psy110 - Research methods (Vollständige Modulbeschreibung)

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Stud.IP Uni Oldenburg

31.03.2023 12:13:17

Modulbezeichnung	Research methods
Modulkürzel	psy110
Kreditpunkte	12.0 KP
Workload	360 h
Einrichtungsverzeichnis	Department für Psychologie
Verwendbarkeit des Moduls	Master Neurocognitive Psychology (Master) > Mastermodule
Zuständige Personen	Hildebrandt, Andrea (Modulverantwortung) Hildebrandt, Andrea (Prüfungsberechtigt)
Teilnahmevoraussetzungen	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.)
Kompetenzziele	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
Modulinhalte	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
Unterrichtssprache	Englisch
Dauer in Semestern	2 Semester
Angebotsrhythmus Modul	The module will start every winter term.
Aufnahmekapazität Modul	unbegrenzt
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

Lehrveranstaltungsform	Kommentar	SWS	Angebotsrhythmus	Workload Präsenz
Vorlesung		4	SoSe und WiSe	56
Seminar	R seminar in summer is voluntary	4	SoSe und WiSe	56
Tutorium	statistics		SoSe und WiSe	0
Präsenzzeit Modul insgesamt				112 h

Prüfung	Prüfungszeiten	Prüfungsform
Gesamtmodul		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).