psy111 - Research methods I - Statistical Modeling (Complete module description)

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24.01.2022 12:41:39

Module label	Research methods I - Statistical Modeling
Module code	psy111
Credit points	6.0 KP
Workload	180 h
Institute directory	Department of Psychology
Applicability of the module	Master's Programme Neurocognitive Psychology (Master) > Mastermodule
Responsible persons	Hildebrandt, Andrea (Module responsibility) Hildebrandt, Andrea (Authorized examiners)
Prerequisites	Enrolment in Master's programme Neurocognitive Psychology.
Skills to be acquired in this module	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
Module contents	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 (seminar) Data examples and applications of GLM, GLMM, polynomial, spline and local regression, path modeling, factor analyses and SEM
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Language of instruction	English
Duration (semesters)	1 Semester
Module frequency	The module will start every winter term.
Module capacity	unlimited
Modullevel / module level	MM (Mastermodul / Master module)
Modulart / typ of module	Pflicht / Mandatory
Lehr-/Lernform / Teaching/Learning method	Parts 1: lecture; Parts 2: seminar; additional tutorials are offered.
Vorkenntnisse / Previous knowledge	Solid knowledge in basic statistics; otherwise please attend Introductory Course Statistics

Course type	Comment	SWS	Frequency	Workload of compulsory attendance
Lecture		2	WiSe	28
Seminar		2	WiSe	28
Tutorial	statistics		WiSe	0
Total time of attendance for the module				56 h

Examination	Time of examination	Type of examination
Final exam of module	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 (use attendance sheet that will be handed out in the beginning of the term).