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: 2013/14 Semester 2, Available to all students (SV1)
|
Learn enabled: Yes |
Quota: 21 |
Web Timetable |
Web Timetable |
| Class Delivery Information |
The course will be run as a three-hour, weekly seminar in the computer laboratory, including an introductory two-hour lecture and discussion, followed by one of two repeat, one-hour, practical exercise workshops, using SPSS (and possibly other statistical software). If demand is high, we will hold the lecture and discussions in the Appleton Tower (Room 2.14), prior to moving to the computer laboratory. The first class will be in the Appleton Tower. |
| Course Start Date |
13/01/2014 |
| Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Seminar/Tutorial Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
176 )
|
| Additional Notes |
|
| Breakdown of Assessment Methods (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| No Exam Information |
Summary of Intended Learning Outcomes
The intended learning outcomes are that students will be able to:
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;
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.
Fielding J and Gilbert N (2006). Understanding Social Statistics (2nd edn). Sage, London.
Leech, NL, Barrett, KC and Morgan, GA (2005). SPSS for Intermediate Statistics: use and Interpretation (2nd edn). 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 edn). Open University Press, Buckingham.
Siegel S and Castellan NJ (1988). Nonparametric statistics. McGraw-Hill, New York, USA.
Stevens J (2009). Applied multivariate statistics for the social sciences (5th edn). Routledge, London.
Tarling, R (2009). Statistical modelling for social researchers: principles and practice. Routledge, 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.
Field A (2009). Discovering statistics using SPSS (3rd edn). Sage, London.
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 | Mr Andrew Macaulay
Tel: (0131 6)51 5067
Email: Andrew.Macaulay@ed.ac.uk |
|
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