Postgraduate Course: Intermediate inferential statistics: testing and modelling (PGSP11321)
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 |
Postgrad (School of Social and Political Studies) |
Other subject area |
None |
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. |
Entry Requirements (not applicable to Visiting Students)
Pre-requisites |
|
Co-requisites |
|
Prohibited Combinations |
|
Other requirements |
None
|
Additional Costs |
None |
Information for Visiting Students
Pre-requisites |
None |
Displayed in Visiting Students Prospectus? |
No |
Course Delivery Information
|
Delivery period: 2011/12 Semester 2, Available to all students (SV1)
|
WebCT enabled: Yes |
Quota: None |
Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
No Classes have been defined for this Course |
First Class |
Week 18, Wednesday, 09:00 - 10:50, Zone: Central. 2.14, Appleton Tower |
Additional information |
A weekly workshop will take place in CMB Basement Computer Lab at the following times after each seminar:
Wednesdays, 11.10 - 12.00
Mondays, 12.30 - 13.30
Students may choose which workshop they would prefer, subject to space constraints. |
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 |
Section A Theoretical considerations
1. Issues in quantitative research and statistical reasoning
2. Design of empirical quantitative investigations
Section B Probability, measurement and comparisons
3. Discrete probability distributions, inc. Normal approximations;
continuity corrections and finite population corrections.
4. Parametric and non-parametric tests
(a) 1 sample
(b) 2 samples - related and independent
(c) More than 2 samples
Section C Explanation and prediction
5. Multiple regression: assumptions and approaches
6. Logistic regression: binary and multinomial
7. Ordinal regression
Section D Comparisons over time
8. Introduction to longitudinal analysis: event history analysis |
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 |
statistical inference testing modelling |
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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copyright 2011 The University of Edinburgh -
1 September 2011 6:40 am
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