Stud.IP Uni Oldenburg
University of Oldenburg
25.09.2022 13:28:52
psy111 - Research methods I - Statistical Modeling (Complete module description)
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Module label Research methods I - Statistical Modeling
Modulkürzel psy111
Credit points 6.0 KP
Workload 180 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.
Skills to be acquired in this module
Goals of module:
After completion of this module, students will have basic knowledge in managing and understanding quantitative data and conducting a wide variety of multivariate statistical analyses. They can apply the statistical methodology in terms of good scientific practice and interpret, evaluate and synthesize empirical results in basic and applied research contexts. Students will be aware of statistical misconceptions and they can 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 statistical modeling

  • Graphical representation of multivariate data
  • The Generalized Linear Modeling (GLM) framework
  • Multiple and moderated linear regression with quantitative and qualitative predictors
  • Logistic regression models
  • Multilevel regression (Generalized Linear Mixed Effects Modeling – GLMM)
  • Non-linear regression models (Polynomial regression, regression splines and local regression)
  • Path modeling
  • Factor analysis (exploratory & confirmatory)
  • Structural equation modeling (SEM; linear and non-linear) 


Part 2: Multivariate statistical modeling with R (seminar)

  • Data examples and applications of GLM, GLMM, polynomial, spline and local regression, path modeling, factor analyses and SEM
Literaturempfehlungen
Links
Language of instruction English
Duration (semesters) 1 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: lecture; Parts 2: seminar; additional tutorials are offered.
Vorkenntnisse / Previous knowledge Solid knowledge in basic statistics; otherwise please attend Introductory Course Statistics
Form of instruction Comment SWS Frequency Workload of compulsory attendance
Lecture 2 WiSe 28
Seminar 2 WiSe 28
Tutorial
statistics
WiSe 0
Präsenzzeit Modul insgesamt 56 h
Examination Prüfungszeiten Type of examination
Final exam of module
end of winter term
The module will be tested with a written exam.

Required active participation for gaining credits:
attendance of at least 70% in the seminar (use attendance sheet that will be handed out in the beginning of the term).