RESEARCH SKILLS IN MEDICAL BIOSCIENCE

SHE Level 5
SCQF Credit Points 15.00
ECTS Credit Points 7.50
Module Code MMC926374
Module Leader Adrian Pierotti
School School of Health and Life Sciences
Subject Biological and Biomedical Sciences
Trimester
  • B (January start)

Pre-Requisite Knowledge

At least a 2:2 honours degree (or equivalent) in a biomedical or biological science discipline.

Summary of Content

Research Skills in Medical Bioscience has three components: Laboratory skills, Employability skills and Experimental Design & Analysis (EDA). The laboratory skills section will focus on key techniques required in medical biosciences in the form of mini projects. Molecular biology, microbiology and microscopy techniques will be combined into structured practicals with the results being analysed and presented in a number of formats. Students will participate in an Employability School to develop their Career Management Skills - consisting of graduate CV writing with additional tutorials on covering letters, psychometric testing, job search and interviews. The emphasis will be on student's devising a career action plan, setting goals and planning ahead for their transition into employment/academia. EDA will develop the philosophy and application of statistical reasoning and statistical analysis methods. Emphasis will be placed on demonstrating how statistical analysis concepts and methods are necessary for the conversion of data into meaningful practical knowledge. EDA will also enhance the student's proficiency in statistical software usage for data presentation, analysis and interpretation and communicating research.

Syllabus

Experimental Design and Analysis Research process and planning principles: Data collection concepts and principles. Generalisation of results. Analysis and critical evaluation of literature; link to the development of a research proposal. Ethical considerations. Exploratory Data Analysis (Descriptive Statistics): Levels of measurement and measurement scales. Techniques of graphical and numerical summary of scientific data. Point and interval estimation. Distribution of data. Reference range calculation. Concepts of accuracy and precision in scientific measurement. Design of Experiments: Research process. Importance of design for gathering relevant scientific information; relation to data analysis. Inference: Statistical inference concepts. Null and experimental hypotheses. Significance level. Studies comparing two samples: Design principles. Diagnostic checking for parametric assumptions. Parametric and non-parametric tests for independent and paired sample studies. Studies comparing more than two samples: Principle of ANOVA. Analysis mechanisms: inferential tests, follow-up analysis. Non-parametric alternative. Method comparison: Correlation; Linear regression analysis; Bland Altman analysis; Diagnostic sensitivity & specificity of a new test (ROC analysis) Laboratory Skills A range of practical techniques combined into three structured mini-projects focusing on molecular biology (nucleic acid isolation and purification, cloning and analysis of nucleic acids), microbiology ( ) and microscopy ( )

Learning Outcomes

On successful completion of this module the student should be able to:1. Recognise the importance of accuracy, reproducibility and documentation in laboratory work.2. Appraise, interpret and present data in an appropriate format.3. Develop a careers action plan in preparation for graduate transition and employment.4. Critically evaluate the use and application of statistical methods within laboratory-based research.5. Perform and interpret parametric and non-parametric tests appropriate to sample data.

Teaching / Learning Strategy

This module uses a blended learning approach with lectures, computer labs, tutorials, practical labs and online learning via GCULearn. Experimental Design and analysis Online resources will include exercises involving the use of a suitable statistical package to consolidate the student's software skills and aid their understanding of how to present and summarise practical, laboratory-generated, research data and perform appropriate statistical analysis. The acquisition of these skills will be assessed in a coursework exercise worth 50% of the module mark, using sample data. Students can gauge their understanding by comparing their formative work to model answers made available on GCULearn. Online and class-based discussions will also be used to enhance reflective learning and provide support and group feedback. Individual, written, formative feedback will be provided on an exercise designed to underpin the summative statistics assignment (coursework 1). Laboratory Skills Practical work will be completed individually within a laboratory environment and will be fully supported by academic staff, PhD student demonstrators and technical staff. The techniques will be explained by tutorials and hands on demonstration. A laboratory book will be maintained and results analysed and presented by various means including oral and written reports. Continuous feedback on laboratory performance will be provided by staff. Career Management Skills A week long intensive career and employability programme covering key aspects of career management skills to support student transition from university into employment or academia. Reflective practice, along with workshops and networking sessions will be scheduled throughout the w programme. Through lectures, tutorials, workshops and online activities, sessions will utilise a blended learning approach of both face-to-face and online delivery. Key professionals from industry, academia and the careers service will contextualise the current graduate employment market for Medical Bioscientists.

Indicative Reading

Daniel, W. W. & Cross C.L. (2012) Biostatistics: A Foundation for Analysis in the Health Sciences, 10th Ed. Wiley. Field, A. (2013) Discovering Statistics Using SPSS, 4th Ed. Sage Publications. Gray, C.D. & Kinnear, P.D. (2012) IBM SPSS Statistics 19 Made Simple. Talyor & Francis. Holmes, D., Moody, P., Dine, D. & Truman, L. (2016) Research Methods for the Biosciences. Oxford University Press. Jones, R. & Payne, B. (1997) Clinical Investigation and Statistics in Laboratory Medicine. ACB Venture Publications. O'Leary, Z. (2017): The Essential Guide to Doing Research 3rd Edition. SAGE, London. Ruxton, G. D. & Colegrave, N. (2016) Experimental Design for the Life Sciences, 4th Ed. Oxford University Press. Bassot, B. (2016) The Employability Journal . London: Palgrave Bassot, B. (2017) The Reflective Journal . 2nd edition. London: Palgrave

Transferrable Skills

Reading, writing and communication skills Interpretation and presentation Independent study skills Problem solving Logical thinking Numeracy ICT skills Information Literacy Working with others Ability to use numerical data Ability to apply knowledge

Module Structure

Activity Total Hours
Independent Learning (FT) 69.00
Practicals (FT) 40.00
Lectures (FT) 6.00
Assessment (FT) 15.00
Tutorials (FT) 20.00

Assessment Methods

Component Duration Weighting Threshold Description
Course Work 03 n/a 0.00 50% Employability portfolio (Pass/Fail)
Course Work 02 n/a 50.00 45% Lab skills portfolio
Course Work 01 n/a 50.00 45% Data Analysis and Report