psy111 - Research methods I - Statistical Modeling (Course overview)

psy111 - Research methods I - Statistical Modeling (Course overview)

Department of Psychology 6 KP
Module components Semester courses Wintersemester 2022/2023 Examination
Lecture
Seminar
Tutorial
(
statistics
)
  • No access 6.02.001 - Introductory Course Statistics Show lecturers
    • Prof. Dr. Andrea Hildebrandt
    • Natalia Castro Gonzalez

    Friday: 12:15 - 17:45, weekly (from 21/10/22)
    Dates on Friday, 14.10.2022 12:15 - 17:45
    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
  • No access 6.02.111_1T - Multivariate statistics I (Tutorial) Show lecturers
    • Prof. Dr. Andrea Hildebrandt

    Tuesday: 10:15 - 11:45, weekly (from 18/10/22)
    Additonal voluntary tutorial for the multivariate statistics lecture. If you are from another study program, please contact the teacher.
Hinweise zum Modul
Prerequisites
Enrolment in Master's programme Neurocognitive Psychology.
Prüfungszeiten
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 within one semester (will be checked in StudIP)
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