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 Lehrende anzeigen
    • Prof. Dr. Andrea Hildebrandt
    • Natalia Castro Gonzalez
    Termine anzeigen
    • Friday, 12:15 - 17:45, Weekly (from 21.10.22)
    • Friday, 14.10.22, 12:15 - 17:45 o'clock

    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) Lehrende anzeigen
    • Prof. Dr. Andrea Hildebrandt
    Termine anzeigen
    • 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.
Method of assesment

The module will be tested with a written exam.
Exam dates

end of winter term
Learning outcomes/competencies

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