Postgraduate Course: Intermediate inferential statistics: testing and modelling (PLIT11002)
Course Outline
| School |
School of Social and Political Science |
College |
College of Humanities and Social Science |
| Course type |
Standard |
Availability |
Available to all students |
| Credit level (Normal year taken) |
SCQF Level 11 (Postgraduate) |
Credits |
20 |
| Home subject area |
Politics |
Other subject area |
Social Policy |
| Course website |
None
|
Taught in Gaelic? |
No |
| Course description |
This course will provide an extension beyond the foundation statistics courses to allow students to acquire a deeper and more extensive knowledge and understanding of how to test and model data at various levels of measurement for the purpose of making statistical generalisations. |
Information for Visiting Students
| Pre-requisites |
None |
| Displayed in Visiting Students Prospectus? |
Yes |
Course Delivery Information
|
| Delivery period: 2010/11 Semester 2, Available to all students (SV1)
|
WebCT enabled: Yes |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| Central | Lecture | Main lecture/seminar | 1-10 | | | 09:00 - 10:50 | | | | Central | Laboratory | Computer workshop alternative to Mondays | 1-10 | | | 11:10 - 12:00 | | | | Central | Laboratory | Computer workshop alternative to Wednesdays | 2-11 | 16:10 - 17:00 | | | | |
| First Class |
Week 1, Wednesday, 09:00 - 10:50, Zone: Central. 3.D01, Forrest Hill |
| Additional information |
There will be a 1 hour computer workshop each week to practice problem solving using SPSS or Stata, which is labelled 'laboratory' above. |
| No Exam Information |
Summary of Intended Learning Outcomes
1. Understand the implications of various types of data measurement and related probability distributions.
2. Understand how to design research to investigate causal and explanatory relationships.
3. Understand the assumptions underpinning various statistical techniques based on asymmetric relationships.
4. Demonstrate ability to solve problems of an inferential nature.
5. Gain proficiency in the use of statistical software to analyse data; i.e. SPSS and Stata.
6. Interpret quantitative solutions in their applied context. |
Assessment Information
| Take-home exercises at the conclusion of the course. These will require the use of computer software, as appropriate, and test knowledge and understanding across the whole course |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Not entered |
| Transferable skills |
Not entered |
| Reading list |
Not entered |
| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords |
inference; generalisation; parametric; non-parametric; ANOVA; multiple regression; logistic regressi |
Contacts
| Course organiser |
Dr Andrew Thompson
Tel: (0131 6)51 1562
Email: Andrew.Thompson@ed.ac.uk |
Course secretary |
Mrs Gillian Macdonald
Tel: (0131 6)51 3244
Email: gillian.macdonald@ed.ac.uk |
|
copyright 2011 The University of Edinburgh -
13 January 2011 6:38 am
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