psy112 - Research methods II - Statistical Learning (Complete module description)

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22.01.2022 15:11:21

Module label	Research methods II - Statistical Learning
Module code	psy112
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: Building upon the basic knowledge in multivariate statistical modeling covered in psy111, after completion of this module students will know how to deal with big data to address empirical questions in neurocognitive psychology. They will be able to solve prediction and classification problems to the realm of basic and applied statistical/machine learning purposes. Furthermore, students will understand the specifics of applied research and the statistical modeling of noisy, longitudinal data. 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: Statistical / machine learning methods Supervised and unsupervised statistical learning and prediction Resampling methods Regularized regression Linear and quadatic discriminant analysis Naive Bayes algorithm Tree-based methods Support vector machines The basics of neural networks Principal component regression Clustering methods Part 2: Statistical / machine learning methods with R (voluntary seminar) Data examples and applications of the basic machine learning methods covered in the lecture Part 3: Evaluation research (seminar) Paradigms and methods in applied evaluation research (quantitative, mixed-methods) Types of studies and designs in evaluation research (experimental, quasi-experimental, (multiple) time series, etc.) Multivariate statistical modeling of change over time and group differences in change Specific statistical tools for sampling and matching (e.g., Propensity score matching) Basics of causality theory and the estimation of average and conditional effects in EffectLiteR Research synthesis and meta-analysis
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Language of instruction	English
Duration (semesters)	1 Semester
Module frequency	The module will start every summer term.
Module capacity	unlimited
Modullevel / module level	MM (Mastermodul / Master module)
Modulart / typ of module	Pflicht / Mandatory
Lehr-/Lernform / Teaching/Learning method	Part 1: lecture; Parts 2 and 3: seminars; additional tutorials are offered.
Vorkenntnisse / Previous knowledge	psy 111 Research methods I – Statistical Modeling

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

Examination	Time of examination	Type of examination
Final exam of module	end of summer term	The module will be tested with an oral exam (25 min). Required active participation for gaining credits: attendance of at least 70% in the mandatory seminar (use attendance sheet that will be handed out in the beginning of the term).