Type of Credit: Elective
Credit(s)
Number of Students
This course is a continuation of "Introduction to Statistical Analysis." It introduces students to more advanced tools of statistics and shows how they are used in the analysis of social science data. The course will introduce students to the idea of multivariate analysis. It covers the basics of regression analysis and more advanced statistical methods.
能力項目說明
Upon successful completion of this course you will be able to complete the following tasks:
1. To 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. To be able to carry out multivariate statistical techniques and methods properly and effectively.
The course expects every student to spend at least 6 hours per week (including in-class time) to prepare for and review course material.
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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 |
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|
課堂講授 In-class Hours |
課程前後 Outside-of-class Hours |
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|
1 |
Introduction and Review |
3 |
3 |
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|
2 |
One-way Analysis of Variance |
Ch. 12: 12.1 to 12.3 |
See Moodle |
3 |
3 |
|
3 |
Linear Regression and Correlation |
Ch. 9 |
See Moodle |
3 |
3 |
|
4 |
Intro to Multivariate Analysis |
Ch. 10 |
See Moodle |
3 |
3 |
|
5 |
Multiple Regression and Correlation |
Ch. 11 |
See Moodle |
3 |
3 |
|
6 |
Multiple Regression and Correlation (cont.) |
Ch. 11 |
See Moodle |
3 |
3 |
|
7 |
Combining Regression and ANOVA |
Ch. 13 |
See Moodle |
3 |
3 |
|
8 |
Model Building with Multiple Regression |
Ch. 14 |
See Moodle |
3 |
3 |
|
9 |
Logistic Regression |
Ch. 15 |
See Moodle |
3 |
3 |
|
10 |
Mid-term presentation of research topics |
3 |
3 |
||
|
11 |
Intro to Advanced Methods |
Ch. 16 |
See Moodle |
3 |
3 |
|
12 |
Factor Analysis |
Supplementary readings |
See Moodle |
3 |
3 |
|
13 |
NCCU Anniversary |
No class |
|
|
|
|
14 |
Multilevel Analysis |
Supplementary readings |
See Moodle |
3 |
3 |
|
15 |
Structural Equation Modeling/Latent Variable Approach |
Supplementary readings |
See Moodle |
3 |
3 |
|
16 |
Time Series Analysis |
Supplementary readings |
See Moodle |
|
|
|
17 |
Intro to Causal Inference |
Supplementary readings |
See Moodle |
3 |
3 |
|
18 |
Final report presentation |
See Moodle |
3 |
3 |
|
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.
IDAS regulations:
“(i) Do your own work. Plagiarizing from other students, books and journals, the internet, and other sources is a serious offense and is not acceptable. Plagiarism is automatic grounds for failing the course. Be sure to fully cite your work in regard to any paper due for the course. Plagiarism is the deliberate or reckless representation of another's words, thoughts, or ideas as one's own without attribution in connection with the submission of academic work, whether graded or otherwise. (ii) All academic work in this course, including homework, quizzes, and exams, is to be your own work unless otherwise specified. It is your responsibility if you have any doubt to confirm whether or not, and in what form, collaboration is permitted.”
Grading:
Homework: 40%
Mid-term Presentation: 15%
Final Research Paper: 35%
Attendance: 10%
A+:100~90; A:89~85; A-:84~80; B+:79~77; B: 76~73; B-:72~70 (For graduate students, the passing grade is 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
Each student is required to present a preliminary research project of his/her choice in the mid-term. The project should be related to the final research paper. A typical presentation includes:
1. A brief review of the 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 - Here the focus is only on the statistical method of concern.
The presentation should be 15 minutes at most.
Final research paper
The course requires each student to use a data set on a topic of their choice. The data set preferably should 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 on such data for testing them.
Agresti, Alan, 2018. Statistical Methods for the Social Sciences. Upper Saddle River, NJ: Pearson International Education.