• Class Number 7340
  • Term Code 3360
  • Class Info
  • Unit Value 6 to 12 units
  • Mode of Delivery In Person
    • Dr Thiago Nascimento da Silva
    • Dr Thiago Nascimento da Silva
  • Class Dates
  • Class Start Date 24/07/2023
  • Class End Date 27/10/2023
  • Census Date 31/08/2023
  • Last Date to Enrol 31/07/2023
SELT Survey Results

This course will introduce students to the fundamental concepts and tools of quantitative methodology in political science. The content covers a range of topics, including descriptive statistics, basic probability, statistical inference and regression analysis. Additionally, students will have the opportunity to gain practical experience in statistical computing and data analysis by utilising the statistical software R and applying the knowledge they have acquired in class to their own research. A strong background in mathematics is not a prerequisite for this course. All that is required is a willingness to actively participate and learn.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. understand the foundations of quantitative analysis that are commonly employed across the discipline;
  2. critically evaluate the complexity of contemporary politics from the perspective of solid research design and empirical analysis;
  3. generate and visualise descriptive and inferential statistics for political phenomena using statistical programming software; and
  4. apply relevant concepts and quantitative techniques to complete a research project and prepare a presentation suitable for delivery at a graduate-level political science conference.

Research-Led Teaching

The ANU is committeed to teaching and training students through a process called research-led teaching. Amongst other things, this apporach involves convenors using their own research and research experience as a pedagogical tool when teaching students. Consequently, I will use examples, datasets, and R/Stata code from my own research on political elites to demonstrate many of the concepts and procedures covered in this course. Further details pertaining to research-led teaching in general, and at the ANU can be found in the links below.



Required Resources

Students are required to obtain copies of the following books:

- Kellstedt, Paul M., and Guy D. Whitten. 2018. The Fundamentals of Political Science Research. Cambridge: Cambridge University Press. [Third Edition]

- Imai, Kosuke, and Nora Webb Williams. 2022. Quantitative Social Science: An Introduction in Tidyverse. Princeton: Princeton University Press.

In this course, students will also be introduced to statistical computing and data analysis using the free statistical software R. It is recommended to download R and RStudio, a free user interface for R, onto your own machine. We will be using RStudio during lectures as it provides an easier way to interact with the R environment.

To delve deeper into the fundamental concepts covered in this course, students can enhance their confidence in statistics and the application of mathematics in political science by exploring supplementary readings. Below, you'll find a list of suggested additional references (though not exhaustive) that may prove valuable in this context.

- Angrist, Joshua D. and Jörn-Steffen Pischke. 2009. Mostly harmless econometrics: An empiricist's companion. Princeton: Princeton University Press.

- Angrist, Joshua D. and Jörn-Steffen Pischke. 2014. Mastering 'metrics: The path from cause to effect. Princeton: Princeton University Press.

- Bueno de Mesquita, Ethan and Anthony Fowler. 2021. Thinking Clearly About Data: A Guide to Quantitative Reasoning and Analysis. Princeton: Princeton University Press.

- Cunningham, Scott. 2021. Causal Inference: The Mixtape. New Haven: Yale University Press.

- Evans, Merran, et al. 2011. Statistical Distributions. Hoboken: John Wiley & Sons.

- Gill, Jeff. 2006. Essential Mathematics for Political and Social Research. Cambridge: Cambridge University Press.

- Greene, William H. 2018. Econometric Analysis. New York: Pearson.

- Hansen, Bruce E. 2022. Econometrics. Princeton: Princeton University Press.

- Imai, Kosuke. 2018: Quantitative Social Science: An Introduction. Princeton: Princeton University Press.

- Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. Newbury Park: Sage.

- Moore, Will and David A. Siegel. 2013. A Mathematics Course for Political and Social Research. Princeton: Princeton University Press.

- Pearl, Judea and Dana Mackenzie. 2018. The Book of Why: The New Science of Cause and Effect. New York: Basic Books.

- Pearl, Judea, Madelyn Glymour and Nicholas P. Jewell. 2019. Causal Inference in Statistics: A Primer. Hoboken: John Wiley & Sons.

- Wooldridge, Jeffrey M. 2019. Introductory Econometrics: A Modern Approach. Boston: Cengage Learning.

While no prior knowledge of R is required, there are many great resources and tutorials available online that will be useful for learning R. The following free tutorials can be particularly helpful:

- "An Introduction to R" (2023) by W. N. Venables, D. M. Smith and the R Core Team.

- "simpleR: Using R for Introductory Statistics" (2002) by John Verzani.

For those that prefer textbooks, I list some suggestions below: 

- Adler, Joseph. 2010. R in a Nutshell by Joseph Adler. O’Reilley Media.

- Braun, John W. and Duncan J. Murdoch. 2008. A First Course in Statistical Programming with R. Cambridge: Cambridge University Press.

- Grolemund, Garrett. 2014. Hands-on programming with R: Write your own functions and simulations. Sebastopol: O’Reilly Media.

- Maindonald, John and W. John Braun. 2010. Data Analysis and Graphics Using R.

- Wickham, Hadley, and Garrett Grolemund. 2016. R for data science: Import, tidy, transform, visualise, and model data. Sebastopol: O’Reilly Media.

It is also a good idea to start building good programming habits. 

- "R Style: An Rchaeological Commentary" (2022) by Paul E. Johnson is a useful text that provides do's and don'ts for programming in R.

Staff Feedback

Students will be given feedback in the following forms in this course:

  • written comments
  • verbal comments
  • feedback to whole class, groups, individuals, focus group etc

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.

Other Information

The information provided is a preliminary Class Outline. A finalised version will be available on Wattle and will be accessible after enrolling in this course. All updates, changes and further information will be uploaded on the course Wattle site and will not be updated on Programs and Courses throughout the semester. Any questions or concerns should be directed to the Course Convenor.

Class Schedule

Week/Session Summary of Activities Assessment
1 Introduction: Exploratory Data Analysis and R
2 Research Question and Research Design
3 Statistical Inference and Causal Inference List of exercises 1
4 Measurement and Types of Data
5 Statistical Distributions and Probability Theory
6 Hypothesis Testing List of exercises 2
7 OLS I: Introduction to Regression Analysis Mid-Term Exam
8 OLS II: Assumptions and Estimation
9 OLS III: Interactions List of exercises 3
10 Causal Inference in Observational Studies I: Instrumental Variables
11 Causal Inference in Observational Studies II: RDD
12 An Introduction to MLE: Logistic Regression List of exercises 4

Tutorial Registration

There are no tutorial registrations for this course.

Assessment Summary

Assessment task Value Learning Outcomes
Lecture Participation 10 % 1, 2
List of Exercises 40 % 1, 2, 3
Midterm Exam 20 % 1, 2, 3
Research Paper 30 % 1, 2, 3, 4

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details


ANU has educational policies, procedures and guidelines , which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Integrity Rule before the commencement of their course. Other key policies and guidelines include:

Assessment Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the Academic Skills website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.


Students are expected to engage fully with the course, the convenor and their peers.


There is no final examination for this course.

Assessment Task 1

Value: 10 %
Learning Outcomes: 1, 2

Lecture Participation

Value: 10%.

Seminar participation is crucial for student learning and will be assessed based on a combination of attendance and active engagement in course discussions. Students are expected to have completed the compulsory readings before each class and to be prepared to discuss them during the class.

Each week students are required to attend and contribute to the overall seminar. This contribution includes but is not limited to the following: participating in the overall discussion; breaking up into groups to solve or discuss a problem, leading a discussion, making a short presentation and short “Try it out” quizzes/assessments.

Assessment Task 2

Value: 40 %
Learning Outcomes: 1, 2, 3

List of Exercises

Value: 40%. (10% for each list of exercises)

The material for this course is best learned through practice. Therefore, there will be four sets of exercises throughout the semester. These exercises will test students' understanding of the concepts covered in class and their abilities in various cognitive domains, including comprehension and application of knowledge and problem-solving skills.

List of Exercises due dates:

List of exercises 1: Week 3. 9 August 2023. 

List of exercises 2: Week 6. 30 August 2023.

List of exercises 3: Week 9. 4 October 2023.

List of exercises 4: Week 12. 25 October 2023.

Assessment Task 3

Value: 20 %
Learning Outcomes: 1, 2, 3

Midterm Exam

Value: 20%.

The midterm exam will assess students' general understanding of aspects of empirical analysis covered in the first part of the course. The midterm exam will consist of multiple-choice exercises from course materials such as slides, lectures and mandatory readings. The midterm exam duration will be 1 hour for the 6-unit option and 2 hours for the 12-unit option.

Midterm Exam due date: Week 7. 20 September 2023.

Assessment Task 4

Value: 30 %
Learning Outcomes: 1, 2, 3, 4

Research Paper

Value: 30%.

Students are required to submit a political science research essay using quantitative methods.

The research paper will evolve from a research paper proposal that the students will have to submit between Week 6 and Week 9 of the course, and associated feedback received from peers and the convenor between Week 10 and Week 12 of the course.

The research paper will require students to apply the concepts and methods covered in the course to their main research interest and will consist of a 3000-word document (for the 6-unit option) and a 6000-word document (for the 12-unit option).

Research Paper due date: 8 November 2023.

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.

The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.

The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.


The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.

Online Submission

You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.

Hardcopy Submission

For some forms of assessment (hand written assignments, art works, laboratory notes, etc.) hard copy submission is appropriate when approved by the Associate Dean (Education). Hard copy submissions must utilise the Assignment Cover Sheet. Please keep a copy of tasks completed for your records.

Late Submission

Individual assessment tasks may or may not allow for late submission. Policy regarding late submission is detailed below:

  • Late submission not permitted. If submission of assessment tasks without an extension after the due date is not permitted, a mark of 0 will be awarded.

Referencing Requirements

The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material.

Returning Assignments

Assessments will be returned via Wattle

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Resubmission of Assignments

Assessment submissions are final. Rewrites are not accepted.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

Dr Thiago Nascimento da Silva

Research Interests

Political institutions, political economy, quantitative methods, comparative politics

Dr Thiago Nascimento da Silva

Wednesday 10:00 11:00
Wednesday 10:00 11:00
By Appointment
Dr Thiago Nascimento da Silva

Research Interests

Dr Thiago Nascimento da Silva

Wednesday 10:00 11:00
Wednesday 10:00 11:00
By Appointment

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