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Member rate £572.50
Non-Member rate £1,145.00
Early bird discount available until 14 December 2026.
Save £45.00 Loyalty discount applied automatically*
Save 5% on each additional course booked
* If you attended a qualifying previous Methods School in 2026, you qualify for £45.00 off your course fee.
Date: Monday 22 – Friday 26 February 2027
Time: 13:30 – 16:30 CET
This interactive course uses advanced pedagogical tools and is designed for master's-level students and above. It runs as three-hour live sessions on Zoom over five days, with course materials and pre-course readings available on our Learning Management System at least two weeks before the start. Office hours are also available for one-to-one consultations on your own project.
The course is limited to 18 participants, ensuring that the teaching team can address the individual needs of each participant.
In this course, you will learn how to analyse survey item batteries (sets of related questions) using latent variable models, focusing on factor analysis and item response theory (IRT). These methods allow you to measure latent constructs, such as political attitudes, preferences and values, based on responses to multiple survey items. We will begin with exploratory and confirmatory factor analysis for continuous indicators before moving to IRT models for binary and ordinal responses, extending the latent variable framework to categorical items. We will then explore explanatory IRT models, which incorporate covariates to investigate differences in latent traits and item responses. You will also learn how to make informed modelling decisions and gain an introduction to measurement invariance and differential item functioning (DIF).
Throughout the course, you will apply the methods to survey data through hands-on exercises in R. By the end, you will be able to specify, estimate and interpret latent variable models and select appropriate approaches for different types of survey data and research questions.
3 ECTS credits awarded for engaging fully in class activities.
1 additional ECTS credit awarded for completing a post-course assignment.
Ingrid Mauerer is a Distinguished Researcher (EMERGIA) at the University of Málaga. She received her PhD in Political Science and Statistics from LMU Munich. Her research focuses on the statistical modelling of categorical data, particularly discrete choice models and item response theory, to investigate substantive questions related to political behaviour, preference formation and attitudes. She has extensive experience in teaching quantitative methods. Her work has been published in journals such as European Economic Review, European Political Science Review, Journal of Choice Modelling, Political Analysis, Statistical Modelling, Statistical Methods & Applications, Sociological Methods & Research, Survey Research Methods and West European Politics, and she has received the Political Analysis Editors’ Choice Article Award.
Many concepts of interest in survey research, such as political attitudes, preferences and values, cannot be observed directly. Instead, researchers use responses to multiple survey items to measure these latent constructs. Factor analysis and item response theory (IRT) provide statistical frameworks for modelling the relationship between observed responses and underlying latent variables.
In this course, you will learn how these approaches are connected and how to apply them to different types of survey data. We will start with exploratory and confirmatory factor analysis for continuous indicators and then move to IRT models for binary and ordinal responses. We will also consider explanatory IRT models and discuss how to make appropriate modelling decisions.
Throughout the course, you will work with survey data in R to gain practical experience in specifying, estimating and interpreting latent variable models.
We will begin by introducing the basic concepts of latent variable modelling and the relationship between observed indicators and underlying latent constructs. You will learn about exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) for continuous indicators, including their assumptions, specification, estimation and interpretation. Through practical exercises in R, you will explore the dimensionality of survey item batteries, estimate factor models, and interpret factor loadings and other model results.
We will introduce item response theory (IRT) as an extension of latent variable modelling to categorical responses, focusing on binary items. You will learn how binary IRT models relate item responses to underlying latent traits and how to interpret item and respondent parameters. We will discuss different binary IRT models and their assumptions. In practical exercises using R, you will estimate binary IRT models, examine item characteristics and interpret the results.
We will extend the IRT framework to ordinal survey items, such as questions with Likert-type response scales. You will learn how ordinal IRT models account for ordered response categories and how to interpret their parameters. We will discuss different modelling approaches and their assumptions. Through practical exercises in R, you will estimate ordinal IRT models, examine response probabilities and interpret the results using survey data.
We will introduce explanatory IRT models, which incorporate covariates to investigate variation in latent traits and item responses. You will learn how these models extend standard IRT approaches and how to interpret the effects of explanatory variables. In practical exercises using R, you will specify and estimate explanatory IRT models and interpret their results in relation to substantive research questions.
The final day will focus on modelling decisions when analysing survey item batteries. You will learn how to choose between different factor analysis and IRT models based on the research question, the type of survey data and relevant model assumptions. Through practical exercises in R, you will compare modelling approaches and discuss their interpretation and limitations. We will also briefly introduce measurement invariance and differential item functioning (DIF), highlighting their importance when comparing latent constructs across groups.
For the optional post-course assignment (approximately 25 hours of work), you will conduct an empirical analysis using your own survey data or a dataset provided by the instructor. You will need to formulate a research question involving the measurement of a latent construct, select an appropriate factor analysis or item response theory model, and estimate and interpret the model using R. You will be expected to justify your modelling decisions, discuss the results, and reflect on the assumptions and limitations of your chosen approach. You will submit a short written report together with your R code.
You should have a good understanding of basic statistical concepts and regression analysis, including linear and logistic regression, and some experience of working with R. Familiarity with the basic concepts of factor analysis is helpful, but you don’t need any prior experience with factor analysis or item response theory models.
Install the latest versions of R and RStudio before the course. Both are freely available, and no paid software or accounts are required. Information on specific R packages will be provided before the course.
Allow approximately two hours to review the basic concepts of regression analysis, particularly logistic regression, refresh your R skills and check that R and RStudio are installed and working properly. Any recommended preparatory materials will be provided before the course.
The course includes three hours of live teaching each day for five days, alongside readings and other learning activities.
Once you have registered and paid, you will receive access to our Learning Management System (LMS) approximately two weeks before the course start date. Course materials, including pre-course readings, will be available here. Allow approximately 20 hours per week before the course to review the materials and complete readings and any pre-course tasks.
During the course week, you should allow approximately two to three hours per day for preparation and assignments, in addition to the live teaching sessions.
The course offers the opportunity to earn three ECTS credits. To earn a fourth credit, you will need to complete a post-course assignment involving approximately 25 hours of work.
This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.
By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach prerequisite items. If in doubt, please contact us before registering.