PSYb2521 Statistical data analysis II

Faculty of Social Studies
Autumn 2025
Extent and Intensity
1/1/0. 6 credit(s). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
doc. Mgr. Stanislav Ježek, Ph.D. (lecturer)
Mgr. Anna Literová (seminar tutor)
Mgr. Mikuláš Mačas (seminar tutor)
Bc. Jan Pavlík (assistant)
Guaranteed by
doc. Mgr. Stanislav Ježek, Ph.D.
Department of Psychology – Faculty of Social Studies
Supplier department: Department of Psychology – Faculty of Social Studies
Timetable
Wed 24. 9. 8:00–9:40 P51 Posluchárna V. Čermáka, Wed 8. 10. 8:00–9:40 P51 Posluchárna V. Čermáka, Wed 22. 10. 8:00–9:40 P51 Posluchárna V. Čermáka, Wed 5. 11. 8:00–9:40 P51 Posluchárna V. Čermáka, Wed 19. 11. 8:00–9:40 P51 Posluchárna V. Čermáka, Wed 3. 12. 8:00–9:40 P51 Posluchárna V. Čermáka
  • Timetable of Seminar Groups:
PSYb2521/s1: each odd Wednesday 14:00–15:40 PC25, S. Ježek, A. Literová
PSYb2521/s2: each even Wednesday 14:00–15:40 PC25, S. Ježek, M. Mačas
PSYb2521/s3: each even Wednesday 8:00–9:40 PC25, S. Ježek
Prerequisites
! PSYb2520 Statistical data analysis II && PSYb1170 Statistics
The course assumes the student has working knowledge of the elementary concepts of statistical description, inference, and probability. Ability to test simple hypotheses on means, proportions and correlations. In this sense the prerequisite of this course is FSS:PSYb1170 or an equivalent.
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
The course is an introduction to applied statistical analysis using statistical software. It introduces students to the most frequently used statistical models in psychology - linear regression models - so that students are able to specify the model, estimate its parameters and interpret these parameters. The course also introduces the logistic regression model that is instrumental for easy understanding of IRT models in psychometrics. Modelling is introduced in the context of open science practices. Besides producing own analyses, the focus is also on the understanding of published analyses.
Learning outcomes
Using statistical software, students will be able to perform basic statistical analyses from data preparation, model selection and specification, parameter estimation and interpretation, to a written report of the results in line with APA recommendations. In their analyses students will be able to make use of the most common models - multiple regression with continuous and categorical predictors and interaction of predictors, mediation linear model, logistic regression, and factorial ANOVA. Students will be able to critically interpret the results withrespect to the purpose of the analysis and assumptions of the model.
Syllabus
1. General analytical procedure, open science, missing data. 2. Statistical description using software. 3. Testing hypotheses about means, proportions and correlations. 4. Multiple linear regression, mediation, moderation. 5. Logistic regression. 6. ANOVA, factorial ANOVA, ANCOVA. 7. Models for repeated-measure designs.
Literature
    required literature
  • Field, A., van Doorn, J., & Wagenmakers J.-E. (2025). Discoverings statistics using JASP. Sage, 2025.
  • MORGAN, Susan E.; Tom REICHERT and Tyler R. HARRISON. From numbers to words : reporting statistical results for the social sciences. Boston: Allyn and Bacon, 2002, xiii, 125. ISBN 080133280X. info
  • Publication manual of the American Psychological Association. Sixth edition. Washington, DC: American Psychological Association, 2010, xviii, 272. ISBN 9781433805622. info
    not specified
  • FIELD, Andy P. Discovering statistics using IBM SPSS statistics. 6th edition. Los Angeles: Sage, 2024, xxxii, 111. ISBN 9781529630008. info
Teaching methods
lecture, seminar, online discussion, discussion of team projects
Assessment methods
Seminar Papers
During the semester, students are expected to work in small teams on a series of seminar analyses. Their 100% completion and acceptance is a prerequisite for registering for the exam. The papers are evaluated on a accepted – not accepted scale; the evaluation is accompanied by comments on partial aspects of the work. “Not accepted” means returned for revision. The team has one week for revision from the moment feedback is uploaded to the study materials.
Midterm Test
The midterm test verifies theoretical knowledge, which is a review and extension of the prerequisite basics of statistical analysis. It focuses primarily on the basics of statistical inference and simple linear regression. A maximum of 20 points can be earned.
Final Exam
The course concludes with a final exam. Registration for the exam requires successful completion of all seminar assignments. The exam has both a theoretical and a practical part. The theoretical part tests knowledge of models covered in the course. The practical part takes the form of carrying out an assigned analysis using statistical software. Each part of the exam carries 30 points. To pass the exam, students must earn at least 15 points in each part. Failure to reach this minimum requires repeating the entire exam.
Overall Assessment
The overall assessment will use the following grading scale: A >= 65 points, B >= 60 points, C >= 55 points, D >= 50 points, E >= 45 points, F < 45 points.
Náhradní absolvování
I the case of study abroad, it is possible to work on the seminar analyses individualy. Instead of submitting the student would be required to introduce them shorly in an online consultation. The midterm test can be taken on a make-up date or during the exam period.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Teacher's information
Communication channels

Please use „PSYb2521“ in the subject of emails related to this course.

If you have questions about statistical theory or practice taught in this courses, please make maximum use of our pubic Facebook group „Statistika, metodologie, psychometrika“ http://goo.gl/Mt95eT. There is no problem using English there. Lecturers read the group and response time there is shorter than to emails. You can also use the coourse discussion forum in the MU information system

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