SemesterSpring Semester, 2018
DepartmentInternational Master's Program in Asia-Pacific Studies, First Year International Master's Program in Asia-Pacific Studies, Second Year
Course NameIntermediate Statistical Methods
Instructor
Credit3.0
Course TypeElective
Prerequisite
Course Objective
Course Description
Course Schedule

The course expects every student to spend at least of 8 hours per week (including in-class time) for the preparation and review of course material.


































































































































































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



1



Introduction and Review



 



 



3



5



2



One-way Analysis of Variance



Ch. 12: 12.1 to 12.3



See Moodle



3



5



3



Linear Regression and Correlation



Ch. 9



See Moodle



3



5



4



Intro to Multivariate Analysis



Ch. 10



See Moodle



3



5



5



Multiple Regression and Correlation



Ch. 11



See Moodle



3



5



6



Tomb-sweeping Day



No class



 



 



 



7



Multiple Regression and Correlation (cont.)



Ch. 11



See Moodle



3



5



8



Combining Regression and ANOVA



Ch. 13



See Moodle



3



5



9



Model Building with Multiple Regression



Ch. 14



See Moodle



3



5



10



Mid-term presentation of research topics



 



 



3



5



11



Logistic Regression



Ch. 15



See Moodle



3



5



12



Intro to Advanced Methods



Ch. 16



See Moodle



3



5



13



Factor Analysis



Supplementary readings



See Moodle



3



5



14



Multilevel Analysis



Supplementary readings



See Moodle



3



5



15



Structural Equation Modeling/Latent Variable Approach



Supplementary readings



See Moodle



3



5



16



Intro to Causal Inference



Supplementary readings



See Moodle



3



5



17



Review



 



 



3



5



18



Final report presentation



 



See Moodle



3



5



Teaching Methods
Teaching Assistant

To be arranged.


Requirement/Grading

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.


Textbook & Reference

Agresti, Alan & Barbara Finlay, 2009. Statistical Methods for the Social Sciences. Upper Saddle River, NJ: Pearson International Education.


Urls about Course
Attachment

Statistical Literacy rubric.pdf