Type of Credit: Required
Credit(s)
Number of Students
This course is a continuation of "Statistics I." It introduces students to more advanced statistics tools and shows how they are used to analyze social science data. The course will introduce students to the idea of multivariate analysis and causal inference. It covers the basics of regression analysis and more advanced statistical methods. The course also requires students to use R to analyze data sets and practice the learned statistical skills.
能力項目說明
Upon successful completion of this course, you will be able to complete the following tasks:
1. Understand the main features of multivariate data.
2. Explain the differences among various statistical techniques and identify an appropriate technique for a given set of variables and research questions.
3. Carry out multivariate statistical techniques and methods properly and effectively.
4. Understand the basics of causal inference based on the counterfactual framework.
5. Develop ability and skills for independent research.
| 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 |
|---|---|---|
The course expects every student to spend at least 8 hours per week (including in-class time) preparing and reviewing course material. The students will also form research groups using learned statistical skills to conduct a research project. Each research group will present its research topic in Week 12 and report the final research result at the end of the semester.
|
週次 Week |
課程主題 Topic |
課程內容與指定閱讀 Content and Reading Assignment |
教學活動與作業 Teaching Activities and Homework |
學習投入時間 Student workload expectation |
|
|
課堂講授 In-class Hours |
課程前後 Outside-of-class Hours |
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|
1 |
Introduction and Review |
|
|
3 |
5 |
|
2 |
Hypothesis Testing IV: Chi-Square Test |
Ch. 11 |
See Moodle |
3 |
5 |
|
3 |
Hypothesis Testing III: The Analysis of Variance |
Ch. 10 |
See Moodle |
3 |
5 |
|
4 |
Bivariate Association for Nominal- and Ordinal-Level Variables |
Ch. 12 |
See Moodle |
3 |
5 |
|
5 |
Association between Variables Measured at the Interval-Ratio Level |
Ch. 13 |
See Moodle |
3 |
5 |
|
6 |
Elaborating Bivariate Tables |
Ch. 14 |
See Moodle |
3 |
5 |
|
7 |
Multiple Regression and Correlation |
Ch. 15 |
See Moodle |
3 |
5 |
|
8 |
R: The 4th Lesson – Exploration of Multivariate Relationship/ Regression with Quantitative and Categorical Predictors |
Supplementary readings |
See Moodle |
3 |
5 |
|
9 |
Model Building with Multiple Regression |
Supplementary readings |
See Moodle |
3 |
5 |
|
10 |
Mid-term quiz |
|
|
|
|
|
11 |
R: The 5th Lesson/Group project discussion |
Supplementary readings |
See Moodle |
3 |
5 |
|
12 |
Presentation of research topics |
|
|
3 |
5 |
|
13 |
Generalized Linear Model & Logistic Regression |
Supplementary readings |
See Moodle |
3 |
5 |
|
14 |
Intro to Advanced Methods; Factor Analysis |
Supplementary readings |
See Moodle |
3 |
5 |
|
15 |
Discussion of the research topics |
|
|
|
|
|
16 |
Final report presentation |
|
TBA |
3 |
5 |
Honor Code:
Please help each other by exchanging notes for missed class sessions, studying for exams, etc. Students must acknowledge all instances in which generative AI tools were used in an assignment (such as in ideation, research, analysis, editing, debugging, etc.). However, the assignments that you turn in should be your own work. At my discretion, any form of violation will result in a "zero" score for that particular assignment or an "F" for the course.
Grading:
Homework: 45%
Quiz: 10%
Mid-term Presentation: 15%
Final Research Paper: 25%
Attendance: 10%
A+:100~90; A:89~85; A-:84~80; B+:79~77; B: 76~73; B-:72~70
Please see the attached "Statistical Literacy Rubrics" for the assessment criteria of all assignments (i.e., homework, presentations, and the final paper).
Mid-term Presentation
Students will form research groups with a maximum of 5 students per group. Each group is required to present a preliminary research project of its choice in Week 13. The project should be related to the final research paper. A typical presentation includes:
1. A brief review of literature - at least one paper related to the intended research questions and using multivariate analysis should be reviewed and discussed in the presentation.
2. Research questions/hypotheses
3. Data utilized - the data set should be appropriate for the intended multivariate analysis
4. Preliminary data analyses and results - The focus is only on the statistical method under concern.
The presentation should last at most 15 minutes.
Final research paper
The course requires each group to use a data set on a topic of their choice. The data set should preferably contain many observations and variables. The task is to develop a series of research hypotheses based on theory or past empirical evidence and then apply some of the multivariate techniques covered in class to such data for testing them.
Healey, J. F., 2021. Statistics: A Tool for Social Research and Data Analysis. New York: Cengage Learning. 11th edition. https://www.tsanghai.com.tw/book_detail.php?c=156&no=4403#p=1
Navarro, Danielle. Learning Statistics with R. https://learningstatisticswithr.com/
Agresti, Alan & Barbara Finlay, 2009. Statistical Methods for the Social Sciences. Upper Saddle River, NJ: Pearson International Education.