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Unit information: Multivariate Statistical Methods in Education in 2021/22

Please note: It is possible that the information shown for future academic years may change due to developments in the relevant academic field. Optional unit availability varies depending on both staffing, student choice and timetabling constraints.

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. Leckie
Open unit status Not open
Pre-requisites

Familiarity with basic descriptive and inferential statistics and the SPSS software to the level covered in EDUCM5504 Statistics in Education / EDUCM0003 Introduction to Quantitative Methods in the Social Sciences. Students should therefore be confident producing and interpreting standard summary statistics, data tabulations, graphs, 95% confidence intervals around sample means, t-tests and correlation coefficients.

Co-requisites

None

School/department School of Education
Faculty Faculty of Social Sciences and Law

Description

The unit will introduce students to a range of multivariate statistical methods widely applied in quantitative educational research. The philosophy of this course is that students will learn more by applying these methods using the SPSS software and to real education and social science datasets than by focusing solely on their underlying statistical theory. Methods covered include: analysis of variance, factor analysis, linear regression, and multilevel modelling.

The unit aims to:

  • Introduce the main multivariate statistical methods used in educational and social research (ANOVA, factor analysis, linear regression, multilevel modelling)
  • provide students with an understanding of when these statistical methods are appropriate and how these methods can contribute to a more robust/powerful evidence base in educational research;
  • provide students with the knowledge and skills to apply these methods to secondary datasets using the SPSS computer package and to interpret their statistical output in relation to specific research questions
  • develop students’ ability to statistically critique published research.

Intended learning outcomes

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

  1. Understand which multivariate statistical methods (ANOVA, factor analysis, linear regression, multilevel modelling) are appropriate in different data situations and for addressing different research questions.
  2. Have a working knowledge of these methods and are able to apply them to data in SPSS and are able to interpret the resulting statistics appropriately.
  3. Are able to select relevant information from SPSS statistical output and present the results in a format appropriate for publication.
  4. Can statistically critique published research which uses these methods.

Teaching details

This unit will be taught using a blended approach consisting of a mixture of synchronous and asynchronous activities including lectures, computer practicals using SPSS software, and critical reading and discussion of published quantitative articles.

Assessment Details

Formative assessment: Regular SPSS worksheets will be provided in which students attempt to apply the taught methods to data and to interpret the results. Annotated answers will then be provided, allowing students to check their progress. (ILO 4)

Students will also have the opportunity to post questions and receive feedback on the unit material, SPSS and general questions about the assignment.

Summative assessment:

The summative assessment consists of a structured assignment with several sections. In each section, students will be required to identify the appropriate method for the given research question and SPSS dataset. They will then have to apply the method and associated descriptive statistics in SPSS, present the results in the format of an academic report, and give a critical interpretation of the findings, reflect on the strengths and weaknesses of their analyses, and suggest potential improvements. (4,000 words equivalent). ILOs 1-3

Reading and References

Field A. (2018) Discovering Statistics Using SPSS (5th Edition). London, Sage

Modules 3 and 5 of the LEMMA 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

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