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.

Other Information

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 RStudio, a free user interface for R. We will be using RStudio during lectures as it provides an easier way to interact with the R environment.

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.

Indicative Assessment

1. Seminar participation (10) [LO 1,2]
2. List of exercises (4) (6 unit: 1 hour per assignment) (12 unit: 2 hours per assignment) (40) [LO 1,2,3]
3. Midterm Exam (6 unit: 1 hour) (12 unit: 2 hours) (20) [LO 1,2,3]
4. Research Paper (6 unit - 3000 words) (12 unit - 6000 words) (30) [LO 1,2,3,4]

The ANU uses 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. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.

Workload

For 6 units:

130 hours of total student learning time made up from:

a) 36 hours of contact over 12 weeks: comprising lectures and tutorials; and

b) 94 hours of independent student research, reading and writing.

For 12 units:

260 hours of total student learning time made up from:

a) 36 hours of contact over 12 weeks: comprising lectures and tutorials; and

b) 224 hours of independent student research, reading and writing.

Inherent Requirements

Not applicable

Prescribed Texts

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.

Preliminary Reading

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.

Offerings, Dates and Class Summary Links