Unit information: Multivariate Statistical Methods in Education in 2016/17

Unit name	Multivariate Statistical Methods in Education
Unit code	EDUCM5507
Credit points	20
Level of study	M/7
Teaching block(s)	Teaching Block 2 (weeks 13 - 24)
Unit director	Professor. Liz Washbrook
Open unit status	Not open
Pre-requisites	Familiarity with basic statistics and the SPSS software (as covered in EDUCM0003 Introduction to Quantitative Methods in the Social Sciences / EDUCM5504 Statistics in Education, but students may have covered the 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 multivariate 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, simple and multiple linear regression, multivariate techniques of factor analysis and multilevel modelling.

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;
• 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.

Intended Learning Outcomes

Upon successful completion of this unit students will be able to demonstrate that they:

1. Understand which complex quantitative modelling methods are appropriate in a given situation.
2. 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.
3. Understand the key statistical issues associated with hierarchical data structures and how multilevel modelling can be used to address these issues.
4. Are able to select relevant information from statistical output and present the results in a format appropriate for publication.

Teaching Information

Lectures, computer practicals using SPSS software, critical reading and discussion of published quantitative articles

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, allowing them to check their progress.

Summative assessment:

Students will provided with a selection of prepared datasets and/or statistical outputs. In a structured assignment with a number of sections, students 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, present the results in the format of an academic report, and give a critical interpretation of the findings (4,000 words). ILOs 1-4.

Reading and References

Field A. (2013) Discovering Statistics Using SPSS (4th Edition) London, Sage

Miles, J. & Shevlin, M. (2001). Applying Regression & Correlation: A Guide for Students and Researchers. Sage Publications Ltd.

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

Taylor, Alan (2004). A Brief Introduction to Factor Analysis. http://www.psy.mq.edu.au/psystat/other/FactorAnalysis.PDF