neu725 - Multivariate Statistics and Applications in R (Complete module description)

2 SWS Lecture (30h contact / 60h self-studies and exam preparation)
2 SWS Seminar (30h contact / 60h statistical data analysis in R)

recommended in semester 1/3
weeks 11-13 of summer semester

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


 

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

Course material will be available in Stud.IP

recommended in semester 1/3
weeks 11-13 of summer semester

End of winter semester

written exam
attendance of at least 70% in the seminars (in addition, mandatory but ungraded)