psy110 - Research methods (Complete module description)

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28.03.2023 11:24:25

Module label	Research methods
Modulkürzel	psy110
Credit points	12.0 KP
Workload	360 h
Institute directory	Department of Psychology
Verwendbarkeit des Moduls	Master's Programme Neurocognitive Psychology (Master) > Mastermodule
Zuständige Personen	Hildebrandt, Andrea (Module responsibility) Hildebrandt, Andrea (Prüfungsberechtigt)
Prerequisites	Enrolment in Master's programme Neurocognitive Psychology. Module psy110 is only relevant for students who started their studies before winter term 21/22. (All other students study modules psy111 and psy112.)
Skills to be acquired in this module	Goals of 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 and statistical learning in basic and applied research context. The courses in this module will additionally point out statistical misconceptions and help students to 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 Statistics I (lecture): winter 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): winter and summer Data examples and applications of GLM, GLMM, polynomial, spline and local regression, path modeling, factor analyses and SEM Part 3: Multivariate Statistics II (lecture): summer Supervised and unsupervised statistical learning and prediction Regularized regression Resampling methods Tree-based methods Support Vector Machines Neural Networks (basics) Principal components and clustering Part 4: Evaluation research (seminar): summer 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.) Specific statistical tools (e.g., Propensity score matching) Research synthesis and meta-analysis
Literaturempfehlungen
Links
Language of instruction	English
Duration (semesters)	2 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 and 3: lectures; Parts 2 and 4: seminars; additional tutorials are offered.
Vorkenntnisse / Previous knowledge	basic statistics; otherwise please attend Introductory Course Statistics

Form of instruction	Comment	SWS	Frequency	Workload of compulsory attendance
Lecture		4	SoSe und WiSe	56
Seminar	R seminar in summer is voluntary	4	SoSe und WiSe	56
Tutorial	statistics		SoSe und WiSe	0
Präsenzzeit Modul insgesamt				112 h

Examination	Prüfungszeiten	Type of examination
Final exam of module		The module will be tested with an oral exam (20 min). Required active participation for gaining credits: attendance of at least 70% in the seminars (use attendance sheet that will be handed out in the beginning of the term).