Prerequisite/Recommended prerequisite for participation in the module

Content, progress and pedagogy of the module

A crucial aspect of designing medialogy systems, tools or applications is the need to evaluate the work experimentally. The knowledge of how to properly design experiments to collect and evaluate data is essential to answer many of the problems within medialogy. Examples are testing which of two tracking algorithms is the most efficient; how users perform with different kinds of feedback; possible relationship between age and performance, etc.

Learning objectives

Knowledge

• Must be able to understand the basic concepts of probability: sample space of all possible events; combinatorics; independent events; conditional probability; Bayes’ formula; binomial distribution, etc.
• Must display knowledge about basic statistic terminology and treatment of data: distributions (probability density function, cumulative distribution function, quantile function); measures of central tendency and variability; histogram; central limit theorem, significance, power, type I and II errors, etc.
• Must be able to understand advantages and disadvantages with different types of designs and studies (between-group and within-group designs; correlational studies; blind/double blind, complete/incomplete and balanced/unbalanced designs)
• Must be able to understand the difference between common experimental designs, e.g., single sample experiments, two sample experiments, and factorial/multifactorial experiments
• Must understand the basic experimental design principles of independence, randomization, replication, and blocking and how these can be applied in experiments.
• Must be able to relate frequency distributions to the concept of hypothesis testing (understanding)
• Must be able to understand possible ethical concerns for a study

Skills

• Must be able to design an experiment to measure changes in a dependent variable, identifying and efficiently controlling relevant independent variables (application)
• Must be able to properly inform and instruct persons participating in a study (application)
• Must be able to understand and select among the most common methods for statistical analysis and assessment of experimental data (e.g., t-test, analysis of variance, chi-square tests, binomial test, correlation, and simple linear and logistic regression)
• Must be able to understand the difference between parametric and non-parametric analysis methods
• Must be able to understand different measurement scales and discuss experiments in terms of reliability, bias and sensitivity
• Must be able to discuss own data in terms of assumptions for statistical testing (application)
• Must be able to use an existing statistical package to analyse and present experimental results
• Must be able to discuss and represent empirical data in different ways (describing text, numbers, formulas, graphs and figures) and shift between these according to the needs of the situation and context (application)
• Must be able to read, understand and implement experimental and empirical work as described in relevant literature (application)

Competences

• Students who complete this module will be able to systematically design quantitative, scientific experiments, taking into account relevant factors (application)
• Students who complete this module will be able to use a statistical software package to analyse experimental data (application)
• Students who complete this module will be able to document their experimental results, and to understand experimental results presented by others (application)

Type of instruction

Refer to the overview of instruction types listed in the start of chapter 3. The types of instruction for this course are decided in accordance with the Joint Programme Regulations and directions are decided and given by the Study Board for Media Technology.

Notice: This elective course might not be offered if less than 10 students sign up

Exam

Exams