Applied Quantitative Research Methods

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

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

Indicative content for this module includes:

• 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:

1. Review of the normal distribution and confidence intervals

2. Review of tests of association between two variables, - parametric and non-parametric

3. Tests of association between multiple variables - correlation and regression

4. Research design and the analysis of variance

5. Effect size, statistical power and sample size calculations

• Interpretation of quantitative data
• 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

6.Meta-analyses

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.

Resources & Reading list

Textbooks

Christine P. Dancey; John Reidy; Richard Rowe (2012). Statistics for the Health Sciences: A Non-Mathematical Introduction. London; Thousand Oaks, Calif: SAGE Publications LTD.

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Plan

• Assessment Type: Formative
• Feedback: Feedback from module leader
• Final Assessment: No
• Group Work: No

Summative

This is how we’ll formally assess what you have learned in this module.