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 |
The course is designed for those students who have already acquired a basic understanding of statistics; for example, through the Core Quantitative Data Analysis course run in the first semester. Its aim is to extend and deepen understanding of statistical approaches to data analysis through an appreciation of the process of statistical reasoning prior to designing appropriate quantitative analysis of data. Attention will be given to discrete probability distributions, including Normal approximations, as well as a range of parametric and nonparametric tests. A number of approaches to regression under different conditions will be considered in depth. There will be an introduction to understanding changes over time through event history (survival) analysis. |
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
| Assessment will take the form of practical exercises, using statistical software, and a critique of published literature. |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Not entered |
| Transferable skills |
Not entered |
| Reading list |
General recommended readings:
de Vaus D (2002). Analysing Social Science data: 50 key problems in data analysis, Sage, London.
Field A (2005). Discovering statistics using SPSS : (and sex, drugs and rock &©n&© roll) (2nd edn), Sage, London.
Fielding J and Gilbert N (2006). Understanding Social Statistics (2nd edition), Sage, London.
Leech, NL, Barrett, KC and Morgan, GA (2005). SPSS for Intermediate Statistics: use and Interpretation (2nd edition), Lawrence Erlbaum Associate, New Jersey, USA.
Moore DS (1997). Statistics, concepts and controversies, (4th edn), Freeman, New York, USA.
Pallant J (2004). SPSS Survival Manual (2nd edition), Open University Press, Buckingham.
Siegel S and Castellan NJ (1988). Nonparametric statistics, McGraw-Hill, New York.
Stevens J (2009). Applied multivariate statistics for the social sciences (5th edition). Routledge, London.
Tarling, R (2009). Statistical modelling for social researchers : principles and practice, Routledge, London.
Wright DB (1997). Understanding statistics: an introduction for the social sciences, Sage, London.
Core texts:
Argyrous G (2005). Statistics for research: with a guide to SPSS (2nd edition), Sage, London.
Congdon P (2005). Bayesian models for categorical data, Wiley, Chichester.
Tabachnick BG and Fidell LS (2007). Using multivariate statistics, (5th edition), Pearson International, Harlow.
|
| 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 |
|
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31 January 2011 8:15 am
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