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Department/Unit / Abbreviation	PCH / NVSM			Academic Year	2025/2026
Academic Year	2025/2026
Title	Multidimensional Statistical Methods			Form of course completion	Exam
Form of course completion	Exam
Accredited / Credits	Yes, 3 Cred.			Type of completion	Combined
Type of completion	Combined
Time requirements	Přednáška 4 [Hours/Semestr]			Course credit prior to examination	Yes
Course credit prior to examination	Yes
Automatic acceptance of credit before examination	No
Included in study average	YES
Language of instruction	Czech
Occ/max				Automatic acceptance of credit before examination	No
Summer semester	54 / -	0 / -	0 / -	Included in study average	YES
Winter semester	0 / -	0 / -	0 / -	Repeated registration	NO
Repeated registration	NO
Timetable	Yes			Semester taught	Summer semester
Semester taught	Summer semester
Minimum (B + C) students	not determined			Optional course	Yes
Optional course	Yes
Language of instruction	Czech			Internship duration	0
No. of hours of on-premise lessons				Evaluation scale	A|B|C|D|E|F
Periodicity	every year			Evaluation scale for credit before examination	S|N
Specification periodicity				Fundamental theoretical course	No
Fundamental course	Yes
Fundamental theoretical course	No
Evaluation scale	A|B|C|D|E|F
Evaluation scale for credit before examination	S|N
Substituted course	None
Preclusive courses	PCH/VSMCN
Prerequisite courses	N/A
Informally recommended courses	N/A
Courses depending on this Course	N/A
Histogram of students' grades over the years: Graphic PNG ,  XLS

Course objectives:
The aim of the course is to introduce students to various methods of statistical analysis of multivariate data. During the semester, students will encounter specific projects where the various methods have been applied and they will apply them to their own data.
Requirements on student
Students are assessed by a grade derived from the number of points they have earned during the semester. Points can be earned in two ways: 1) By submitting a report documenting the use of a multivariate statistical method. 2) By evaluating the reports of their classmates. The semester is divided into 4 periods. Students may (but are not required to) submit one report in each period. Each report submitted is scored from 0 to 4 points. The number of points a student receives for a report depends on how the report was evaluated by other students and possibly also by the lecturer. The reports are evaluated in terms of their level of complexity (sophistication, originality, correctness...) and didactic quality (clarity, comprehensibility, instructiveness...). At the end of each period, each student is anonymously assigned five submitted reports. If the student rates these reports, they can earn one extra point for the period (regardless of whether or not they have submitted a report themselves during the period). This extra point is only given to some students. The criterion is whether the ratings given by the student match those of other students. Thus, students who score more accurately have a higher chance of receiving the extra point, although the influence of chance cannot be ruled out, so it is not recommended to rely on this source of points. In order to receive points for the period, students must submit their evaluation of their classmates' reports. Points are converted to grades at the end of the term as follows: 8-20 pts. = A, 7 pts. = B, 6 pts. = C, 5 pts. = D, 4 pts. = E, 0-3 pts. = F.
Content
During the course, students will be introduced to the following methods: - Advanced skills in building regression models (non-linear relationships, nominal regressors, interactions) - Mixed effects models - Hotelling test, MANOVA and MANCOVA - Log-normal regression - Logistic regression - Poisson regression - Cluster analysis (k-means method and hierarchical clustering) - Analysis of canonical correlations - Other methods according to student and lecturer interest. The projects will expose students to other procedures, many of which they are familiar with from other courses. For example: - Various linear regression models - Exploratory factor analysis and principal components analysis - Structural modelling and confirmatory factor analysis - IRT models, DIF analysis - ROC analysis
Activities
Link to MOODLE: : Vícerozměrné statistické metody
Fields of study
Guarantors and lecturers
Guarantors: PhDr. Daniel Dostál, Ph.D. (100%),  Lecturer: PhDr. Daniel Dostál, Ph.D. (100%),
Literature
Recommended: Hendl, J. (2012). Přehled statistických metod: analýza a metaanalýza dat. Portál. Recommended: Urbánek, T., Denglerová, D., & Širůček, J. (2011). Psychometrika: měření v psychologii. Portál. Recommended: Martin, P. R., Bateson, P. P. G., & Müller, I. (2009). Úvod do teorie a metodologie měření chování. Portál. On-line library catalogues
Time requirements
All forms of study Activities Time requirements for activity [h] Homework for Teaching 30 Attendace 4 Preparation for the Exam 20 Total 54
Prerequisites - other information about course preconditions
Knowledge of descriptive and inferential statistics. Orientation in terms of linear models (regression analysis).
Competences acquired
Students will gain an overview of multivariate data analysis methods and try out some of the procedures themselves.
Teaching methods
Monologic Lecture(Interpretation, Training) Demonstration Projection (static, dynamic)
Assessment methods
Oral exam Student performance Seminar Work