Doing Multivariate Analysis (DMA-2)

Prerequisites

The SSRMC Administrator will make all bookings for this module. If you would like to follow the module you must complete the SSRMC skills audit or have successfully completed the Foundations in Applied Statistics (FiAS) and Basic Quantitative Analysis (BQA) modules before a place can be booked for you.

This module is part of the Social Science Research Methods Centre training programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently.

Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software.

To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models.

• Mphil and PhD students from participating departments taking the Social Science Research Methods Centre training programme as part of their research degree

• Completion of Foundations in Applied Statistics and Basic Quantitative Analysis modules (or equivalent level of knowledge, verified by the SSRMC skills audit)
• a University Information Services (Computing) Desktop Services password (http://www.ucs.cam.ac.uk/linkpages/newcomers)
• You must have enrolled for the module's Moodle course page (instructions: http://www.ssrmc.group.cam.ac.uk/ssrmc-modules/moodle)

Number of sessions: 4

• The basic theory and practice of multivariate regression: underlying assumptions; and issues of specification, including dealing with missing values and categorical variables
• Logistic regressions: what are they for? transforming and interpreting logistic coefficients; dealing with nonlinear relationships; basic diagnostic statistics; collinearity

Presentations, demonstrations and practicals

R on MCS

• One online exercise [optional, dependent upon department]
• The SSRMC encourages all students to take the assessment

NB. There are no facilities (eg. toilets/water fountains) at the Biffen Lecture theatres, so please make sure you come prepared.

• To gain maximum benefits from the course it is important that students do not see this course in isolation from the other MPhil courses or research training they are taking.
• Responsibility lies with each student to consider the potential for their own research using methods common in fields of the social sciences that may seem remote. Ideally this task will be facilitated by integration of the SSRMC with discipline-specific courses in their departments and through reading and discussion.

Eight hours over two weeks (4 hours lectures; 4 hours lab)