Stud.IP Uni Oldenburg
University of Oldenburg
26.01.2022 13:39:19
neu725 - Multivariate Statistics and Applications in R (Complete module description)
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Module label Multivariate Statistics and Applications in R
Module code neu725
Credit points 6.0 KP
Workload 180 h
2 SWS Lecture (30h contact / 60h self-studies and exam preparation)
2 SWS Seminar (30h contact / 60h statistical data analysis in R)
Institute directory Department of Neurosciences
Applicability of the module
  • Master's Programme Biology (Master) > Skills Modules
  • Master's Programme Neuroscience (Master) > Skills Modules
Responsible persons
Hildebrandt, Andrea (Module responsibility)
Hildebrandt, Andrea (Authorized examiners)
recommended in semester 1/3
weeks 11-13 of summer semester
Skills to be acquired in this module
Students will acquire basic knowledge in planning empirical investigations, managing and understanding quantitative data and conducting a wide variety of multivariate statistical analyses. They will learn how to use the statistical methodology in terms of good scientific practice and how to interpret, evaluate and synthesize empirical results from the perspective of statistical modeling in basic and applied research context. The courses in this module will additionally point out statistical misconceptions and help students to overcome them.

+ Independent research
+ Scient. Literature
+ Social skills
++ Interdiscipl. knowledge
++ Maths/Stats/Progr.
++ Data preset./disc.
+ Scient. English
++ Ethics

Module contents
Part 1: Multivariate Statistics I (lecture):
Graphical representation of multivariate data
The Generalized Linear Modeling (GLM) framework
Multiple and moderated linear regression with quantitative and qualitative predictors
Logistic regression
Multilevel regression (Generalized Linear Mixed Effects Modeling – GLMM)
Non-linear regression models
Path modeling
Factor analysis (exploratory & confirmatory)
(Multilevel) Structural equation modeling (SEM linear and non-linear)

Part 2: Analysis Methods with R (seminar)
Data examples and applications of GLM, GLMM, polynomial, spline and local regression, path
modeling, factor analyses and SEM
Reader's advisory
Course material will be available in Stud.IP
Language of instruction English
Duration (semesters) 1 Semester
Module frequency winter term, annually
Module capacity unlimited (
recommended in semester 1/3
weeks 11-13 of summer semester
Modullevel / module level MM (Mastermodul / Master module)
Modulart / typ of module Wahlpflicht / Elective
Lehr-/Lernform / Teaching/Learning method
Vorkenntnisse / Previous knowledge
Course type Comment SWS Frequency Workload of compulsory attendance
2 SoSe oder WiSe 28
2 SoSe oder WiSe 28
Total time of attendance for the module 56 h
Examination Time of examination Type of examination
Final exam of module
End of winter semester
written exam
attendance of at least 70% in the seminars (in addition, mandatory but ungraded)