Type of Credit: Required
Credit(s)
Number of Students
This course consists of three major themes: probability theory, hypothesis testing, and experiments. These themes serve as the foundation for students to pursue further studies in advanced statistical analysis and methods such as models with fixed and random effects, time series data, and Bayesian modeling. The order of the themes is arranged in a way to ensure that students understand the fundamentals (probability, descriptive statistics, probability distribution, and sampling distribution) before knowing how to make statistical inferences (hypothesis tests, experiments, and regression analysis). In addition to the lectures, we have labs in which the students will learn how to use R codes to carry out analytical tasks related to the topics we have covered in class.
能力項目說明
This course aims to equip students with the necessary statistical tools and concepts to analyze and interpret data within the context of social science research. The primary learning goals include (1) learning to summarize and describe the essential features of a dataset; (2) understanding the principles of drawing conclusions about populations based on sample data; (3) gaining an understanding of probability and probability/sampling distributions and their application in social science research.; (4) understanding various statistical tests (e.g., t-tests, chi-square tests, etc.) and when to use them, and (5) digging in how to design, conduct, analyze, and interpret experimental research within the context of social science. By achieving these learning goals, students should be well-equipped to conduct rigorous and valid statistical analyses in their social science research, contributing to the advancement of knowledge in social sciences.
|
週次 |
課程主題 |
課程內容與指定閱讀 |
教學活動與作業 |
|
1 |
Course introduction |
Agresti, ch 1 |
Data Variables |
|
2 |
Sampling and measurement |
Agresti, ch 2 |
Sapling bias Variability Probabilistic sampling Non-probabilistic sampling |
|
3 |
Getting to know the data |
Agresti, ch 3 |
Descriptive analysis Visualization |
|
4 |
Probability
|
Sahu, ch 3 & 4 |
Probability Conditional probability Bayes theorem Statement of Research Questions due by end of class |
|
5 |
Probability distribution
|
Sahu, ch 6 Agresti, ch 4 |
Probability distributions Sampling distributions |
|
6 |
Statistical inference: Estimation |
Agresti, ch 5 |
Point and interval estimation Confidence interval |
|
7 |
Statistical inference: Significant tests (I) |
Agresti, ch 6.1-6.3 |
Significance test for a mean Significance test for a proportion Literature Review due by end of class |
|
8 |
Statistical inference: Significant tests (II) |
Agresti, ch 6.4-6.8 |
Types of errors Limitations of significance tests |
|
9 |
Midterm exam |
Midterm exam |
Midterm exam |
|
10 |
Two group comparisons (I) |
Agresti, ch 7.1-7.4 |
Comparing two means or proportions |
|
11 |
Two group comparisons (II) |
Agresti, ch 7.5-7.8 |
Other methods for comparing means and proportions |
|
12 |
Relationships between categorical variables |
Agresti, ch 8 |
Contingency tables Chi-squared tests Description of Theory due by end of class |
|
13 |
Experiments (I) |
Druckman, ch 2 |
Scientific process and causal inferenceSampling Measurement Causal inference |
|
14 |
Experiments (II)
|
Druckman, ch 3 & 5 |
Evaluating experiments Realism Validity Samples Pre-analysis plan Replication |
|
15 |
Experiments (III) |
Druckman, ch 4 & 6 |
Audit field experiments Conjoint experiments Lab-in-the-field experiments Description of Hypothesis due by end of class |
|
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/Participation and Quizzes (9% and 15%)
Homework (36%)
Research Proposal Statements (2.5%*4)
Midterm (15%)
Final Exam (15%)
Attendance/Participation and Quizzes (9% and 15%): 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 class and lab activities. Your class participation grade will be assessed based on lecture attendance and your contributions to lab activities. At the beginning of each class, students will take a quiz to test their comprehension of the reading materials of that specific week. For the lab, the teaching assistant may distribute additional section syllabi that detail specific lab expectations and requirements. Please note that unexcused absences in lectures or 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 (36%): Students will complete six problem sets designed to test the comprehension of the material covered in class (see the schedule for the exact due dates). 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 and need to be submitted to submission links at Moodle.
Research Proposal Statements (2.5*4%): Students are required to finish a proposal adopting quantitative methods. The proposal should highlight the following aspects: selecting an interesting topic, reviewing relevant literature, building your theory, and formulating hypotheses. You are advised to be extremely realistic while preparing your research proposal because you must carry it out in the next semester. An infeasible proposal will receive a very low score for the final paper. I strongly urge you to start thinking about the research topics as early as possible so you can have sufficient time to identify an appropriate topic through trial and error. In order to keep students on track, these project assignments (about one to two pages, doubled spaced) will be assigned throughout the semester (please see the details in the “Schedule” Section).
Midterm and Final Exams (15%*2): The final exam is not cumulative. You may bring one A4-size note to the exam. A calculator is necessary, hopefully, one with which you are familiar, without connection to the Internet. Laptop computers are not permitted during the test. Mark your calendar now because it is very unlikely that I create make-up tests or re-schedule tests for any one person.
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.