PSD_DS4 Seminar for Doctoral Students IV

Faculty of Arts
Spring 2020
Extent and Intensity
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)
prof. PhDr. Tomáš Urbánek, 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 6. 3. 9:00–12:40 C51, Fri 3. 4. 9:00–12:40 C51, Fri 15. 5. 9:00–12:40 C51
  • Timetable of Seminar Groups:
PSD_DS4/01: Fri 6. 3. 13:00–16:40 C51, Fri 3. 4. 13:00–16:40 C51, Fri 15. 5. 13:00–16:40 C51, M. Blatný
PSD_DS4/02: Fri 6. 3. 13:00–16:40 C42, Fri 3. 4. 13:00–16:40 C42, Fri 15. 5. 13:00–16:40 C42, M. Blatný
Prerequisites
PSD_DSIII 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
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.
The course is also listed under the following terms Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (Spring 2020, recent)
  • Permalink: https://is.muni.cz/course/phil/spring2020/PSD_DS4