Stud.IP Uni Oldenburg
University of Oldenburg
29.11.2021 01:51:32
psy111 - Research methods I - Statistical Modeling
Department of Psychology 6 KP
Module components Semester courses Wintersemester 2021/2022 Examination
Lecture
  • Limited access 6.02.111_1L - Multivariate statistics I Show lecturers
    • Prof. Dr. Andrea Hildebrandt

    Wednesday: 08:15 - 09:45, weekly (from 20/10/21)
    Dates on Monday. 21.02.22 08:00 - 10:00

    hybrid 59 students can attend in presence, the others can watch the recoreded lectures online. For participation in presence, you need to register for each session you want to attend by signing up to the respective group. The class in only open for Neurocognitive Psychology, Neuroscience and PhD students, NOT for Biology students.

Seminar
  • Limited access 6.02.111_2_Gr1 - analysis methods with R - group 1 in presence Show lecturers
    • Juan Felipe Quinones Sanchez

    Wednesday: 12:15 - 13:45, weekly (from 20/10/21)

    Classes will take place in the lecture hall. Students will work on online R tutorials and the teacher will answer questions.

  • Limited access 6.02.111_2_Gr2 - analysis methods with R - group 2 online Show lecturers
    • Juan Felipe Quinones Sanchez

    Wednesday: 16:15 - 17:45, weekly (from 20/10/21)

    The first 3 classes will take place as BBB sessions, the other classes online with R markdown tutorials and BBB Q&A sessions from 16-17h. The course is open for Neurocognitive Psychology and Neuroscience students.

Tutorial
(
statistics
)
  • Limited access 6.02.001 - Introductory Course Statistics Show lecturers
    • Prof. Dr. Andrea Hildebrandt

    Friday: 12:15 - 17:45, weekly (from 22/10/21)

    Hybrid 59 students can attend in presence (3G), the others can attend online. For participation in presence, you need to register for each session you want to attend by signing up to the respective group. This course is designed for students who are completely new to the world of statistics and for those who have the feeling that many statistical concepts they learned about earlier are not present to them anymore. Relying on theoretical input and applied exercises, this interactive lecture covers all those topics that need to belong to students’ procedural knowledge in order to be able to follow the topics covered by the Psychological methods module. Course contents • Empirical research, variables and scales • Statistical parameter • Graphical data visualization • Probability theory • Probability distributions • Statistical sampling • Hypothesis testing • Testing hypothesis on differences • Correlation • Simple linear regression

  • Limited access 6.02.111_1T - Multivariate statistics I (Tutorial) Show lecturers
    • Prof. Dr. Andrea Hildebrandt
    • Maira Keller

    Tuesday: 16:15 - 17:45, weekly (from 19/10/21)

    34 students can attend in presence, the others can attend online. For participation in presence, you need to register for each session you want to attend by signing up to the respective group. Additonal voluntary tutorial for the multivariate statistics lecture. If you are from another study program, please contact the teacher.

Notes for the module
Prerequisites
Enrolment in Master's programme Neurocognitive Psychology.
Time of examination
end of winter term
Module examination
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).
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

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