# Module 06-23856 (2011)

## Level 4/M

 Benjamin Cowan Andrew Howes Semester 2 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 HCI research
• Give students practical and theoretical knowledge in statistical techniques common to HCI

### Learning Outcomes

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

• Identify and discuss HCI research methodologies and the theoretical issues of such methodologies in research design
• Recognise the appropriateness of statistical techniques in HCI data analysis
• Conduct and report a variety of statistical tests used in HCI research
• Interpret research findings from a variety of statistical techniques
• Discuss issues related to conducting research in HCI (sampling, recruitment etc)

### Teaching methods

``````1 hr lecture, 2hr tutorial/practical a week
``````

### Assessment

• Sessional: 1.5hr Examination (70%), Coursework (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. 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.