The University of Southampton

HLTH6111 Applied Quantitative Research Methods

Module Overview

This module will give students a practical understanding of quantitative research, including methodology, methods and analysis. Students will get hands on experience of basic statistical analyses using SPSS including building data bases, producing descriptive statistics, and being able to compare and correlate different units of data.

Aims and Objectives

Module Aims

To acquire a practical understanding of quantitative research (including methodology, methods and analysis).

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

  • Identify and critically appraise quantitative methods and their application, and select those appropriate to specified research questions
  • Demonstrate an ability to define a data requirement, collect, manage and prepare quantitative data
  • Critically analyse data collection approaches relevant for specified research questions and approaches
  • Critically appraise the validity and reliability of quantitative methods and analytical approaches within a research project
  • Use a range of quantitative analysis methods to summarise and analyse quantitative data
  • Demonstrate an ability to interpret and present analysis results and justify the conclusions and recommendations arising


Indicative content for this module may include: • Identification and selection of clinical research questions amenable to quantitative inquiry • Selecting appropriate quantitative methods to address research question(s) • Developing skills in quantitative methods: including data collection, data management and use of statistical software • Understanding the main approaches to quantitative data analysis including: • Review of the normal distribution and confidence intervals • Review of tests of association between two variables, - parametric and non-parametric • Tests of association between multiple variables - correlation and regression • Research design and the analysis of variance • Effect size, statistical power and sample size calculations • Interpretation of quantitative data, including in mixed methods studies • Techniques for presentation, writing-up and publishing findings Practical sessions and summative and formative assignments will provide experience of a range of techniques and approaches to data analysis of a variety of quantitative approaches including randomised controlled trials, single group, pre-post studies, cohort, and case control studies and surveys. Approaches to analysis may include: 1. Issues in data summary, description and presentation 2. Hypothesis testing 3. Regression methods and risk factor identification 4. Analysis of Variance 5. Multivariate methods

Special Features

For features such as field trips, information should be included as to how students with special needs will be enabled to benefit from this or an equivalent experience.

Learning and Teaching

Teaching and learning methods

The module will use interactive learning styles so you will work with facilitators and colleagues in the group. The format of taught sessions may include lectures, tutorials and computing workshops which will draw on expertise and examples of recent and ongoing clinically and health-related research in the University and NHS. Approximately 50% of the course will be practical work, and there will be opportunities for students to discuss and develop their data analysis issues in relation to the dissertation and/or Open module. There will be opportunities for self directed learning, much of which can be focused around the dissertation or Open module. Independent study will be supported by module notes and web resources, including Blackboard.

Wider reading or practice50
Follow-up work50
Completion of assessment task70
Preparation for scheduled sessions50
Practical classes and workshops30
Total study time250

Resources & Reading list

Field, A (2009). Discovering statistics using SPSS. 

Gray, C.D. and Kinnear, P.R. (2012). IBM SPSS 19 Statistics Made Simple. 

Polgar, S. and Thomas, S.A. (2000). Introduction to Research in the Health Sciences. 

Access to software for analysis. SPSS, STATA will be available via departmental/University workstations

Greenhalgh, T (2006). How to read a paper: the basics of evidence-based medicine. 

Access to word processing and internet. 

Dancey, C.P. et al. (2012). Statistics for the Health Sciences. 



Data Analysis


MethodPercentage contribution
Data Analysis  (3500 words) 100%


MethodPercentage contribution
Data Analysis 100%

Repeat Information

Repeat type: External

Share this module Facebook Google+ Twitter Weibo

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.