The University of Southampton
Courses

RESM6207 Quantitative Methods IIa (EDUC)

Module Overview

More specifically, the module covers three main topics: multiple linear regression, logistic regression and other models for categorical data, and the data reduction methods of principal component analysis and factor analysis. The module aims to provide a firm understanding of the use of these methods for the analysis of quantitative data and their application in a range of disciplinary contexts. The emphasis will be on the practical application of these statistical techniques to quantitative data using the statistical software SPSS and then interpreting and presenting the results.

Aims and Objectives

Module Aims

This module builds upon the material learnt in RESM6004 Quantitative Methods I. The aim is to introduce you to some commonly used statistical methods for analysing data involving two or more variables per observation.

Learning Outcomes

Transferable and Generic Skills

Having successfully completed this module you will be able to:

  • Problem analysis and problem solving;
  • Statistical computing;
  • Data handling and manipulation;
  • Report writing
Learning Outcomes

Having successfully completed this module you will be able to:

  • Demonstrate knowledge and understanding of the basic ideas behind several commonly used statistical methods for analysing multivariate data – multiple linear regression, logistic regression and other models for categorical data, principal components analysis, and factor analysis
  • Analyse quantitative data by applying these methods using SPSS and interpret the findings
  • Write statistical reports based on these analyses

Syllabus

The module is split into three parts. After a brief review of simple linear regression, the first part focuses on multiple linear regression. Indicative topics include: model interpretation, assumptions of multiple regression, hypothesis testing, model selection, handling of categorical explanatory variables, interactions, and variable transformations. The second part of the course covers logistic regression and other models for categorical data. Indicative topics include: binary response variables, the linear probability model, probabilities and odds, the logistic regression model, model interpretation, model selection, multinomial logistic regression, models for ordinal data, and log-linear models. The final part of the module covers the data reduction methods of principal component analysis and factor analysis.

Special Features

This module is one of the ESRC DTC’s seven research methods modules. The modules are taught by leading experts from across Academic Units and Faculties. This co-ordinated research methods training programme brings together students from across the faculties of Human and Social Sciences, Humanities and Health Sciences. The first and third parts of this module are taught jointly with RESM6007B

Learning and Teaching

Teaching and learning methods

Teaching will be through a combination of multidisciplinary lectures, tutorials and computer workshops. Learning activities will include learning in lectures, which will cover explanations of the statistical techniques and their use, discussing problems during the tutorials, as well as by independent study. The computer workshops will provide hands-on experience of the analysis of data and the application of the techniques introduced in the lectures using SPSS.

TypeHours
Independent Study150
Total study time150

Resources & Reading list

Other. A variety of relevant e-learning resources are available on Blackboard. These include recordings of lectures, exercise/tutorial sheets, computer workshop sheets, datasets for analysis, reading lists, and links to online statistics textbooks and other useful websites. Resources to support the production of these blended learning materials will be made available by the Doctoral Training Centre

Field, A. (2009). Discovering Statistics Using SPSS. 

Agresti A (2007). An Introduction to Categorical Data Analysis. 

Kleinbaum, D. G., Kupper, L. L., Muller, K. E. and Nizam, A. (1998). Applied Regression Analysis and Other Multivariable Methods. 

Hosmer, D. W. and Lemeshow, S. (2000). Applied Logistic Regression. 

SPSS. You will require access to SPSS, which is available on the University’s computer workstations and can be downloaded to your own computer for use with your studies

Manly, B. F. J. (2005). Multivariate Statistical Methods: a Primer. 

Assessment

Assessment Strategy

The module will be assessed by one 2,500-3,000 word coursework assignment. The coursework will require you to write a report on the analysis of a given dataset using SPSS and the application of the statistical methods covered during the module to investigate a substantive problem. In addition, formative assessment is based on individual work that can be completed in part during the computer workshops and tutorials

Summative

MethodPercentage contribution
Assignment  (3000 words) 100%

Referral

MethodPercentage contribution
Coursework assignment(s) 100%

Repeat Information

Repeat type: Internal & External

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