Stud.IP Uni Oldenburg
Universität Oldenburg
31.03.2023 12:13:17
psy110 - Research methods (Vollständige Modulbeschreibung)
Originalfassung Englisch PDF Download
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)
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.)
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

++ 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 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
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
R seminar in summer is voluntary
4 SoSe und WiSe 56
SoSe und WiSe 0
Präsenzzeit Modul insgesamt 112 h
Prüfung Prüfungszeiten Prüfungsform
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).