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Applied Multivariate Statistics (2024/2025: Semester 1 – Fall)
Course aim
After completing this course students are able to:
- understand various statistical analysis techniques such as (multiple) regression and various ANOVA techniques;
- conduct advanced statistical analyses using JASP;
- read JASP output of advanced statistical techniques;
- report statistical results in APA-format;
- choose the correct statistical analysis technique for a specific research question/data collection method.
Relationship between assessment and learning goals:
In this course, students are assessed 4 times :
- Written in-class exam 1: this tests the knowledge and the understanding of statistical terminology and the ability to interpret and report JASP output covered in week 1-4 of the course (course goals 1, 3, and 4).
- Written in-class exam 2: this tests the knowledge and the understanding of statistical terminology and the ability to interpret and report JASP output covered in week 6-9 of the course (course goals 1, 3, and 4).
- Computing test: this tests the ability to conduct the analyses covered in the course using the software JASP (course goal 2).
- Participation/HW: completion of the assigned homework and participation in the in-class exercises (course goals 2, 3, and 4).
Course content
The content of this course overlaps with UCACCMET22 and UCACCSTA21. Do not take both.
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 Multivariate statistical analysis techniques course, the theory and application of multivariate statistical models is discussed in depth. The techniques that are dealt with include multiple regression analysis, mediation, moderation, AN(C)OVA, MAN(C)OVA, and repeated measures analysis. These models can be used to investigate the relations among many variables simultaneously, and thus provide an accurate description of the subject of interest.
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 Multivariate statistical analysis techniques course, the theory and application of multivariate statistical models is discussed in depth. The techniques that are dealt with include multiple regression analysis, mediation, moderation, AN(C)OVA, MAN(C)OVA, and repeated measures analysis. These models can be used to investigate the relations among many variables simultaneously, and thus provide an accurate description of the subject of interest.
Format
For a period of 10 weeks, students and teachers meet twice a week for two hours. During the first meeting, the teachers introduce the topic of the week. Before the second meeting, the 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. The results of these analyses will be discussed by the students and the instructor during the second half of the class meeting.
Directly following this 5 ECTS Multivariate statistical analysis course, students continue in one of three separate 5 week course modules (2,5 ECTS). Separate outlines are available for these course modules. The topics of these course modules are:
- UCACCMET2A: Analysis of Behavioral data (Spring semester)
This module is recommended for students with an interest in different areas of psychology and cognitive sciences and focuses mainly on factor analysis techniques. - UCACCMET2B: Analysis of Societal data (Fall semester)
This module is recommended for students with an interest in sociology, economics, geography, criminology, empirical political sciences, social psychology and focuses mainly on logistic regression analysis. - UCACCMET2D: Qualitative research techniques (Spring semester)
This module is recommended for students with an interest in non-quantitative data, as often used in for instance anthropology.
Other modules (UCACCMET2x) also count towards the SSC methodology requirement if specifically stated in the comments section of the outline. They are taught in different timeslots though and may have priority placement for other student groups.
Instructional formats
UCU course
Examination
Exam 1
Required | Weight 35% | ECTS 1.75
Exam 2
Required | Weight 35% | ECTS 1.75
Computing test
Required | Weight 20% | ECTS 1
Participation/HW part 1
Required | Weight 5% | ECTS 0.25
Participation/HW part 2
Required | Weight 5% | ECTS 0.25
*midterm FEEDBACK*
Not required
Entry requirements and preknowledge
Entry Requirements
The following course module must be completed:
Preknowledge
No data about preknowledge is available.
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
Succeeded by a 2,5 ECTS module (UCACCMET2x) within the same semester. Counts towards SCC or HUM methodology.
Coördinator
| dr. K.T. Silvester PhD | k.t.silvester@uu.nl |
Lecturers
| A.M. Scheel | a.m.scheel@uu.nl |
Enrolment
Attention: this course is not open to students from other faculties, so subsidiary students can't enroll for this course.
Go to OSIRIS-enrolments
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