Type of Credit: Required
Credit(s)
Number of Students
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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. Following the course in the previous semester, this course will cover topics including regression models, generalized linear models, and Bayesian analysis. |
能力項目說明
The lab is designed to deepen students’ understanding of those 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/STATA.
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
|
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 STATA and R |
Long and Freese, ch 1-2 Agresti, Appendix (pp.517-526) |
Installing STATA and R and using both for data wrangling, descriptive statistics, and hypothesis testing |
|
3-4 |
Correlation and Simple Linear Regression |
Agresti, ch 9 Heumann et al, ch 11.1-11.5 |
Generating crosstabs and scatterplots for preliminary analysis Conducting analysis of simple linear regression models and interpreting the results |
|
5-6 |
Multiple linear regression |
Agresti, ch 10-14 Heumann et al, ch 11.6-11.12 |
Carrying out analysis that involves multiple linear regression with both continuous and categorical independent variables and interpreting the model outputs |
|
7-8 |
GLM and models for binary outcomes |
Long and Freese, ch 4-6 and 9.1-9.3 Heumann et al, ch 12 |
Constructing models for binary dependent variables (hypothesis testing, model fit, result interpretation) |
|
9-10 |
Ordinal logit models and Multinomial logit models |
Long and Freese, ch7-8 |
Understanding the differences between ordinal and multinomial logit models and implement analysis of both |
|
11 |
Midterm exam |
Midterm exam |
Midterm exam |
|
13-14 |
Bayesian analysis (I) |
McElreath, ch 1-3 |
Using simulation to explore the Bayes’ theorem and sampling |
|
15-16 |
Bayesian analysis (II) |
McElreath, ch 4-6 |
Conducting Bayesian linear models and interpreting the results |
|
18 |
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 students' own original work. Violations of this policy will be considered academic misconduct. |
指定書目
Agresti. 2018. Statistical Methods for the Social Sciences (5th Edition).
Heumann, Schomaker, Shalabh. 2022. Introduction to Statistics and Data Analysis With Exercises, Solutions and Applications in R (2nd Edition).
Long. and Freese. 2014. Regression Models for Categorical Dependent Variables Using Stata. 3rd Edition, Stata Press, College Station
McElreath. 2016. Statistical Rethinking: A Bayesian Course in R and Stan. CRC Press.
參考書目
Weisberg, Sanford. 2013. Applied Linear Regression, 4th Ed. New York: Wiley. (ALR’s Wiley page.)
Fox, John. 2015. Applied Regression Analysis and Generalized Linear Models, Third Edition. Thousand Oaks, CA: Sage Publications.
Faraway, Julian J. 2016. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression, 2nd Ed. London: Chapman & Hall.
Fox, John, and Sanford Weisberg. 2019. An R and S-Plus Companion to Applied Regression, Third Edition. Thousand Oaks, CA: Sage Publications.
Gelman, Andrew, Jennifer Hill, and Aki Vehtari. 2020. Regression and Other Stories. New York: Cambridge University Press.
Shalizi, Cosma. 2021. Advanced Data Analysis from an Elementary Point of View.