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| Unit name |
Advanced Quantitative Methods in the Social Sciences |
| Unit code |
EDUCM0054 |
| Credit points |
20 |
| Level of study |
M/7
|
| Teaching block(s) |
Academic Year (weeks 1 - 52)
|
| Unit director |
Professor. Liz Washbrook |
| Open unit status |
Not open |
| Pre-requisites |
Familiarity with basic statistics (as covered in Introduction to Quantitative Methods, but students on different courses may have covered with required material on similar statistics units elsewhere)
|
| Co-requisites |
None
|
| School/department |
School of Education |
| Faculty |
Faculty of Social Sciences and Law |
Description including Unit Aims
The unit will introduce students to the uses of and interpretations of statistical methods. The philosophy of the course is that students learn more about inferential statistics by carrying them out using a real data set than by trying to learn statistical theory from first principles. Statistics covered include: analysis of variance and covariance, simple and multiple linear regression, multivariate techniques of factor analysis and multilevel modelling. The course will build in complexity, in terms of both the techniques covered and the datasets to which they are applied, culminating in analysis of realistically complex and hierarchical data from educational settings (e.g. pupils nested within classrooms, departments, schools and LEAs).
The unit aims:
- To provide students with an understanding of when complex quantitative modelling methods are appropriate and how these can contribute to a more robust/powerful evidence base in educational research.
- To provide students with the knowledge and skills to apply a range of statistical modelling techniques to large-scale secondary datasets using the SPSS and Mlwin computer packages and to interpret statistical output in relation to specific research questions.
- To provide students with a statistical understanding of the complex nature of educational data, and, more specifically, of the modelling of educational outcomes such as examination and assessment results, in relation to a variety of explanatory factors comprising for example, educational processes, inputs and context.
Intended Learning Outcomes
Students will be able to demonstrate that they:
- Understand the use and value of secondary analyses of existing data sets in education and how educational outcomes can be modelled using quantitative approaches.
- Understand which complex quantitative modelling methods are appropriate in a given situation.
- Have a working knowledge of a range of essential multivariate inferential statistics available on SPSS (multiple regression, ANOVA and factor analysis), and be able to apply and interpret these statistics appropriately.
- Have a working knowledge of basic multilevel modelling techniques using Mlwin computer software, and be able to apply and interpret these statistics appropriately in the context of large-scale hierarchical datasets.
Teaching Information
Lectures and computer practicals using SPSS and Mlwin software
Contact Hours Per Week
2 hours x 10 weeks = 20 hours
Assessment Information
Formative assessment:
Weekly worksheets will be provided in which students attempt a statistical analysis task. Annotated answers to the original worksheet will then be provided for students the following week, allowing them to check their progress.
Summative assessment:
Structured assignment consisting of analysis of several secondary data sets. In each of 3 sections, the student will be required to identify the appropriate method for statistical analysis for a given research question and dataset, conduct that analysis in an appropriate software package and give a critical interpretation of the results (4,000 words).
Reading and References
- Bryman, A. and Cramer, D. (2011) Quantitative Data Analysis with SPSS 17, 18 and 19: a guide for Social Scientists. London: Routledge
- Erickson, B.H. and Nosanchuk. T.A. (1992) Understanding Data, Buckingham: Open University Press
- Field A. (2013) Discovering Statistics Using SPSS (4th Edition) London, Sage
- Goldstein, H (1997) Methods in School Effectiveness Research, School Effectiveness & School Improvement, 8, (4): 369-395
- Kreft, IGG and De Leeuw, J (1998) Introducing Multilevel Modelling. London: Sage.
- Rasbash et al (2015) A users guide to Mlwin, v2.32. Centre for Multilevel Modelling, University of Bristol.
- Wright, D.B. (1997) Understanding Statistics: An introduction for the social sciences, London: Sage
- Modules 1 to 5 of the LEMMA on-line multilevel modelling on-line course: http://www.bristol.ac.uk/cmm/learning/online-course/course-topics.html
