Objectives and competences
Goals:
• Explain basic terms of advanced data analysis
• Explain correlational studies
• Explain the concept of (quasi)experiment
• Explain hypothesis stating and testing
• Explain parametric and non-parametric tests
Competencies:
• Design of data analysis project
• Conducting advanced data analysis
• Use of SPSS
• Interpretation of analysis results
• Improvement of business ideas and business itself based on analytics
Content (Syllabus outline)
• Introduction to advanced data analysis
• Data: structure, measurements, presentation
• Distributions
• Experimental studies
o Hypothesis testing
o Hypotheses rejection
o Test statistics
• Parametric tests
• Non-parametric tests
Learning and teaching methods
• Seminary work
• Case studies
• Problem based learning
• Group work
Intended learning outcomes - knowledge and understanding
Knowledge and understanding:
Students:
• Understanding of statistical methods
• Make distinction between correlational and experimental studies
• Conduction of (quasi)experimental study
• Use of SPSS
• Use of appropriate statistical tests
• Evaluation of statistical analyses’ results
Readings
George Argyrous. Statistics for Research: With a Guide to SPSS. SAGE Publications. 3rd Ed. ISBN 9781849205955
Tiffany Bergin. An Introduction to Data Analysis. Quantitative, Qualitative and Mixed Methods. Sage Publications. ISBN 9781446295151 / 9781446295144
»Seznam dodatnih študijskih materialov bo razdeljen študentom ob začetku predavanj. A list of additional study materials will be distributed when the module begins.«
Prerequisits
There are no prerequisites for this course.
Additional information on implementation and assessment Method (written or oral exam, coursework, project):
Written exam * 50%
Exercises 50%
* written exam can be substituted by two written colloquia; students with positive grade from exercises may write the exam; subject is passed with positive grade from written exam
