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Applied Multivariate Statistics (2026/2027: Semester 1 – Fall)
Course aim
- understand various statistical analysis techniques such as linear and logistic regression, various ANOVA techniques, and data reduction techniques;
- conduct the corresponding statistical analyses using the software JASP;
- interpret JASP output;
- determine which statistical technique should be used for a specific research question/data collection method;
- report statistical results in APA-format
Relationship between tests and course goal
- Exam 1: this tests the knowledge and the understanding of statistical terminology and the ability to interpret and report JASP output covered in the first half of the course (course goals 1, 3, 4, and 5).
- Exam 2: this tests the knowledge and the understanding of statistical terminology and the ability to interpret and report JASP output covered in the second half of the course (course goals 1, 3, 4, and 5).
- Computing test: this tests the ability to conduct the analyses covered in the course using the software JASP (course goal 2).
- Project/HW: completion and participation in the project and assigned homework (course goals 1, 2, 3, 4, and 5).
Course content
The subjects investigated by social scientists are rather complex; as a result, it is usually necessary to use many (‘multi’) variables to be able to give an adequate description of a subject. In the course Multivariate statistical analysis techniques, the theory and application of multivariate statistical models is discussed in depth.
In this course, the techniques that are dealt with include: multiple linear regression analysis, moderation, mediation, logistic regression, ANOVA, ANCOVA, repeated measures analysis, data reduction techniques, and reliability analysis . These techniques are useful for a range of different research questions that are investigated in various branches of social sciences such as psychology, cognitive sciences, sociology, economics, human geography, criminology, and political sciences.
Fall and Spring semester format
For a period of 15 weeks, there are two 2-hour meetings per week. During the first meeting, the instructor introduces the topic of the week. Before the second meeting, students have to practice with executing statistical analyses in JASP related to the topic of the week. During the second meeting, the students will conduct more elaborate analyses and write a report on the results.
Summer term format
During the 5-week full-time summer period, there are two 2-hour meetings per day for three days a week. During the first meeting, the instructor introduces the topic of the week. Before the second meeting, students have to practice with executing statistical analyses in JASP related to the topic of the week. During the second meeting, students will conduct more elaborate analyses and write a report on the results.
For questions about the course, requirements and entrance tests, contact the course coordinator.
Instructional formats
Examination
Project/HW
Required | Weight 20% | ECTS 1.5
Exam 1
Required | Weight 30% | ECTS 2.25
Exam 2
Required | Weight 35% | ECTS 2.63
Computing test
Required | Weight 15% | ECTS 1.13
Entry requirements and preknowledge
Entry Requirements
The following course module must be completed:
Preknowledge
Passed the JASP exam of UCACCMET12 Research in Practice with at least a D (1.0). Students who did not pass this exam with a D and wish to take the course UCACCMET22, can contact the course coordinator of UCACCMET12 to schedule a separate exam to meet this entrance requirement.
Languages
- English
Competences
-
Academic writing
-
Research skills
Course Iterations
Related studies
Exams
There is no timetable available of the exams
Required Materials
-
BOEKTBA
Recommended Materials
No information available on the recommended literature
Remarks
From Fall 2026 onward, this course replaces UCACCMET23 + module. Counts towards SCC or HUM methodology.
Coördinator
| dr. K.T. Silvester PhD | k.t.silvester@uu.nl |
Lecturers
| dr. K.M. Meitinger | k.m.meitinger@uu.nl |
| dr. M. Shafiee Kamalabad | m.shafieekamalabad@uu.nl |
Enrolment
Go to OSIRIS-enrolments
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