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Mon 30 Jan 2017
09:00 - 18:00
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Provided by: Social Sciences Research Methods Programme


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Doing Multivariate Analysis (DMA Intensive)
Prerequisites

Mon 30 Jan 2017

Description

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.

Bookings

Before a place can be booked for them, all students wishing to book a place on this module must have either:

OR


Students that have already completed the SSRMC Skill Check may have had a place booked for them by their Department. Students can check this by typing their CRSid into the search box at the very top right of this page, hitting the enter key then clicking on their name. This will show all module(s) that they are booked onto, as applicable.

Students for whom this module is not compulsory can make a booking via the Basic Statistics Stream Booking Form on the SSRMC website.

In cases where you have a problem or a clash, please contact the SSRMC Administrator who will try to help you.

Target audience

This module is designed for MPhil and PhD students as part of the Social Science Research Methods Centre (SSRMC) training programme - 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.

Prerequisites
  • Successful completion of Foundations in Applied Statistics and Basic Quantitative Analysis modules, or equivalent level of knowledge, verified by the SSRMC Skill Check
  • Enrolment on this module's Moodle course page: How to Enrol on Moodle
  • To use the Titan Teaching Room computers you must bring your password for the Desktop Services system. Please note, your password for the Desktop Services system is distinct from your Raven/department/email password. If you are uncertain about this you are advised to go to the University Computing Service Helpdesk before the first day of class or find out more on the UCS Newcomers page.
Sessions

Number of sessions: 2

# Date Time Venue Trainer
1 Mon 30 Jan 2017   09:00 - 13:00 09:00 - 13:00 8 Mill Lane, Lecture Room 4 map Dr M.J. Ramsden
2 Mon 30 Jan 2017   14:00 - 18:00 14:00 - 18:00 Titan Teaching Room 1, New Museums Site map Dr M.J. Ramsden
Topics covered
  • 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
Format

Presentations, demonstrations and practicals

Taught using

Stata on MCS

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

All students are expected to give feedback for each module they take...

At the end of each module, students will be sent a link to a very short evaluation form. They will also be able to find this link on the Moodle page for their course. The survey takes a few minutes to fill in, and can even be done on a mobile phone. Students that do not respond to the survey the first time, will receive regular automated reminders until the survey is completed.

Students will not be given certification or proof of attendance for any module for which they have not provided feedback.

Notes
  • 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.
Duration

8 hours - A morning lecture and an afternoon lab session

This is an intensive, one-day module

Frequency

Once a year

Related courses
Theme
Basic Statistics Stream

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