| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
| This course introduces practical methods for analyzing categorical data and explains how they differ from continuous data. By the end of the course, students are expected to be able to select appropriate analyses, use statistical software to perform them, and correctly interpret the results. |
|
|
|
| Written Presentation |
| Oral Presentation |
| Quiz |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
linear models and dummy variables |
| Week 2 |
linear models and dummy variables |
| Week 3 |
from continuous responses to discrete responses |
| Week 4 |
two-way contingency tables |
| Week 5 |
multi-way contingency tables |
| Week 6 |
binary logit models |
| Week 7 |
multi-category logit models |
| Week 8 |
midterm exam |
| Week 9 |
alternative methods for logit models |
| Week 10 |
log-linear models |
| Week 11 |
Poisson regressions |
| Week 12 |
random-effects models |
| Week 13 |
marginal models |
| Week 14 |
imbalanced data problem |
| Week 15 |
propensity score |
| Week 16 |
final presentation |
self-directed
learning |
02.Viewing multimedia materials related to industry and academia.
03.Preparing presentations or reports related to industry and academia.
|
|
| Evaluation |
Homework: 40%
Midterm Exam: 30%
Final Presentation: 30% |
| Textbook & other References |
Upton, G. J. (2016). Categorical data analysis by example. John Wiley & Sons.
Tang, W., He, H., & Tu, X. (2012). Applied categorical and count data analysis. Chapman and Hall/CRC. |
| Teaching Aids & Teacher's Website |
| The iLearning site will host the uploaded materials. |
| Office Hours |
| Tuesday 13:00~15:00 |
| Sustainable Development Goals, SDGs(Link URL) |
| 08.Decent Work and Economic Growth 09.Industry, Innovation and Infrastructure | include experience courses：N |
|