何影響後續的統計推論。透過此一訓練，使學生能建立完整且可反思的量化研究方法基礎。

Statistics is an indispensable methodological foundation in quantitative research in sociology, and is also widely used in fields such as market surveys and public opinion surveys. The core goal of this course is not to simply teach statistical techniques, but to guide students to understand the research logic and methodological thinking behind statistical analysis, and to cultivate the ability to transform research questions into analyzable data.

The course content will start from the conceptualization and measurement of variables, introduce the basic principles of descriptive statistics and statistical inference, including data comparison, graphical presentation, central tendency and discrete trend, etc., and further explain inferential statistical concepts such as normal distribution, sampling and sampling distribution, estimation and hypothesis testing. On this basis, the course also covers two-variable and multi-variable analysis methods, such as two-sample hypothesis test, chi-square test, correlation analysis (applicable to categories, levels and equidistant variables), linear regression, variation analysis, multiple regression and Logis regression, etc. It emphasizes the applicable conditions and research implications of different methods, not just at the operational level.

This course also uses the statistical software R as an analysis tool, but the focus is not on the software itself, but on training students' data processing and analysis processes through practice. The course will particularly emphasize the importance of data collection, explain the various steps required from raw data to analyzable data, and guide students to understand how data collection affects subsequent statistical inferences. Through this training, students can establish a complete and reflective foundation for quantitative research methods.

Agresti, Alan 2018 Statistical Methods For the Social Sciences. Fifth Edition. Pearson.

Agresti, Alan 2018 Statistical Methods For the Social Sciences. Fifth Edition. Pearson.