Module 23856 (2011)

Syllabus page 2011/2012

06-23856
Evaluation Methods and Statistics
Level 4/M

Andrew Howes
Andrew Howes:5
Ben Cowan:5
10 credits in Semester 2

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

The 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 course aims to give students knowledge in research methodologies relevant to HCI and the use of statistical techniques in the analysis of data gathered in HCI research. The course focuses on the theoretical implications of methodology and statistics (through lectures) and the practical implementation of research methodologies and analysis techniques on real world HCI datasets.

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: Assessed by:
1Identify and discuss HCI research methodologies and the theoretical issues of such methodologies in research design Coursework, examination
2Recognise the appropriateness of statistical techniques in HCI data analysis Coursework, examination
3Conduct and report a variety of statistical tests used in HCI research Coursework, examination
4Interpret research findings from a variety of statistical techniques Coursework, examination
5Discuss issues related to conducting research in HCI (sampling, recruitment etc) Coursework, 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

  • Sessional: 1.5hr Examination (70%), Coursework (30%)
  • Supplementary (where allowed): Examination only (100%)

Recommended Books

TitleAuthor(s)Publisher, Date
Statistical Methods for Psychology Howell, David C. Wadsworth Publishing , 2006
Statistics in R: An introduction using R Crawley, Michael J. Wiley , 2005
Discovering Statistics using SPSS; Third Edition Field, A. Wiley , 2009

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.

Last updated: 31 January 2012

Source file: /internal/modules/COMSCI/2011/xml/23856.xml

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