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 |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | 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. Students will be shown techniques for data reduction and ways to explore the dimensionality in data for potential production of indexes. A number of approaches to regression under different conditions will be considered in depth. |
| Course description |
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 Data reduction
5. Principal components analysis / Factor analysis
Section D Explanation and prediction
6. Multiple regression: assumptions and approaches
7. Logistic regression: binary and multinomial and ordinal
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2015/16, Available to all students (SV1)
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Quota: 30 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| 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 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Assessment will take the form of practical exercises, using statistical software. |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand how to design research to investigate causal and explanatory relationships with quantitative data
- Understand the implications of various levels of data measurement and their related probability distributions
- Demonstrate ability to understand and to solve problems of an inferential nature based on symmetric and asymmetric relationships
- Gain proficiency in the use of statistical software to analyse multivariate data
- Interpret and communicate quantitative solutions in their applied context
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Reading List
Core texts:
Argyrous G (2011). Statistics for research: with a guide to SPSS (3rd edn). Sage, London.
Congdon P (2005). Bayesian models for categorical data, Wiley, Chichester.
Field A (2013). Discovering statistics using SPSS (4th edn). Sage, London.
Hair JF, Black WC, Babin BJ, Anderson RE and Tatham RL (2013). Multivariate data analysis, (7th edn). Prentice-Hall, London.
Tabachnick BG and Fidell LS (2013). Using multivariate statistics, (6th edition), Pearson International, Harlow.
General recommended readings:
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.
Treiman D (2009). Quantitative data analysis: doing social research to test ideas. Jossey Bass, USA.
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Additional Class Delivery Information |
The course will be run as a three-hour, weekly seminar in a lecture room and a computer laboratory, including an introductory lecture and discussion, followed by practical exercise workshops, using SPSS (and possibly other statistical software). |
| Keywords | statistical inference testing modelling reduction dimensions |
Contacts
| Course organiser | Prof 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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