FF:PSDDS4 Doctoral Seminar IV - Course Information
PSDDS4 Seminar for Doctoral Students IV
Faculty of ArtsSpring 2023
- Extent and Intensity
- 0/0/0. 5 credit(s). Type of Completion: z (credit).
- Teacher(s)
- prof. PhDr. Marek Blatný, DrSc. (lecturer)
doc. PhDr. Martin Jelínek, Ph.D. (lecturer)
Mgr. Helena Klimusová, Ph.D. (lecturer) - Guaranteed by
- prof. PhDr. Marek Blatný, DrSc.
Department of Psychology – Faculty of Arts
Contact Person: Jarmila Valchářová
Supplier department: Department of Psychology – Faculty of Arts - Timetable
- Fri 10. 3. 9:00–12:40 C51, Fri 21. 4. 9:00–12:40 C51, Fri 19. 5. 9:00–12:40 C51
- Timetable of Seminar Groups:
PSDDS4/02: Fri 10. 3. 13:00–16:40 C42, Fri 21. 4. 13:00–16:40 C42, Fri 19. 5. 13:00–16:40 C42, M. Blatný - Prerequisites
- PSDDS3 It is assumed that the student is equipped with general knowledge in the field of quantitative psychological research. In particular, sufficient terminological background and orientation in basic statistical data analysis is required.
- Course Enrolment Limitations
- The course is only offered to the students of the study fields the course is directly associated with.
- fields of study / plans the course is directly associated with
- Psychology (programme FF, D-PSYCH_) (2)
- Course objectives
- Students build on their previous knowledge of quantitative methodology and methods of data analysis with the aim to deepen and extend their knowledge with practical application. They will become familiar with the detailed aspects of quantitative approach, types of research plans and designing of the research project; they will deepen their knowledge of methods of collecting, processing and analysis of quantitative data and they will subsequently be able to implement this knowledge in practice. Students will be able to apply rules of ethics of psychological research and research evaluation criteria, using a quantitative approach and its methods. They will be able to work effectively with procedures of quantitative methodology, in particular to choose the data analysis methods appropriate to the research project and the nature of research data, and critically evaluate the results of the analysis and to propose any alternative data analysis procedures. They will be able to see the limits of a research plan in terms of methodological correctness and feasibility.
- Learning outcomes
- Student will be able to: - select a statistical approach adequate to the research question; - interpret the results of the analyzes (including advanced statistical procedures); - critically evaluate the application of data processing techniques in research studies.
- Syllabus
- The content of the seminars: Basic statistical methods (mean comparisons, correlation, etc.) Regression analysis Factor analysis (including confirmatory FA) Psychometrics Mediation analysis Moderation analysis Presentation and discussion on students' data analysis proposals
- Literature
- FIELD, Andy P. Discovering statistics using IBM SPSS statistics. 5th edition. Los Angeles: Sage, 2018, xxix, 1070. ISBN 9781526419521. info
- Goodwin, K. A., Goodwin, C. J. (2016). Research in Psychology: Methods and Design, 8th Edition. Hoboken, NJ: Wiley.
- HAYES, Andrew F. Introduction to mediation, moderation, and conditional process analysis : a regression-based approach. New York: Guilford Press, 2013, xvii, 507. ISBN 9781609182304. info
- SCHUMACKER, Randall E. and Richard G. LOMAX. A beginner's guide to structural equation modeling. Fourth edition. New York: Routledge, 2016, xxi, 351. ISBN 9781138811904. info
- Relevantní české i zahraniční elektronické zdroje a periodika, e-learningové materiály přednášejících v IS .
- Teaching methods
- Lectures, discussions, presentations, case studies, solution of model situations.
- Assessment methods
- Credit. Conditions for obtaining credit is 80% attendance at seminars and processing the final version of the dissertation research design with focus on the part regarding approaches to data analysis. Further, student needs to process two reviews - feedback on projects of their colleagues, including critical evaluation of research design and data analysis plan. For successful completion of the course, student must actively contribute at seminars by presentations of their own data analysis proposals and by the discussion of their colleagues' proposals.
- Language of instruction
- Czech
- Follow-Up Courses
- Further Comments
- Study Materials
The course is taught annually.
- Enrolment Statistics (Spring 2023, recent)
- Permalink: https://is.muni.cz/course/phil/spring2023/PSDDS4