BM7001 - Scientific Frameworks For Research (2017/18)
|Module specification||Module approved to run in 2017/18|
|Module title||Scientific Frameworks For Research|
|Module level||Masters (07)|
|Credit rating for module||20|
|School||School of Human Sciences|
|Total study hours||200|
|Running in 2017/18||
The module is designed to provide students with an understanding of skills needed for the planning, organisation and practice of research in science. Different analytical approaches to problems will be reviewed together with the need to consider statistics and quality control in the design of projects. Students will consider the impact of appropriate safety, ethical and resourcing implications in the design and operation of a project.
To provide students with the skills needed to analyse and evaluate published scientific research. To review a variety of statistical tests commonly applied to quantitative data sets, together with their respective assumptions and outlines of their underpinning theoretical basis. To consider the impact on scientific research of a variety of issues including safety, ethical and resourcing implications, data protection and intellectual property rights.
Characteristics of the scientific process; controlled experimentation; types of experimental design.
Communicating scientific ideas; research literature and how to use it.
Principles of data analysis: hypothesis forming and testing; statistical modelling and testing
Developing and managing research projects
Professional codes of practice and ethics; intellectual property; data protection; health and safety.
Learning and teaching
Students will develop a knowledge and understanding of the various aspects of research planning and execution through a series of lectures, workshops and IT-based practicals. Throughout the module students will be guided towards the use of IT-based approaches to literature searching, data analysis, and the presentation of results, and will be expected to exploit these fully and effectively in their work. Directed study is provided in the form of practice statistical problems and analyses.
On completion of this module students’ provide an evaluation of how the module allowed them to develop skills such as information technology, organisational skills, team building, communication time management, and working under pressure.
- Develop a deep and systematic understanding of the principles of experimental design, including the need for randomisation, replication and control.
- Demonstrate a basic understanding of statistical modelling; identify appropriate tests for a given dataset; run a variety of statistical tests using a commercially-available software package
- Analyse and critically evaluate published research articles; locate literature relevant to a scientific topic, using appropriate databases and search techniques.
- Appreciate the implications and importance for research of a variety of incidental topics including ethics, health & safety, resourcing, data protection and intellectual property rights.
Assessment is coursework based and comprises two components.
- Knowledge and application of methods of data analysis will be assessed by set statistical problems.
- Students will write an essay comprising a discursive analysis of a research paper they have selected from within their subject speciality. The analysis will consider the problem being addressed by the research in the context of previous knowledge; the design and methodology employed; and the results obtained along with their implications and conclusions. The report should show due consideration, where appropriate, for ethical issues, health and safety, intellectual property rights and other incidental relevant issues.
To pass the module students need to achieve a minimum aggregate mark of 50%.
|Analysis||1, 2, 3, 4|
Where the resource is in the library, the classmark is given in bold. The library contains a large number of texts relevant to all aspects of this module. Those given in the list below are a selection.
For all students:
(i) Research methods and design
Holmes D, Moody P & Dine D. (2006) Research Methods for the Biosciences OUP
Ruxton GD & Colegrave N. (2006) Experimental Design for the Life Sciences. 2nd ed. OUP
(ii) Statistical methods
Freund JE & Simon GA (2007). Modern Elementary Statistics, 12th edn. Prentice-Hall. 519.5 FRE
McClave JT & Sincich T (2003). Statistics (9th Edition). Prentice Hall. 519.5 MCC
Pelosi MK & Sandifer TM (2003). Elementary Statistics. Wiley. 519.5 PEL
(iii) Statistics using SPSS/PASW
Field A (2009). Discovering statistics using SPSS. Sage: London. 519.50285536 FIE
Kinnear PJ & Grey CD (2009). SPSS Made Simple. Hove: Psychology Press. 005.55 KIN
For MSc Biomedical Sciences:
Brown BW & Hollander M (2007). Statistics: A Biomedical Introduction. Wiley.
Corley RB (2005). A Guide to Methods in the Biomedical Sciences. Springer.
Dunn, OJ & Clark VA (2009). Basic Statistics: A Primer for the Biomedical Sciences. 4th Ed. Wiley
Turgeon, ML (2011) Linne & Rinsgrud's Clinical Laboratory Science. 6th Ed. Elsevier
For Sports Therapy courses:
Vincent WJ (2005). Statistics in Kinesiology. 3rd Ed. Human Kinetics. 519.207 VIN
Thomas JR, Nelson JK & Silverman SJ (2005). Research Methods in Physical Activity, 5th Ed. Human Kinetics. 613.71072 THO