module specification

BM7001 - Scientific Frameworks For Research (2020/21)

Module specification Module approved to run in 2020/21
Module title Scientific Frameworks For Research
Module level Masters (07)
Credit rating for module 20
School School of Human Sciences
Total study hours 200
54 hours Scheduled learning & teaching activities
146 hours Guided independent study
Assessment components
Type Weighting Qualifying mark Description
Coursework 50%   Statistics problems
Coursework 50%   Essay (2000 words)
Running in 2020/21
Period Campus Day Time Module Leader
Autumn semester North Tuesday Morning
Spring semester North Wednesday Morning

Module summary

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.


LO 1 - 4

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


Balance of independent study and scheduled teaching activity

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.

Learning outcomes

On successful completion of this module students will:
1. Develop a deep and systematic understanding of the principles of experimental design, including the need for randomisation, replication and control.
2. 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
3. Analyse and critically evaluate published research articles; locate literature relevant to a scientific topic, using appropriate databases and search techniques.
4. Understand  the implications and importance for research of a variety of incidental topics including ethics, health & safety, resourcing, data protection and intellectual property rights.

Assessment strategy

Assessment is coursework based and comprises two components.
1. Knowledge and application of methods of data analysis will be assessed by set statistical problems.
2. 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%.

Component    Learning outcomes
Statistics problems   2
Analysis             1, 2, 3, 4


Dunn, OJ Clark VA (2011).  Basic Statistics: A Primer for the Biomedical Sciences.
Holmes D, Moody P & Dine D. (2016) Research Methods for the Biosciences OUP
Ruxton GD & Colegrave N. (2016) Experimental Design for the Life Sciences.
Turgeon, ML (2015) Linne & Rinsgrud's Clinical Laboratory Science. Elsevier

Statistical methods
Freund JE & Simon GA (2013). Modern Elementary Statistics, Prentice-Hall.
Rowntree D (2018) Statistics without Tears: An Introduction for Non-Mathematicians Penguin.
Graham A (2017) Statistics: An Introduction: Teach Yourself: The Easy Way to Learn Stats

Statistics using SPSS/PASW
Field A (2017). Discovering statistics using SPSS. Sage: London.
Pallant J (2016) SPSS Survival Manual. OUP