This information is for the 2020/21 session.
Teacher responsible
Dr. Daniele Fanelli and Dr. Indraneel Sircar
Availability
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 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 (Information Systems and Innovation), 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.
Research students where programme regulations allow.
Pre-requisites
Students are required to have completed MY451/MY551 Introduction to Quantitative Analysis or an equivalent level statistics course.
Course content
The course is designed for students with a good working knowledge of elementary descriptive statistics; sampling distributions; one and two sample tests for means and proportions; correlation and the linear regression model with one or more predictor variables. The course is concerned with deepening the understanding of the generalized linear model and its application to social science data. The main topics covered are linear regression modelling and binary, multinomial and ordinal logistic regression.
Teaching
This course is delivered through a combination of classes and lectures totalling a minimum of 20 hours per term. This year, some or all of this teaching will be delivered through a combination of virtual classes and flipped-lectures delivered as short online videos.
This course is given twice per session, starting in the first week of each of the MT and LT. Students must either register for MY552M which is taught in Michaelmas Term, or MY552L which is taught in Lent Term.
This course has a Reading Week in Week 6 of MT and LT.
Formative coursework
Self-guided computer exercises to be completed before weekly classes for discussion.
Indicative reading
A Agresti & B Finlay, Statistical Methods for the Social Sciences. A course pack will be available for download online. Additional reading will be recommended.
Assessment
Take-home assessment (80%) in the ST.
Continuous assessment (20%) in the MT and LT.
(Homework and participation will constitute 20% of the final overall mark).
Key facts
Department: Methodology
Total students 2019/20: Unavailable
Average class size 2019/20: Unavailable
Value: Half Unit
Important information in response to COVID-19
Please note that during 2020/21 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the situation of students in attendance on campus and those studying online during the early part of the academic year. For assessment, this may involve changes to mode of delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.