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psy112 - Research methods II - Statistical Learning (Complete module description)
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Module label Research methods II - Statistical Learning
Module code psy112
Credit points 6.0 KP
Workload 180 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)
Prerequisites
Enrolment in Master's programme Neurocognitive Psychology.
Skills to be acquired in this module
Goals of module:
Building upon the basic knowledge in multivariate statistical modeling covered in psy111, after completion of this module students will know how to deal with big data to address empirical questions in neurocognitive psychology. They will be able to solve prediction and classification problems to the realm of basic and applied statistical/machine learning purposes. Furthermore, students will understand the specifics of applied research and the statistical modeling of noisy, longitudinal data.

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: Statistical / machine learning methods

  • Supervised and unsupervised statistical learning and prediction
  • Resampling methods
  • Regularized regression
  • Linear and quadatic discriminant analysis
  • Naive Bayes algorithm
  • Tree-based methods
  • Support vector machines
  • The basics of neural networks
  • Principal component regression
  • Clustering methods
     

Part 2: Statistical / machine learning methods with R (voluntary seminar)

  • Data examples and applications of the basic machine learning methods covered in the lecture
     

Part 3: Evaluation research (seminar)

  • 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.)
  • Multivariate statistical modeling of change over time and group differences in change
  • Specific statistical tools for sampling and matching (e.g., Propensity score matching)
  • Basics of causality theory and the estimation of average and conditional effects in EffectLiteR
  • Research synthesis and meta-analysis
Reader's advisory
Links
Language of instruction English
Duration (semesters) 1 Semester
Module frequency The module will start every summer term.
Module capacity unlimited
Modullevel / module level MM (Mastermodul / Master module)
Modulart / typ of module Pflicht / Mandatory
Lehr-/Lernform / Teaching/Learning method Part 1: lecture; Parts 2 and 3: seminars; additional tutorials are offered.
Vorkenntnisse / Previous knowledge psy 111 Research methods I – Statistical Modeling
Course type Comment SWS Frequency Workload of compulsory attendance
Lecture
2 SuSe 28
Seminar
R seminar voluntary
2 SuSe 28
Tutorial
statistics
SuSe 0
Total time of attendance for the module 56 h
Examination Time of examination Type of examination
Final exam of module
end of summer term
The module will be tested with an oral exam (25 min).

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