The courses below are available for the 2026 Summer School

Foundations of Programming in R for Political Analysis - Dr Thiago Nascimento da Silva

This course provides hands-on experience for learning programming in R, a free and open-source language for statistical computing. Designed for newcomers without any prior programming background, the course covers foundational programming concepts, including basic R commands, operators, package utilisation, functions, conditionals, and loops. Emphasis is then placed on the core areas of data science applied to political analysis, including data types such as categorical, continuous, discrete, and nominal; data structures like vectors, matrices, data frames, and lists; data wrangling techniques from data import/export to cleaning, addressing missing values, outliers, and transformations; exploratory data analysis focusing on measures of central tendency and descriptive statistics; and crafting data visualizations to create professional academic tables and figures for presenting distributions, crosstabulations, and correlations. 

Supplemental online resources, including in-depth mathematical materials, will be available to provide further elaboration on the discussed topics.

Foundations of Statistical Analysis in Political Science – Dr Charles Miller

This course aims to provide students with an accessible introduction to the fundamentals of quantitative political science research. The course will focus on understanding types of data, data visualisation, bivariate statistical methods (t tests, chi-squared tests, correlation); and multivariate statistical methods (simple and multiple regression). 

Survey Experiments for Political Science – Professor Ben Goldsmith 

This course covers the foundations of designing, implementing and analysing basic online survey experiments in political science. It is intended for those with no or limited experience with online survey experiments. Topics covered under survey design include: the inferential logic of experiments, choosing from different types of treatments, designing your experiment to achieve useful results, attention and manipulation checks, ensuring sufficient statistical power. Topics covered under implementation include: piloting / testing, pre-registration, getting the sample you need, soft-launch, and fielding your survey. Topics covered under analysis include: data cleaning and checking, hypothesis testing and data visualization in R, confirmatory and exploratory analysis, and issues of external validity. All examples are drawn from the political science literature.

Critical Discourse Analysis in Political Science - Dr April Biccum

The aim of this course is to introduce students to the foundations of Critical Discourse Analysis and specifically the Fairclough method. 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 the Fairclough method and how it can be operationalised in political science. The course will conclude by pointing students in the direction of more advanced approaches.

This is a full list of courses we offer. 

Please see the “Upcoming Classes” tab for those included in SSPA 2026

Our courses are taught periodically in February and July. We also schedule courses for institutional clients. 

Please contact Prof. Ben Goldsmith to discuss.

Foundational Course

Foundations of Statistical Analysis in Political Science

This course aims to provide students with an accessible introduction to the fundamentals of quantitative political science research. The course will focus on understanding types of data, data visualisation, bivariate statistical methods (t tests, chi-squared tests, correlation); and multivariate statistical methods (simple and multiple regression). 

Instructor: Dr. Charles Miller

Foundations of Programming in R for Political Analysis

This course provides hands-on experience for learning programming in R, a free and open-source language for statistical computing. Designed for newcomers without any prior programming background, the course covers foundational programming concepts, including basic R commands, operators, package utilisation, functions, conditionals, and loops. Emphasis is then placed on the core areas of data science applied to political analysis, including data types such as categorical, continuous, discrete, and nominal; data structures like vectors, matrices, data frames, and lists; data wrangling techniques from data import/export to cleaning, addressing missing values, outliers, and transformations; exploratory data analysis focusing on measures of central tendency and descriptive statistics; and crafting data visualizations to create professional academic tables and figures for presenting distributions, crosstabulations, and correlations.

Supplemental online resources, including in-depth mathematical materials, will be available to provide further elaboration on the discussed topics.

Instructor: Dr Thiago Nascimento da Silva

Advanced Courses

Prerequisites: the foundational course or permission of the instructor

Bayesian Statistics I

This course aims to provide an introduction to the Bayesian approach to statistical inference and estimation for applications in political science. Emphasis will be placed on the understanding the basic framework of Bayesian statistics and implementing and evaluating these models. Basic models will use basic R commands, while more complicated models will use a variety of commands from specific R model packages, as well as general modeling environments NIMBLE (a fast implementation of the BUGS model)and Stan, all run through R. Bayesian I will feature foundational 1 and 2 parameter models, linear regression, and applications to limited variable models (ordered and unordered logit/probit, count data, and multivariate regression). In addition to the scheduled lectures, background material on the models considered (e.g., basics of limited dependent variable models or basic usage of R) will be made available, while most mathematical content will be reserved for auxiliary recordings posted online. Prerequisites: Foundations of Statistical Analysis, or equivalent. 

Instructor: Dr. Shawn Treier

Bayesian Statistics II

This course aims to cover several important advanced applications of the Bayesian approach to statistical inference and estimation in political science. Emphasis will be placed on the understanding the basics and implementation of these models. The statistical environment of R will be used, with specific model commands from R packages, as well as general modelling environments NIMBLE (a fast implementation of the BUGS model) and Stan. Bayesian 2 will feature hierarchical models (simple, linear regression, and more general specifications) and measurement models (factor analysis, IRT, ideal point estimation, LCA, SEMs). In addition to the scheduled lectures, background material on the models considered will be made available, while most mathematical content will be reserved for auxiliary recordings posted online. Prerequisites: Bayesian Statistics I or equivalent. 

Instructor: Dr. Shawn Treier

Critical Discourse Analysis in Political Science 

The aim of this course is to introduce students to the foundations of Critical Discourse Analysis and specifically the Fairclough method. 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 the Fairclough method and how it can be operationalised in political science. The course will conclude by pointing students in the direction of more advanced approaches.

Instructor: Dr. April Biccum

Survey Experiments for Political Science

This course covers the foundations of designing, implementing and analysing basic online survey experiments in political science. It is intended for those with no or limited experience with online survey experiments. Topics covered under survey design include: the inferential logic of experiments, choosing from different types of treatments, designing your experiment to achieve useful results, attention and manipulation checks, ensuring sufficient statistical power. Topics covered under implementation include: piloting / testing, pre-registration, getting the sample you need, soft-launch, and fielding your survey. Topics covered under analysis include: data cleaning and checking, hypothesis testing and data visualization in R, confirmatory and exploratory analysis, and issues of external validity. All examples are drawn from the political science literature.

Instructor: Professor Ben Goldsmith 

Longitudinal Data Analysis (Panel and Time Series data)

This course aims to provide a practical, hands-on introduction to the analysis of longitudinal (repeated cross-sectional 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; and understanding how to interpret the results of these analyses. 

Instructor: TBD

Philosophy and Methods of Political Science

The course is based on Keith Dowding’s book of the same title. In the most general terms, the course tries to provide a philosophically sound justification of both quantitative and qualitative research in terms of the different research questions they adopt. It considers the nature of explanation and the relationship of explanation to prediction, and the relationship of both to forecasting. It looks at the nature of theories about politics. It reconsiders the demand for causal inference and causal explanation in political science and examines how description is an important part of explanation. It introduces the type/token distinction and the idea of the granularity of description and explanation. It covers the manner in which causation is approached by quantitative and qualitative methods, suggesting that both have compatible accounts, and the difference between quantitative and qualitative work concerns the nature of the research questions they adopt. It examines the process-tracing account of qualitative research and asks in what ways case studies can test hypotheses drawn from theories. It considers the relationship between empirical generalisations and causal mechanisms and examines the use of models in political science. 

Instructor: Prof. Keith Dowding

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 democratise but not others? What are the causes of war and peace? How do we improve the representation of marginalised 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

From 2026, the ANU Online Summer School in Political Analysis (SSPA) is being run in partnership with the Australian Consortium for Social & Political Research Inc. (ACSPRI) [link: https://www.acspri.org.au/]. The registration fees are based on the ACSPRI short course fees, and there is a discount for participants from ACSPRI Member Institutions [https://www.acspri.org.au/og/members].

Early bird Member: $1,160

Early bird Non Member: $1,980

Early bird full-time student Member: $720

Member: $1,480

Non Member: $2,280

Full-time student Member: $1,280

*ANU Students will receive a 20% discount. Please contact ACSPRI at info@acspri.org.au before enrolling to arrange your discount and payment.

From 2026, the ANU Online Summer School in Political Analysis (SSPA) is being run in partnership with the Australian Consortium for Social & Political Research Inc. (ACSPRI) [link: https://www.acspri.org.au/]. Please direct any queries about registration and payment to ACSPRI at info@acspri.org.au.