Condensed list of topics

1. How to write a scientific report

o The standard format of reports*
o Components of reports*
o Writing summaries, abstracts and introductions*
o Writing method sections (background, method, equipment, approach) *
o Writing results sections (analysis, results) *
o Writing discussions (discussion, conclusion, further work) *
o Referencing and formatting*

2. Introduction to experimental design

o Introduction to variables
o The scientific method
o Quantitative vs. qualitative methods
o How are questions researched? (hypothesis testing, explorative methods)
o The aims of research and experimentation
o Ethical considerations
o Data sources (user studies, log files, questionnaires) & introduction to data types
o Planning experiments:
- Data requirements
- Probability distributions, expected results*
- Within vs. between subject approaches*
- Effects of learning*
- Measures of data spread (standard deviation, variance)
- Data sources
- Data quality (precision + accuracy)

3. Introduction to results analysis and basic statistics
o Introduction to Excel & SPSS (statistics/analysis software)
o Descriptive statistics:
- Statistical significance*
- Confidence intervals
- Regression + line fits
- Inferential statistics
- Testing hypotheses
o Parametric statistics:
- T-tests*
- ANOVA
o Non-parametric statistics [may not be required]:
- Mann-Whitney Test
- Wilcoxon Signed-Rank Test
- Friedman´s ANOVA
o Choosing statistical tests:
- The need to think about statistics when designing a study
- Avoiding confusion etc.
o Populations: small vs. large
o Dealing with outliers
o Checking the validity of statistical assumptions (in practice) &
representativeness of results

4. Introduction to survey-based methods & analysis
o Finding the target audience**
o Creating surveys**
o Data analysis**
o Factor analysis & clustering

5. Introduction to data and results visualization
o Graphing, charting, representing data**
o Iterative visualization


Items marked [*] are currently covered in the Usability with Project course.
Items marked [**] are currently covered in the Target Group Analysis course.



Lectures: The basic approach will follow the standard model of 2 hours of lecturing (over 16 weeks) with however small exercises inserted to break up the lecturing stream and provide hands-on experience with the methodological material. The course will feature a reading list containing the material students should acquire for each lecture.

Exercises: The lectures will be followed by 2 hours of practical, exercise-based work in groups, where students follow cases based on the DDK-subjects and try out the statistical methods taught during the lectures. Group size will be varied from 2-5 depending on the tasks, to keep the environment flexible and accommodate different student learning styles. The exercises will vary according to the specific topic. Typical exercises will include planning, reporting or calculations.

Some exercises will be followed by short written assignments or presentations given to the class.

Workload: Expected 10 hours per week.
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Information about study structure
This course is part of the DDK specialization User-centered Design. Find it described here:
DDK specializations

Se hvordan undervisningen er tilrettelagt her:
link til skemaoplysninger
Skemaoplysningerne vil være tilgængelige fra kort før semesterstart.

See the schedule here:
link to the time table
The schedule will be available shortly before the beginning of the term.