杏吧论坛

Professor Jouni Kuha

This course is available on the MPhil/PhD in Cities Programme, MPhil/PhD in Data, Networks and Society, MPhil/PhD in European Studies, MPhil/PhD in Health Policy and Health Economics, MPhil/PhD in International Relations, MPhil/PhD in Media and Communications, MPhil/PhD in Social Policy, MPhil/PhD in Social Research Methods, MPhil/PhD in Sociology, MRes/PhD in Management (Employment Relations and Human Resources), MRes/PhD in Management (Marketing), MRes/PhD in Management (Organisational Behaviour) and MRes/PhD in Political Science. This course is available 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/MY551 (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 (MY552A) and again in WT (MY552W). 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.