SemesterSpring Semester, 2021
DepartmentPhD Program of Business Administration, First Year PhD Program of Business Administration, Second Year PhD Program of Business Administration, Third Year
Course NameSeminar in Special Topics in QuantitativeMethod (I)
InstructorHSIEH KAI-YU
Credit3.0
Course TypeElective
Prerequisite
Course Objective
Course Description
Course Schedule









































































































































Week



Topic



Content



Activities



Workload (hours)



1



(2/25)



Orientation



Overview of the course



Lecture and Q&A



6



2



(3/4)



Basics of statistics



Descriptive statistics, confidence interval, and hypothesis testing



(Wooldridge Appendix C5-6)



Lecture and discussion



6



3



(3/11)



OLS I



Simple ordinary least square (OLS) regression



(Wooldridge Chapter 2)



Lecture, discussion, and practicum (using Stata)



6



4



(3/18)



OLS II



Multiple regression analysis



(Wooldridge Chapters 3-5)



Lecture, discussion, and practicum (using Stata)



6



5



(3/25)



OLS III



Further issues on Multiple regression analysis



(Wooldridge Chapters 6-8)



Lecture, discussion, and practicum (using Stata)



6



6



(4/1)



Progress review and consultation



Project #1 and topics covered from week 2 to week 5



Individual consultation

(with Dr. Fu)



6



7



(4/8)



Panel data



Panel data methods



(Wooldridge Chapters 13, 14)



Lecture, discussion, and practicum (using Stata)



6



8



(4/15)



Instrument variable



IV regression and 2-stage least squares



(Wooldridge Chapters 15)



Lecture, discussion, and practicum (using Stata)



6



9



(4/22)



Limited dependent variable



Limited DV and sample selection corrections



(Wooldridge Chapter 17)



Lecture, discussion, and practicum (using Stata)



6



10



(4/29)



Event history



Survival analysis



(Supplementary materials)



Lecture, discussion, and practicum (using Stata)



6



11



(5/6)



Progress review and consultation



Project #2 and topics covered from week 7 to week 10



Individual consultation

(with Dr. Hsieh)



6



12



(5/13)



Understanding variables and data



Construct, observed item, and measurement scale



Lecture, discussion, and practicum (using SPSS)



6



13



(5/20)



Developing a conceptual (box-and-arrow) model



Correlation, causal relationship, and survey design (1)



Lecture, discussion, and practicum (using SPSS)



6



14



(5/27)



Validating research model



Survey design (2), mediation, and moderation



Lecture, discussion, and practicum (using SPSS)



6



15



(6/3)



Advanced topics



Market segmentation, concepts of machine learning and neuro-marketing



Lecture, discussion, and practicum (using SPSS)



6



16



(6/10)



Progress review and test



Quiz and topics covered from week 12 to week 15



Dataset-based quiz

(administrated by Dr. Park)



6



17



(6/17)



Presentations



Presentations based on either project #1, project #2, or quiz



Individual presentation



6



18



(6/24)



Submissions and consultation



Assignments and all topics covered in the course



Individual consultation



6



Teaching Methods
Teaching Assistant

TBA


Requirement/Grading

Project #1: 30%

Project #2: 30%


Data-based quiz: 30%

Presentation: 10%


Textbook & Reference

TBA


Urls about Course
Attachment