Scientific theory and data analysis

1. What is science?
- Important concepts and contributers (e.g. Aristotle, Hume, Kuhn, Popper, Lakatos)
- Paradigms in science (e.g. Kuhn, Lakatos)
- Relationships between different types of research (e.g. basic research, applied research, action research)

2. Science, ethics, and society
- Perspectives, norms, objectivity, and subjectivity
- Science and ethics (The "Helsinki declarations", Merton's four ethical norms, etc.)
- Fraud in science

3. Science and modelling
- "Hypothetic-deductive method", the "science circle"
- Functionalism, reductionalism, and reflective theory
- Building models (concepts, variables, relationships, mediating and moderating effects, formative and reflective measures, etc.)
- Modelling as a method for the prediction and controlling of processes and activities

4. Research planning

5. Introduction to SPSS (data manipulation and descriptive statistics)

6. Basic statistical techniques for comparing groups and analysing relationships between variables
- Analyses of variance
- Cross-table analysis
- Linear multiple regression analysis
- Factor analysis

7. Advanced statistical techniques
- Logistic regression 
- Conjoint analysis
- Cluster analysis
- Linear structural equation (PLS)

8. Communicating research results

Knowledge:
- Have acquired comprehensive knowledge of scientific theory and have developed good skills in basic and more advanced statistical methods
- Have knowledge of research ethical norms
- Have knowledge of presentation and reporting techniques

Skills:
- Be able to carry through an independent, small-scale research project in accordance with prevailing scientific ethical norms
- Be able to reflect on relevant ethical problems with respect to subject, profession and research
- Be able to communicate research problems, analyses, and conclusions, as well as acquire a basis for communicating more comprehensive scientific work

Competence:
- Be able to view the topics of this course in a larger business/economic professional perspective
- Be able to view the topics of this course in a larger social perspective
- Be able acquire updated knowledge within the above mentioned areas of knowledge and skills

Pedagogiske metoder:

Lectures, discussions in groups and in plenary, laboratory exercises (statistical analyses), and preparation of a compulsory group assignment (maximum of three students) connected to scientific problems.   

Obligatoriske arbeidskrav:

A compulsory group assignment, and three approved individual statistical exercises must be completed and passed. If the candidate does not pass all three exercises, new exercises must be completed and passed before the the next ordinary exam (the following year year). This also applies to the compulsory group assignment.