University of Birmingham School of Computer Science
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SYLLABUS PAGE, 2012/13

06-23856
Evaluation Methods and Statistics

Level 4/M

Prof A Howes
Dr B R Cowan
 		10 credits in Sem1

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The School of Computer Science Module Description is a strict subset of this Syllabus Page. (The University module description has not yet been checked against the School's.)

Relevant Links

Module web page

Outline

The aim of the module is to provide an introduction to the use of experimental design and statistics for the purpose of investigating human behaviour. The module is targeted at computer scientists with an interest in (i) understanding empirical studies concerning human behaviour, including studies of cognitive, social, and economic behaviour, and (ii) designing and conducting empirical research into the interaction between people and computers. The module may be of interest to computer scientists who look to an understanding of human behaviour to help constrain the development of computational systems, including novel forms of social media, information visualisation, information retrieval system, decision support system, robotics, and dynamic control systems. The module focuses on the implications of methodology and statistics (through lectures) and the practical implementation of research methodologies on real world datasets. Students will learn about how to design experiments, how to analyse data (using a statistical programming language or package), and how to write evidence-based reports.

Aims

The aims of this module are to:

Learning Outcomes

On successful completion of this module, the student should be able to: Assessed by:
1 Identify and discuss research methodologies for investigating human behaviour. Continuous assessment, examination
2 Recognise the appropriateness of statistical techniques in data analysis. Continuous assessment, examination
3 Conduct and report a variety of statistical tests using appropriate software. Continuous assessment, examination
4 Interpret research findings from a variety of statistical techniques to a high level. Continuous assessment, examination
5 Discuss issues related to conducting research on human participants (sampling, recruitment, ethics etc). Continuous assessment, examination

Restrictions, Prerequisites and Corequisites

Restrictions:

None

Prerequisites:

None

Co-requisites:

None

Teaching

Teaching methods:

1 hr lecture, 2hr tutorial/practical a week

Contact hours:

33

Assessment

Normal (sessional): 1.5hr Examination (70%), Continuous assessment (30%)

Resit (supplementary) assessment (where allowed): Examination only (100%)

The continuous assessment is based on three practical reports (in the format of scientific papers- 10% each) based around 3 datasets used in the course.

Recommended Books

Title Author(s) Publisher, Date Comments
Statistical Methods for Psychology Howell, David C. Wadsworth Publishing, 2006 Essential course text for methodology part of course
Statistics in R: An introduction using R Crawley, Michael J. Wiley, 2005 Essential course text for statistics and practical part of course
Discovering Statistics using SPSS; Third Edition Field, A. Wiley, 2009 Essential course text for statistics and practical part of course

Detailed Syllabus

  1. Introduction- Evidence Based Argumentation
    • Traditional versus rational authority
    • Claims, Data, Warrants, and Qualifiers
    • Good science / Bad Science
    • Overview of the course
    • Practical (2 hours) – Introduction to R for Describing Data
  2. Basic Statistics 1
    • Plotting data
    • Frequencies
    • The Normal distribution
    • Measures of central tendency: Mean, mode, median
    • Measures of variability, standard deviation, variance, confidence
    • Boxplots: Graphical representations of dispersion and extreme scores
    • Central Limit Theorem
    • Practical (2 hours)
  3. Basic Statistics and Experimental Design
    • Independent and dependent variables.
    • Hypotheses. Null hypotheses. Type I and type II errors
    • Data types: Nominal, Ordinal, Interval, Ratio
    • The importance of data screening
    • Basic Principles of probability
    • Practical (2 hours)
  4. Writing scientific research
    • What is a good theory
    • Structure of a paper
    • The importance of being methodologically aware
    • What makes a good report?
    • Practical (2 hours)
  5. Correlation
    • What is a correlation? What does it mean?
    • The value of correlation
    • Practical (2 hours)
  6. Comparing two means (T-test)
    • What is a t-test? Why use t-tests?
    • Between and within subjects-what’s the difference?
    • The purpose of t-tests
    • Practical (2 hours)
  7. Comparing 3 means (ANOVA)
    • Why use ANOVA? What does it mean?
    • The purpose of ANOVA
    • Practical (2 hours)
  8. Multiple Independent variables (2 way ANOVA)
    • What is it? Why use it?
    • Main effects and interaction effects
    • Practical (2 hours)
  9. Making measures and collecting opinions (Factor analysis)
    • Making a questionnaire- item development
    • Testing-What is factor analysis? Why use it?
    • Types of reliability and validity
    • Interview techniques
    • Practical (2 hours)
  10. Summary and revision
    • Putting it all together.
    • What to expect from the examination.

Programmes | Modules | Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus