PUPn4568 Multivariable Statistics

Faculty of Social Studies
Autumn 2023
Extent and Intensity
1/1/0. 12 credit(s). Type of Completion: z (credit).
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
Thu 12:00–13:40 PC26
Prerequisites
Course does not assume any previous methodological or statistical knowledge.
Course Enrolment Limitations
The course is only offered to the students of the study fields 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
fields of study / plans the course is directly associated with
Course objectives
Graduates should: 1. understand utility/usefulness of multivariable data analysis in public policy and human resources 2. be able to choose relevant multivariable method with respect to research goal 3. be able to interpret results of multivariable data analysis in research journals (passive knowledge) 4. master selected multivariable methods (active knowledge)
Learning outcomes
1. understand utility/usefulness of multivariable data analysis in public policy and human resources 2. be able to choose relevant multivariable method with respect to research goal 3. be able to interpret results of multivariable data analysis in research journals (passive knowledge) 4. master selected multivariable methods (active knowledge)
Syllabus
  • 1. Introduction to multivariable data analysis (Introduction to SPSS/PASW, Assumptions of linear multivariable data analysis, Assumptions of multivariable analysis of categorical data. 2. Selected methods of linear multivariable data analysis (factor and cluster analysis) 3. Selected methods of multivariable analysis of categorical data (logistic regression)
Literature
    required literature
  • AGRESTI, Alan and Christine A. FRANKLIN. Statistics : the art and science of learning from data. 3rd ed. Boston: Pearson, 2013, xxiii, 757. ISBN 9780321805744. info
  • AGRESTI, Alan. An introduction to categorical data analysis. 2nd ed. Hoboken, NJ: Wiley-Interscience, 2007, xvii, 372. ISBN 9780471226185. 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
English
Further Comments
Study Materials
The course is also listed under the following terms Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2024.
  • Enrolment Statistics (Autumn 2023, recent)
  • Permalink: https://is.muni.cz/course/fss/autumn2023/PUPn4568