Professor Jouni Kuha

This course is available on the Global MSc in Management, Global MSc in Management (CEMS MIM), Global MSc in Management (MBA Exchange), MPhil/PhD in Demography/Population Studies, MSc in Applied Social Data Science, MSc in Comparative Politics, MSc in Environmental Policy and Regulation, MSc in European and International Politics and Policy, MSc in European and International Politics and Policy (LSE and Bocconi), MSc in European and International Politics and Policy (LSE and Sciences Po), MSc in European and International Public Policy (LSE and Bocconi), MSc in European and International Public Policy (LSE and Sciences Po), MSc in Gender (Research), MSc in Human Geography and Urban Studies (Research), MSc in International Migration and Public Policy, MSc in International Migration and Public Policy (LSE and Sciences Po), MSc in International Political Economy (Research), MSc in International Relations (Research), MSc in International Social and Public Policy (Research), MSc in Local Economic Development, MSc in Political Science (Conflict Studies and Comparative Politics), MSc in Political Science (Political Science and Political Economy), MSc in Public Administration and Government (LSE and Peking University), MSc in Public Policy and Administration, MSc in Social Research Methods and MSc in Urban Policy (LSE and Sciences Po). This course is available with permission as an outside option to students on other programmes where regulations permit.

This course is not controlled access. If you register for a place and meet the prerequisites, if any, you are likely to be given a place.

The course assumes a good working knowledge of basic descriptive statistics and statistical inference, to the level covered on a standard introductory statistics course such as MY451 (Introduction to Quantitative Analysis). Some prior familiarity with linear regression modelling will also be useful.

The course provides an introduction to statistical regression modelling and different types of regression models that are commonly used in the social sciences. The main topics covered are linear regression models, binary logistics models for dichotomous outcomes, multinomial and ordinal logistic models for polytomous outcomes, and Poisson and negative binomial regression models for counts. Examples are drawn from different social sciences. The course includes computer classes, where the R software is used for computation. Prior knowledge of R is not required.

This course is delivered through a combination of classes and lectures totalling a minimum of 20 hours across Autumn Term.

The course runs twice per year: in AT (MY452A) and again in WT (MY452W). The content of the course, and the method of assessment, is exactly the same in each term.

This course has a Reading Week in Week 6 of AT. 

Weekly multiple-choice quizzes on Moodle, with feedback on the answers.

• A course pack will be available for download online.
• Gelman, A., Hill, J. & Vehtari, A. (2022). Regression and Other Stories. Cambridge University Press.
• Agresti, A. (2018). Statistical Methods for the Social Sciences. Pearson Education Limited.
• James, G., Witten, D., Hastie, T., and Tibshirani, R. (2021). An Introduction to Statistical Learning with Applications in R. Springer.

Exam (100%, duration: 2 hours) in the spring exam period.