# Module 06-23856 (2012)

## 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
• 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.