VPLn4468 Multivariate data analysis

Faculty of Social Studies
Autumn 2022

The course is not taught in Autumn 2022

Extent and Intensity
1/1/0. 10 credit(s). Type of Completion: z (credit).
Taught in person.
Teacher(s)
Mgr. Miroslav Suchanec, Ph.D., M.Sc. (lecturer)
Guaranteed by
Mgr. Miroslav Suchanec, Ph.D., M.Sc.
Department of Social Policy and Social Work – Faculty of Social Studies
Supplier department: Department of Social Policy and Social Work – Faculty of Social Studies
Timetable
Mon 12:00–13:40 PC25
Prerequisites
Course does not assume any previous methodological or statistical knowledge.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 18 student(s).
Current registration and enrolment status: enrolled: 0/18, only registered: 0/18, only registered with preference (fields directly associated with the programme): 0/18
fields of study / plans the course is directly associated with
there are 11 fields of study the course is directly associated with, display
Course objectives
Graduates should: 1. understand utility/usefulness of multivariate data analysis in public policy and human resources 2. be able to choose relevant multivariate method with respect to research goal 3. be able to interpret results of multivariate data analysis in research journals (passive knowledge) 4. master selected multivariate methods (active knowledge)
Learning outcomes
1. understand utility/usefulness of multivariate data analysis in public policy and human resources 2. be able to choose relevant multivariate method with respect to research goal 3. be able to interpret results of multivariate data analysis in research journals (passive knowledge) 4. master selected multivariate methods (active knowledge)
Syllabus
  • 1. Introduction to multivariate data analysis (Introduction to SPSS/PASW, Assumptions of linear multivariate data analysis, Assumptions of multivariate analysis of categorical data. 2. Selected methods of linear multivariate data analysis (factor and cluster analysis) 3. Selected methods of multivariate analysis of categorical data (logistic regression)
Literature
    required literature
  • AGRESTI, Alan. An introduction to categorical data analysis. 2nd ed. Hoboken, NJ: Wiley-Interscience. xvii, 372. ISBN 9780471226185. 2007. info
  • AGRESTI, Alan and Christine A. FRANKLIN. Statistics : the art and science of learning from data. Upper Saddle River, NJ: Pearson Prentice Hall. xxv, 693. ISBN 0130455369. 2006. info
Teaching methods
Each class consists of lecture and following workshop. At the end of semester students will choose one method and apply it in their own research.
Assessment methods
credit for final project (data analysis with one selected method)
Language of instruction
Czech
  • Permalink: https://is.muni.cz/course/fss/autumn2022/VPLn4468