Statistics - OPNP3Q209A

• Title: Statistics
• Guaranteed by: Katedra psychologie (41-KPSY)
• Faculty: Faculty of Education
• Actual: from 2025 to 2026
• Semester: summer
• E-Credits: 2
• Examination process: summer s.:
• Hours per week, examination: summer s.:0/1, Ex [HT]
• Capacity: unknown / 40 (unknown)
• Min. number of students: unlimited
• 4EU+: no
• Virtual mobility / capacity: no
• State of the course: taught
• Language: English
• Teaching methods: full-time
• Note: course can be enrolled in outside the study plan; enabled for web enrollment; priority enrollment if the course is part of the study plan
• Guarantor: doc. Mgr. Kateřina Lukavská, Ph.D.
• Teacher(s): doc. Mgr. Kateřina Lukavská, Ph.D.

Annotation

The first part of the course covers theoretical background and practical skills related to hypotheses testing in cross-sectional data. It deals with simple statistical tests such as mean comparison (t-test, analysis of variance), correlation tests and simple linear (and binominal logistic) regression. In the second part, we will introduce more complex methods aimed at inferential statistical analysis of data coming from longitudinal observation research and intervention research (e.g., randomized control trials). It deals with advanced statistical methods such as generalized linear mixed models, generalized estimating equations, propensity scores matching, but some basic methods are introduced as well (e.g., paired t-test, one-way repeated measures ANOVA). The special attention will be on sampling procedures (type of sampling and power analysis) and correct description of sample in scientific papers (e.g., CONSORT diagram, response rate, attrition rate).

Last update: Lukavská Kateřina, doc. Mgr., Ph.D. (02.02.2024)

Course completion requirements

• Class attendance

• Written test

• Oral examination

Last update: Lukavská Kateřina, doc. Mgr., Ph.D. (04.02.2026)

Literature

HENDL, J. Přehled statistických metod zpracování dat. Praha: Portál, 2006.

FIELD, Andy; MILES, Jeremy; FIELD, Zoë. Discovering statistics using R. Sage publications, 2012.

VAN DER LINDEN, Wim J.; HAMBLETON, Ronald K. (ed.). Handbook of modern item response theory. Springer Science & Business Media, 2013.

Last update: Hrabec Ondřej, Mgr., Ph.D. (07.02.2024)

Syllabus

• The NHST paradigm of hypothesis testing and its alternatives

• Statistical distributions and the calculation of statistical power

• Tests based on mean comparisons

• Frequency analysis

• Analysis of relationships (correlation, regression)

• Preregistration, reporting of results, and limits of statistical data analysis

Supporting study materials for these topics are available here: 

https://dl1.cuni.cz/course/view.php?id=18932

Last update: Lukavská Kateřina, doc. Mgr., Ph.D. (05.03.2026)

Learning resources

Supporting study materials for these topics are available here: https://dl1.cuni.cz/course/view.php?id=18932

Last update: Lukavská Kateřina, doc. Mgr., Ph.D. (05.03.2026)

Learning outcomes

Explain the basic principles of hypothesis testing:
Students explain the fundamental principles of hypothesis testing in cross-sectional data, including basic statistical tests such as the t-test, analysis of variance (ANOVA), correlation tests, and regression analysis.

Apply statistical tests to cross-sectional data:
Students apply basic statistical tests (e.g., t-test, ANOVA, correlation, simple linear and logistic regression) to analyze cross-sectional data in the context of specific research problems.

Interpret the results of statistical tests:
Students interpret the results of statistical tests, explain their meaning, and state whether the hypotheses have been supported or rejected.

Apply methods for longitudinal data:
Students apply more advanced statistical methods, such as the paired t-test and repeated-measures ANOVA, to analyze longitudinal data.

Last update: Lukavská Kateřina, doc. Mgr., Ph.D. (04.02.2026)