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16.05.2021 04:39:26
psy110 - Research methods (Complete module description)
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Module label Research methods
Module code psy110
Credit points 12.0 KP
Workload 360 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)
Enrolment in Master's programme Neurocognitive Psychology.
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

++ 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
Reader's advisory
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
Course type Comment SWS Frequency Workload of compulsory attendance
4.00 SuSe and WiSe 56
R seminar in summer is voluntary
4.00 SuSe and WiSe 56
0.00 SuSe and WiSe 0
Total time of attendance for the module 112 h
Examination Time of examination 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).