Type of Credit: Required
Credit(s)
Number of Students
The purpose of the lab is to ensure that students can use statistical software to facilitate their data analysis after they have learned relevant concepts in the lectures. The skills of quantitative analysis allow students to observe, describe, and explain political phenomena. Such skills are essential for students to not only complete their final projects but also academic writing.
能力項目說明
The lab is designed to deepen students’ understanding of the statistical concepts and methods they have acquired in the lectures. In the lab, students will have opportunities to carry out quantitative analysis with real-world examples. The learning-by-doing process can familiarize students with the fundamental know-how to master the statistical software of R.
|
週次 |
課程主題 |
課程內容與指定閱讀 |
教學活動與作業 |
|
1-2 |
Prepping for R |
Agresti, ch1-2 |
Installing R and learn the basics of R coding; random sampling |
|
3-4 |
Descriptive Analysis |
Agresti, ch 3 |
Using statistics and plots to describe data |
|
5-6 |
Probability and Sampling Distribution |
Agresti, ch 4 |
Visualize probability and sampling distribution |
|
7-8 |
Estimation and Hypothesis Testing |
Agresti, ch 5-6 |
Constructing confidence intervals and calculating p-values for tests |
|
9 |
Midterm |
Midterm exam |
Midterm exam |
|
10-11 |
Two group comparisons |
Agresti, ch7 |
Comparing two means, proportions, and variances |
|
12-13 |
Relationship between categorical variables |
Agresti, ch 8 |
Contingency table, chi-squared tests |
|
14-15 |
Experiment |
Druckman, ch 2-5 |
Analyze experiment data and draw DAGs to describe causal relationships |
|
16 |
Final Exam |
Final Exam |
Final Exam |
This course is organized around lectures, readings, and labs. You are expected to attend lectures as well as lab sections. To pass the class, ALL assignments must be completed.
Attendance and Participation (10%)
Homework (30%)
Quizzes (20%)
Midterm (20%)
Final exam (20%)
Attendance and Participation (10%): Your preparation, presence, and participation are crucial. Please complete the required readings, be on time for each class, bring all relevant readings, and contribute energetically to the lab activities. Teaching assistants may distribute additional section syllabi that detail specific lab expectations and requirements. Please note that unexcused absences in the lab will count heavily against your grade. An absence will be excused only with documentation of medical necessity or with prior approval from your teaching assistant.
Homework (30%): Students will complete five problem sets designed to how well you can utilize statistical softwares for data analysis. You may consult with your classmates. However, each student must write up and turn in their own work/assignment. Assignments deemed too similar to another student’s assignment will receive a score of 0. Working (struggling) on the homework is the only sure way to master the material. All homework assignments are due at the beginning of the class.
Quizzes (20%): In lab sessions, we will conduct in-class quizzes to test students’ comprehension of the materials covered in class. No make-up for quizzes will be arranged.
Midterm and final exams (20*2%): The exams are open book, open-note. The exams are designed to test your ability to carry out data analysis using either R or STATA. A calculator is necessary, hopefully, one with which you are familiar (with functions no more than + - x ÷ ⎷ xy log exp M). Laptop computers are not permitted during the test. Mark your calendar now because it is very unlikely that I will create make-up tests or re-schedule tests for any one person.
Policy on using Generative AI tools: It is totally fine for students to consult with Generative AI tools for learning purposes. However, for completing their assignments and the data analysis project, all the input should be the student's own original work. Violations of this policy will be considered academic misconduct.
指定書目
Agresti. (2018). Statistical Methods for the Social Sciences. Pearson (5th Edition).
Druckman. (2022). Experimental thinking. Cambridge University Press.
Sahu (2024). Introduction to Probability, Statistics & R: Foundations for Data-Based Sciences. Cham: Springer International Publishing.
參考書目
Lewin, C. (2005). Elementary quantitative methods. Research methods in the social sciences, 215-225.
Petscher, Y. M., Schatschneider, C., & Compton, D. L. (Eds.). (2013). Applied quantitative analysis in education and the social sciences. Routledge.
Davies, M. B., & Hughes, N. (2014). Doing a successful research project: Using qualitative or quantitative methods. Bloomsbury Publishing.
Hancock, G. R., Stapleton, L. M., & Mueller, R. O. (Eds.). (2018). The reviewer’s guide to quantitative methods in the social sciences. Routledge.
Stockemer, D., Stockemer, G., & Glaeser, J. (2019). Quantitative methods for the social sciences (Vol. 50, p. 185). Cham, Switzerland: Springer International Publishing.