Type of Credit: Elective
Credit(s)
Number of Students
This course introduces students to the essential tools of statistics and shows how these tools are used in the analysis of social science data. A fundamental understanding of statistics is a critical foundation for social science research in many fields. The course covers descriptive statistics, inference from samples, hypothesis testing, and the basics of regression analysis.
能力項目說明
Upon successful completion of this course, you will be able to complete the following tasks:
1. Explain basic concepts of social statistics (e.g., population vs. sample, sampling distribution).
2. Summarize numeric data by computing descriptive statistics (e.g., mean, variance) and by creating tables and graphs. For each procedure, you will learn a hand calculation method (using calculators) and a computer method (using software such as Stata or R).
3. Compute various inferential statistics (e.g., t-score).
4. Test hypotheses applying probability theory.
5. Explain the differences among various statistical techniques and identify an appropriate technique for a given set of variables and research questions.
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
|
Week |
Topic |
Content and Reading Assignment |
Teaching Activities and Homework |
|
1 |
Course Introduction |
|
Lecture |
|
2 |
Statistics and Social Research |
Ch. 1 |
Lecture; In-class Discussion; Homework |
|
3 |
Basic Descriptive Statistics I |
Ch. 2 |
Lecture; In-class Discussion; Homework |
|
4 |
Descriptive Statistics II
|
Ch. 3 |
Lecture; In-class Discussion; Homework |
|
5 |
R Lesson I |
|
Lecture; In-class Discussion |
|
6 |
Probability Distribution I |
Ch. 4 |
Lecture; In-class Discussion; Homework |
|
7 |
Probability Distribution II |
Ch. 4 |
Lecture; In-class Discussion; Homework |
|
8 |
1st Quiz & Review |
|
Quiz |
|
9 |
Estimation |
Ch. 5 |
Lecture; In-class Discussion; Homework |
|
10 |
Statistical Inference I |
Ch. 6 |
Lecture; In-class Discussion; Homework |
|
11 |
Statistical Inference II |
Ch. 6 |
Lecture; In-class Discussion; Homework |
|
12 |
2nd Quiz & Review |
|
Quiz |
|
13 |
Comparison of Two Groups |
Ch. 7 |
Lecture; In-class Discussion; Homework |
|
14 |
Analyzing Association between Categorical Variables |
Ch. 8 |
Lecture; In-class Discussion; Homework |
|
15 |
Comparing Groups: ANOVA I |
Ch. 12 |
Lecture; In-class Discussion; Homework |
|
16 |
Linear Regression and Correlation |
Ch. 11 |
Lecture; In-class Discussion; Homework |
|
17 |
Linear Regression and Correlation II |
Ch. 11 |
Lecture; In-class Discussion; Homework |
|
18 |
Final Exam |
|
Quiz |
Attendance:
Class attendance is required. Unlike some other courses, statistics requires you to gradually but constantly build your knowledge and skills. It is very difficult to catch up once you get behind. You are also expected to contribute to the class by asking questions, participating in class discussions, and working with each other on in-class exercises. Therefore, your attendance is essential for making these contributions.
Reading Assignments:
You are expected to read the assigned chapters before you come to each session. In order to successfully complete reading assignments, you need to understand what is in each chapter. In addition to highlighting the text and taking notes, I suggest you write down any specific questions.
You may find some chapters challenging to follow. Don't worry if this happens. It is important to finish reading the assigned chapter before each session to get a general idea about the chapter and go back to it after class to make sure that you understand the materials better.
Homework Assignments:
Learning by doing is very important for your understanding of statistics. There will be exercise questions given to you at the end of most sessions. You will have at least a week to complete each assignment. If you start working on your assignments early, you will have a chance to ask questions in the next class session before submitting your assignments.
I will collect assignments at the beginning of the scheduled class sessions (or you may submit assignments to Moodle in MS Word format). If you turn in your assignments late (anytime after the class session starts and before 4:00 pm on the next day), you will lose points. Where to submit late assignments: To be arranged by the TA.
Honor Code:
Please help each other, by all means, to exchange notes for missed class sessions, study for exams, etc. The assignments that you turn in should be your own work, however. Any form of violation will result in a "zero" for that particular assignment or an "F" for the course, at my discretion.
Grading:
Homework Assignments: 50%
Tests (3 tests including final): 45%
Attendance: 5%
1. Agresti, Alan 2018. Statistical Methods for the Social Sciences. UpperSaddle River, NJ: Pearson International Education. https://www.pearson.com/us/higher-education/program/Agresti-Statistical-Methods-for-the-Social-Sciences-5th-Edition/PGM334444.html
2. Navarro, Danielle. Learning Statistics with R. https://learningstatisticswithr.com/