School of Computer Science

Module 06-23856 (2012)

Evaluation Methods and Statistics

Level 4/M

Benjamin Cowan Andrew Howes Semester 1 10 credits
Co-ordinator: Andrew Howes
Reviewer: Mirco Musolesi

The Module Description is a strict subset of this Syllabus Page.

Aims

The aims of this module are to:

  • Provide the student with knowledge and skills necessary to assess and conduct empirical research for HCI and computational science
  • Give students practical and mathematical knowledge in basic statistical techniques

Learning Outcomes

On successful completion of this module, the student should be able to:

  • Identify and discuss research methodologies for investigating human behaviour
  • Recognise the appropriateness of statistical techniques in data analysis
  • Conduct and report a variety of statistical tests using appropriate software
  • Interpret research findings from a variety of statistical techniques to a high level
  • Discuss issues related to conducting research on human participants (sampling, recruitment, ethics etc)

Teaching methods

1 hr lecture, 2hr tutorial/practical a week

Assessment

  • Sessional: 1.5hr Examination (70%), Continuous assessment (30%)
  • Supplementary: Examination only (100%)

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. An example of evidence-based argumentation
    • Social Capital and intensity of facebook use
    • R Programming language
    • Practical (2 hours)
  3. 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
    • The importance of data screening
    • 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. Live R coding
    • How to explore statistics with the R programming language
    • Practical (2 hours)
  7. Regression
    • Hypotheses. Null hypotheses. Type I and type II errors
    • Data types: Nominal, Ordinal, Interval, Ratio
    • What is a regression?
    • Residuals
    • Calculation of model fit
    • p-value and significance
    • Practical (2 hour)
  8. Comparing two means (T-test)
    • What is a t-test? Why use t-tests?
    • Between and within subjects-what’s the difference?
    • Type I and Type II
    • The purpose of t-tests
    • Basic Statistics and Experimental Design
    • Independent and dependent variables
    • Practical (2 hours)
  9. Comparing 3 means (ANOVA)
    • Why use ANOVA? What does it mean?
    • The purpose of ANOVA
    • Practical (2 hours)
  10. Multiple Independent variables (2 way ANOVA)
    • What is it? Why use it?
    • Main effects and interaction effects
    • Practical (2 hours)
  11. Summary and revision
    • Putting it all together.
    • What to expect from the examination.

Programmes containing this module