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Statistics & Data Analysis II


Module name (EN):
Name of module in study programme. It should be precise and clear.
Statistics & Data Analysis II
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Business Psychology, Bachelor, regulation 01.10.2026
Module code: BWP-305
SAP-Submodule-No.:
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
P470-0017
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
2V+2U (4 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
5
Semester: 3
Mandatory course: yes
Language of instruction:
German
Assessment:
Project work

[updated 09.11.2026]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

BWP-305 (P470-0017) Business Psychology, Bachelor, regulation 01.10.2026 , semester 3, mandatory course
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
60 class hours (= 45 clock hours) over a 15-week period.
The total student study time is 150 hours (equivalent to 5 ECTS credits).
There are therefore 105 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
None.
Recommended as prerequisite for:
Module coordinator:
Prof. Dr. Susan Pulham
Lecturer:
Prof. Dr. Susan Pulham


[updated 17.09.2026]
Learning outcomes:
After successfully completing this course, students will be able to:
- independently select inferential statistical methods,
- perform statistical analyses using statistical software,
- check the assumptions of statistical methods,
- interpret effect sizes,
- replicate scientific studies,
- critically evaluate empirical findings,
- report results according to APA standards,
- evaluate scientific claims based on statistical evidence.

[updated 09.11.2026]
Module content:
1. One-factor analysis of variance
2. Multifactor analysis of variance
3. Multiple regression
4. Moderation and mediation (introduction)
5. Factor analysis and scale development
6. Nonparametric methods

[updated 09.11.2026]
Recommended or required reading:
Pulham, S. (2011): Statistik leicht gemacht
Georg, L. J.; Georg, S.; Pulham, S. (2026): Einfach nur Wahrscheinlichkeitsrechnung
Field, A.; Miles, J. N. V. (2026): Discovering Statistics Using R
Eid, M.; Gollwitzer, M.; Schmitt, M. (2026): Statistik und Forschungsmethoden
Bortz, J.; Schuster, C. (2017): Statistik für Human- und  Sozialwissenschaftler
Bühner, M.; Ziegler, M. (2017): Statistik für Psychologen und Sozialwissenschaftler

[updated 09.11.2026]
 
[Sun Nov 15 00:34:48 CET 2026, CKEY=wsxdia, BKEY=wpsy, CID=BWP-305, LANGUAGE=en, DATE=15.11.2026]