ANU Online Summer School in Political Analysis 2022

Date & time

Mon 31 Jan 2022, 9.30am – Fri 18 Feb 2022, 12pm



ANU Online Summer School in Political Analysis (SSPA)

January 31st -  February 18th 2022 

Advanced and foundational studies in political analysis and research methods. Hosted by the Australian National University’s School of Politics & International Relations (SPIR).

Research training for tomorrow's world

2022 sees the global community facing significant challenges — politically, economically, socially and environmentally. It’s a world that’s fraught with uncertainty, but it’s also a future that will rely on the expertise of academics and professionals in political science to help navigate the road ahead. That's why, in February 2022, the School of Politics and IR will host a fully online summer school in Political Analysis. We will provide a sequenced combination of foundational and advanced short-course topics focused on methods in political science. 

    Who should take part?

    The SSPA has been created to meet the needs of….

    • University graduates pursuing or seeking to pursue PhD degrees in political science at ANU and other Australian universities
    • International political science graduates pursuing or seeking to pursue advanced studies or enhance their skills and employability in political analysis
    • Professional political science researchers in academia and policy organisations


    Small cost, big benefit

    Each course has been designed to deliver a fully interactive and informative learning experience that will make an invaluable contribution to your academic or professional development — and at a cost that should prove a sound investment for you or your institution to make. 

    The SSPA comprises:

    • Each course (5 half-day sessions) costs A$750

    LINK TO BOOKING/PAYMENT (ANU Students will receive a 20% discount. Students from the ANU Research School of Social Sciences please contact to arrange payment)

    ANU Summer School in Political Analysis Timetable*

      Week 1 (Jan 31 - Feb 4)   Week 2 (Feb 7 - Feb 11)   Week 3 (Feb 14 - Feb 18)
    Mon Tue Wed Thu Fri Mon Tue Wed Thu Fri Mon Tue Wed Thu Fri
    AM session (9:30-12:00)

    Foundations of Research Design & Causal Inference


    AM session (9:30-12:00)

    Maximum Likelihood Estimation and Limited Dependent Variables (AC6)

    AM session (9:30-12:00)

    Bayesian Statistics



    Political Survey




    PM session (13:00-15:30)

    Foundations of Statistical Analysis


    PM  session (13:00-15:30)

    Critical Discourse Analysis


    PM session (13:00-15:30)

    Game Theory


    Political Science



    Longitudinal Data Analysis (Panel and Time Series Data)


    Social Network



    Evening Session (18:00-20:30)   Evening Session (18:00-20:30)

    Political Text Analysis


    Evening Session (18:00-20:30) Advanced Political Text Analysis (AC8)
    *Please note that courses may not be taken concurrently. Please ensure that your chosen courses do not clash before registering.

    Teaching quant with R 

    R is a free and increasingly common statistical computing tool for political science researchers. Unless stated otherwise, quantitative courses in our summer school are taught using R. Keeping our course stack within R eliminates unnecessary cost in training and equipping students, while at the same time streamlining course progression.

    Foundational Courses

    Research Design and Causal Inference in Political Science 

    Causal inference is at the heart of many of the key questions in political science: - why do some nations democratize but not others? What are the causes of war and peace? How do we improve the representation of marginalized groups in political life? But causal inference is hard. In this course, we will examine the principles of good research design, with a particular focus on causal inference. We begin by examining what we mean by ‘causation’, ‘causal inference’ and ‘causal explanation’. We then move to examining a suite of techniques for answering causal questions: - experiments in their various forms, natural or quasi experimental methods such as instrumental variables, difference in differences and regression discontinuity designs, and methods for estimating causal quantities from observational data such as matching, sensitivity analysis and directed acyclic graphs (DAGs). [Instructor: Dr. Charles Miller]

    Foundations of Statistical Analysis in Political Science 

    The aim of this course is to provide students with an accessible introduction to the fundamentals of quantitative political science research. The course will be focused on understanding types of data; data visualization; bivariate statistical methods (t tests, chi-squared tests, correlation); and multivariate statistical methods (simple and multiple regression). [Instructor: Dr. Patrick Leslie]

    Advanced Courses (prerequisites: both foundational courses or permission of instructor)

    Maximum Likelihood Estimation and Generalised Linear Models in Political Science

    The aim of this course is to provide an introduction to commonly used approach to statistical inference and estimation when the dependent variable is either discontinuous or limited in range, and where the partial effects of interests are nonlinear. These models of limited and discrete dependent variables are estimated by maximum likelihood (MLE) approaches, and where appropriate, presented as generalized linear models (GLM). Emphasis will be placed on estimation within R, interpreting and presenting the results (using cases or graphical techniques), hypothesis testing, and the evaluation of model fit. Models considered include

    • Regression for binary, ordered and unordered dependent variables;

    • Regression for counts as dependent variables;

    • Limited dependent variable models: Tobit, selection models, and duration analysis;

    • and potentially other topics of interest.

    Readings will include applied articles which utilise the models considered. [Instructor is Dr. Shawn Treier]

    Political Survey Design and Analysis 

    The aim of this course is to provide students with an introduction to designing and administering high-quality surveys in political science, as well as the basics of survey analysis using R. The course will be focused on the following content areas: measuring concepts and ideas; questionnaire design and question wording; sampling and response maximisation; modes of data collection; and basic survey data analysis. [Instructor: Dr. Jill Sheppard]

    Introduction to Political Text Analysis 

    This course surveys methods for systematically extracting quantitative information from political text for social scientific purposes, starting with classical content analysis and dictionary-based methods, to classification methods, and state-of-the-art scaling methods and topic models for estimating quantities from text using statistical techniques. The course lays a theoretical foundation for text analysis but mainly takes a very practical and applied approach, so that students learn how to apply these methods in actual research. [Instructor: Prof. Ken Benoit]

    Longitudinal Data Analysis (Panel and Time Series Data)

    Instructor Bio: Dr. Feodor Snagovsky is an Assistant Professor in the Department of Political Science at the University of Alberta. His research interests include elections, political behaviour, representation, comparative politics and political elites. Snagovsky’s research has been published in Electoral Studies, Parliamentary Affairs, Government and Opposition, the Australian Journal of Political Science and the Canadian Journal of Political Science.

    Course outline: The aim of this course is to provide a practical, hands-on introduction to the analysis of longitudinal (cross-sectional time series and panel) data for answering research and policy questions. The course will be focused on the following content areas:

    • Learning how longitudinal data differs from other forms of data
    • Familia­rising students with some common longitudinal datasets in the social sciences in Stata
    • Understanding a number of statistical methods for analysing longitudinal data in Stata
    • Understanding how to interpret the results of these analyses

    Learning outcomes: Upon successful completion of this course, students will have the knowledge and skills to:

    • Develop good research questions for applying longitudinal data analysis
    • Understand the assumptions, strengths and limitations of techniques used to analyse longitudinal data in the social sciences
    • Describe, clean and manipulate longitudinal data
    • Apply basic estimation techniques for longitudinal data analysis
    • Present and interpret longitudinal data analyses; and
    • Communicate the policy and/or theoretical implications of longitudinal analyses.

    ReadingsRabe-Hesketh, Sophia and Skrondal, Anders. 2012. Multilevel and longitudinal modelling using Stata. Vol. 1: Continues responses. Third Edition. College Station, Texas: Stata Press.

    Pre-requisite understanding: This is an introductory course, but students will benefit from having some previous statistical training and experience in using the statistical software Stata. Students with less than an undergraduate-level knowledge of linear regression methods and some familiarity with issues like sample selection and endogeneity should review:

    • Kellstedt, Paul M. and Guy D. Whitten. 2018. The Fundamentals of Political Science Research. (3rd ed.). Cambridge University Press.

    Students without familiarity of basic Stata commands and experience with writing Stata do-files should review:

    • Acock, Alan C. 2012. A Gentle Introduction to Stata. (6th ed.). Stata Press.

      [Instructor: Dr. Feodor Snagovsky] NOTE: This course will be taught in Stata. Students will require their own Stata licence. 

    Bayesian Statistics for Politics 

    The aim of this course is to provide an introduction to the Bayesian approach to statistical inference and estimation for applications in political science using the statistical modelling package R. Emphasis will be placed on the understanding the basic framework of Bayesian statistics, comparison with traditional frequentist approaches, the implementation of these models, the interpretation of results, and the evaluation of model fit.  Models considered include. [Instructor: Dr. Shawn Treier]

    Introduction to Critical Discourse Analysis 

    The aim of this course is to introduce students to the foundations of Critical Discourse Analysis. The course will provide an overview of the foundations of CDA in critical and social theory, hermeneutics, social constructivism, and socio-linguistics before introducing students to the conceptual framework and analytical tools of CDA.  [Instructor: Dr. April Biccum]

    Introduction to Social Network Analysis 

    The “network perspective” puts emphasis on social ties between actors, rather than individual characteristics, in understanding behaviour and outcomes. This course provides a practical introduction to the network perspective and social network analysis (SNA), with a focus on applications in social science, in particular political analysis. Students will learn about collecting and storing social network data, and will gain practical experience in visualising networks and constructing basic SNA metrics. There will also be an introduction to exponential random graph models (ERGM), which provide a statistical inferential framework for modelling interdependencies in social tie formation such homophily, reciprocity and transitivity. The course involves the use of R, with a focus on igraph for network visualisation and basic SNA, and statnet for ERGM. [Instructor: Professor Robert Ackland]

    Game Thoery in Political Science 

    This course will introduce the basics of game theory and show how they can be used to help explain political phenomena. We will cover normal form, extensive form, and repeated games and discuss what they suggest about the nature of coordination problems and political institutions. The course will touch on a range of topics familiar to political scientists, including voting, disarmament, and the regulation of public goods. The goal is to equip participants with the knowledge they need to use game-theoretic models in future research. [Instructuor: Dr. Will Bosworth]

    Advanced Political Text Analysis 

    The course covers advanced methods for systematically extracting quantitative information from political text for social scientific purposes, building on the foundations from Political Text Analysis. The topics covered include: statistical scaling of texts to measure latent variables; topic models for estimating quantities from text using statistical techniques; classification methods for predicting document classes; clustering methods for text and word features; and ways to model and use word embedding to further improve model accuracy. [Instructor: Prof. Ken Benoit]


    For further information please contact Prof. Ben Goldsmith or Matthew Robertson .

    Updated:  1 December 2021/Responsible Officer:  Head of School/Page Contact:  CASS Marketing & Communications