This 15 credit module, Research Methods, is concerned with the careful use of scientific method, qualitative research and the application of statistical techniques in empirical research. Research governance including ethics, health and safety, use of human tissue, animal welfare and genetic medication, will be delivered. It covers the principles of data quality and method validation.

The module aims to develop research skills required to deliver evidence-based analysis that underpins each core module, where research governance plays a central role.

The statisticians on the module are applied statisticians by education, training, qualification and experience. Similar subject knowledge experts deliver the qualitative components, systematic review training, and in research ethics. 

On successful completion of this module, you will be able to:

• apply advanced statistical techniques in empirical research using modern day software.
• evaluate model diagnostics to inform empirical model building.
• interpret and explain a wide variety of empirical statistical models in different contexts.
  

Careers / Further study

This module can contribute towards the MSc Health Technology.

Content

The module syllabus typically includes the following:

• Advanced modelling techniques : Binary, ordinal and nominal logistic regression models: application, theoretical underpinning, model diagnostics
• Discriminant analysis: applications and interpretation, theoretical underpinning, model diagnostics
• Multivariate analysis of variance
• Survival analysis
• Biomedical research techniques: Principles of experimental designs relating to medical studies; determination of sample size for a given study; measures of reliability; Cohen's kappa; analysis of survival data including the proportional hazards survival model, estimation of survival probabilities, Kaplan-Meier survival curves, log rank tests.
• Industrial studies: Two-level full and fractional factorial designs, central composite and rotatable designs and process optimisation; Taguchi methods and their role in product design and quality improvement; missing data.
• Meta-analysis: Introduction to meta-analysis; systematic reviews, publication bias, effect sizes, random and fixed effects models; examples taken from the empirical literature.
• Research governance: Overview including bioethics and ethical standard frameworks and committees. 

Learning and Teaching

The statisticians on this module are applied statisticians by education, training, qualification and experience.  Similar subject knowledge experts deliver the qualitative components, systematic review training, and in research ethics.  

This module will be delivered in discrete sections, following the subject areas outlined in the syllabus. Each topic area will be introduced with underpinning lectures followed by a series of tutorials where extensive use of case studies will be made.

Tutorials will use indicative lists of questions to guide your learning. It is expected that the tutorial work will be completed before the tutorial. 

Assessment

Assessment for this module comprises a written assignment (15 pages maximum): Statistical modelling of data and design concepts.

The assessment is designed to develop your understanding of how quantitative data is currently used in the Healthcare Technology field and will test statistical modelling of data and second design concepts. This piece of coursework will involve a written report featuring a comprehensive statistical analysis designed to showcase core learnings using a large real-world dataset within the healthcare field.

You are supported through dedicated guidance, access to relevant resources, and opportunities for feedback to ensure they succeed in applying statistical modelling effectively.