Module Identifier RD20920  
Academic Year 2003/2004  
Co-ordinator Dr Malcolm H Leitch  
Semester Semester 2 (Taught over 2 semesters)  
Mutually Exclusive RD25520  
Course delivery Lecture   36 Hours 18 x 2 hour combined lectures/practical sessions on Data Analysis  
  Other   14 Hours 3 x 2 hour workshops in Research Methodologies; 4 x 2 hour workshops in Data Analysis  
  Lecture   10 Hours 10 x 1 hour lectures on Research Methodologies  
Assessment TypeAssessment Length/DetailsProportion
Semester Exam3 Hours Data analysis Open book examination Outcomes assessed: 2, 5, 6, 7  25%
Semester Assessment Research methodologies assignment Outcomes assessed: 1, 3, 4, 7  50%
Semester Assessment Practical exercises Outcomes assessed: 5, 6, 7  25%
Supplementary Assessment Candidates will be required to re-take the assessment that led to failure. 

Learning outcomes

On successful completion of this module students should be able to:
Outcome 1
Produce a fully documented review of a body of scientific/social science literature and draw logical conclusions

Performance criteria:
a. Relevant information is selected and included in the review.
b. A range of information sources covering the scope of the subject is presented.
c. Information is compiled, selected, interpreted and summarised in the student''s own words.
d. Conclusions are identified and presented.
e. Reference to published material is cited appropriately in the text.
Dependent on subject area.
Information Sources: books, journal articles, press, personal communications, other
published material, results from investigations (where applicable).
Reference citation: Harvard system.

Outcome 2
Discuss the differences in experimental approach between quantitative and qualitative research
Performance criteria:
Definitions of quantitative and qualitative research methods can be given.
Examples of quantitative and qualitative research methodologies can be quoted.
Dependent upon subject area

Outcome 3
Propose an appropriate experimental hypothesis for subsequent testing
Performance criteria:
a. The general introduction to the subject provides a background to the topic studied.
b. The significance and context of the subject area is stated.
c. The aims of the project are presented.
d. The limitations of the project/scope of study are identified and presented.
Dependent on subject area.

Outcome 4
Design an appropriate experimental approach for testing an experimental hypothesis
Performance criteria:
a. The methodology is presented and could be repeated by other workers.
b. A suitable/correct methodology is employed within the project.
c. Details of equipment/survey technique used are recorded.
Suitable/correct methodology: dependent on subject area.
Equipment/survey technique: dependent on subject area.
Metric system of measurement.

Outcome 5
Identify appropriate methods of analysis for different types of data
Performance criteria:
a. Method chosen is appropriate to the numbers of treatment groups.
b. Method chosen is appropriate to the type of data available.
Methods: parametric and distribution free non-parametric tests.
Data: discrete and continuously variable.

Outcome 6
Analyse data using a range of statistical methods
Performance criteria:
a. Correct arithmetic procedures are followed.
b. Test statistics are correctly compared to published values.
Parametric and distribution free non-parametric test.

Outcome 7
Interpret experimental data and draw suitable conclusions based on the results of the data analysis
Performance criteria:
a. A suitable report format is chosen.
b. Conclusions and recommendations are made in view of the results of the statistical tests.
Parametric and non-parametric tests.
Example data provided or collected by the student.

Brief description

This module is designed to develop an appreciation of the scientific method, moving from a critical analysis of the existing scientific literature to the development of an experimental hypothesis through to the design of experimental approaches for testing the hypothesis, and the statistical evaluation of data and their interpretation. The production of a detailed research plan will require students to review the relevant scientific literature, propose an experimental hypothesis for testing, and design an experiment to test this hypothesis, taking due account of statistical techniques to be used for data analysis and resources available. This will adopt a formative approach in which the students will be required to develop their plan in stages, will be provided with feedback on their initial attempts and then be given opportunity to revise their plans.

The module includes the theory and practice of a range of statistical methodologies. These include probability, a description of the normal distribution and parametric tests based on samples drawn from normally distributed populations including t-tests, one way and multi way ANOVA and correlation and regression analysis. Non-parametric methods will include chi-square analysis of frequencies, contingency tables, Mann-Whitney U test and Spearman rank correlation. This component of the course is taught through a series of two-hour lecture/practical sessions consisting of an introductory lecture followed by practical examples to work through.   

Transferable skills

.1 Independent project work
The production of the detailed research plan will require students to work independently.

.2 IT and information handling
Students will be required to use IT-based literature searches for the production of their research plan and to produce their assignment in word processed format.

.3 Use and analysis of numerical information
Data analysis using a range of statistical methods represents a major component of the module and will be assessed by means of open book examination.

.4 Writing in an academic context
The Literature Review part of the research plan will require students to write in an academic context appropriate to their chosen area of research, as well as to demonstrate an ability to interpret rather than simply reporting existing literature.

.6 Careers need awareness
The skills obtained from this module will be transferable to many `real world? situations that are likely to confront students in their subsequent careers

.7 Self-management
The production of the research plan will require students to work to specified deadlines, but they will be responsible for organizing their own time management to meet those deadlines

Reading Lists

Mead, R; Curnow, R H & Hasted, A M (1993) Statistical Methods in Agriculture and Experimental Biology 2nd. Chapman and Hall, London
Campbell, R C (1989) Statistics for Biologists 3rd. Cambridge University Press, London


This module is at CQFW Level 5