psy111 Research Methods I - Statistical Modeling

Enrolment in Master's programme Neurocognitive Psychology.

Goals of module:
After completion of this module, students will have basic knowledge in managing and understanding quantitative data and conducting a wide variety of multivariate statistical analyses. They can apply the statistical methodology in terms of good scientific practice and interpret, evaluate and synthesize empirical results in basic and applied research contexts. Students will be aware of statistical misconceptions and they can overcome them.

Competencies:
++ interdisciplinary kowledge & thinking
++ statistics & scientific programming
++ data presentation & discussion
+ independent research
+ scientific literature
++ ethics / good scientific practice / professional behavior
++ critical & analytical thinking
++ scientific communication skills
+ group work

Part 1: Multivariate statistical modeling

• Graphical representation of multivariate data
• The Generalized Linear Modeling (GLM) framework
• Multiple and moderated linear regression with quantitative and qualitative predictors
• Logistic regression models
• Multilevel regression (Generalized Linear Mixed Effects Modeling – GLMM)
• Non-linear regression models (Polynomial regression, regression splines and local regression)
• Path modeling
• Factor analysis (exploratory & confirmatory)
• Structural equation modeling (SEM; linear and non-linear) 

Part 2: Multivariate statistical modeling with R (hands-on seminar)

• Data examples and applications of GLM, GLMM, polynomial, spline and local regression, path modeling, factor analyses and SEM

end of winter term

The module will be tested with a written exam.

Required active participation for gaining credits:
attendance of at least 70% in the seminar within one semester (will be checked in StudIP)