

Semester Offering:  




As shown by the needs expressed by alumni and managers working in both public and private organizations, it is extremely important for students to have basic understanding of and practical experience with selected techniques in quantitative methods, in particular, analysis of factors to establish causal relationship between independent variables and dependent variables. The objective of this course is to provide pratical analytical training to students so that they can examine their research questions and prove or disprove hypotheses through these tools. In particular, the course introduces a set of basic regression techniques, which enable the students to implement the analysis of factors while quantifying the effects of each factor. In fact, many students recognize at the time of writing their thesis that addressing some of the research questions in their thesis actually requires these skills. The course emphasizes handson practices in hybrid class and interpretation of analytical results, rather than deliberation on theories and concepts. Use of complicated equations including calculus is dispensed with.




Upon successful completion of this course, students will be able to:




None. It does not require any statistical training beforehand.




1. Types of variables




Sessions involve lectures and handson exercise with students’ laptop computers. Lectures and practices go hand in hand.


Learning Resources:  


1. Stock, JH and Watson, MW (2017) Introduction to Econometrics, 3rd edition, AddisonWesley International.




1. Venables, WN, Smith DM, and the R Core Team (2017) Introduction to R: Notes on R: A Programming Environment for Data Analysis and Graphics. Manual for R version 3.4.3. R, Foundation for Statistical Computing. https://cran.rproject.org/doc/manuals/rrelease/Rintro.pdf




1. International Journal of Social Research Methodology (Taylor & Francis)




Lecture: 22.5hours




Lectures and practices in class. Selected quantitative techniques will be illustrated by using R software.




The midterm exam carries 50% of the weight; the assignment 10%; and the term paper 40%. Class attendance will not be taken into account, as the students are expected to decide which classes to attend based on their previous knowledge in statistics. Grade “A” would be awarded if a student demonstrates excellent understanding on topics covered in the course. Grade “B” would be awarded if a student shows an overall understanding of topics covered in the course. Grade “C” would be given if a student meets below average expectations on both understanding and application. Grade “D” would be given if a student does not meet basic expectations in analyzing or understanding issues covered in the course. Grade “B+” is positioned between Grade “A” and Grade “B”, while Grade “C+” is considered between Grade “B” and Grade “C”.

