Prospectus

nl en

Applied multivariate data analysis (fall and spring)

Course
2021-2022

Admission requirements

Only open to MSc Psychology (Research) students and MSc (Research) students Developmental Psychopathology in Education and Child Studies.

Description

The complete course Applied Multivariate Data Analysis which runs during an entire calendar year is a joint enterprise of the Institute of Psychology (responsible for the Spring semester) and the Institute of Education and Child Studies (responsible for the Fall semester). Participants can start either in Spring or Fall. The course is part of all Research Master programs in Psychology as well as in Education and Child Studies. Each semester has meetings once a week during a whole semester (see the Course Schedules for precise details). Both semesters consist of a series of topics each of which include “when and why to use a special statistical technique”, “how to use a special technique” and “how to interpret the results”. Each technique could be encountered in academic, clinical and corporate (research) environments. The course aims to provide foundations in a wide range of different techniques, to train methodological experience and flexilibity in many fields.
The examples and exercises will rely heavily on the R software suite (with or without R-studio); basic knowledge and understanding is advised. A short introductory module is provided to cover the bare minimum.

AMDA topics – Fall semester: (1) principal component analysis, confirmatory factor analysis, (2) logistic regression and item response theory, (3) nonparametric regression and nonparametric principal components analysis (using optimal scaling), (4) clustering and (5) meta-analysis.

AMDA topics – Spring semester: (1) analysis plan (2) mediation and moderation, (3) predictive regression, (4) multilevel analysis and longitudinal analysis, and (5) missing data.

The treatment of each topic in the course will have a similar structure, in particular:

  • Exposition of the situations in which a particular technique should be used and why, illustrated with an example from actual research, wherever possible.

  • A summary exposition of the basic principles and the working of the technique and how it can be applied to real data.

  • Discussion of the output of computer programs designed to carry out the analyses.

  • Exercises to gain hands-on experience with the techniques in each topic.

Timetable

The timetables are available through My Timetable.

Mode of instruction

Semester I (Fall):
10 two-hour lectures
10 two-hour (computer) workgroup sessions

Semester II (Spring):
12 two-hour lectures
11 two-hour (computer) workgroup sessions

Assessment method

Performance will be evaluated by both a final exam and by written assignments. The exam will consist of MC questions and short essay questions. Participants work on the assignments in self-chosen pairs.

The assignments consist of executing and (written) reporting on a data analysis with the technique discussed in the current topic. Each assignment will be graded. The final grade for each semester will be determined by the individual exam (75%) and the weighted average of the assignments (25%). Both grades should be higher than 5.5 to pass the course. Any of the assignments can only be resit when the overall assignment grade is unsatisfactory (lower than 5.5).

The Institute of Psychology and the Institute of Education and Child Studies adhere to the policy of the Faculty of Social and Behavioural Sciences to systematically check student papers for plagiarism with the help of software. Disciplinary measures will be taken when fraud is detected. Students are expected to be familiar with and understand the implications of this fraud policy.

Reading list

Digital Syllabus Applied Multivariate Data Analysis – Fall semester, and announced online reading materials.
Digital Syllabus Applied Multivariate Data Analysis – Spring semester.

Registration

Education
It is mandatory to register for each course via uSis. This applies to both the lectures and the working groups, even if they take place online. Without a valid registration in uSis you will not be able to participate in the course and you will not have access to the Brightspace module of the course.

Exams
In addition, it is also mandatory to register separately in uSis for each exam (i.e. both the first exam opportunity and, if necessary, the resit) in uSis. This also applies to partial examinations in a course. This is possible up to 10 calendar days prior to the exam. You cannot take the exam without a valid registration in uSis.

NB If the exam concerns a paper or a practical assignment, you do not need to register in uSis.

Carefully read all information about the procedures and deadlines for registering for courses and exams.

NB After you have registered in uSis, check whether your registration for lectures, working groups and the exam are registered there. This prevents disappointment.

Elective

Students have to enroll for each elective course separately.

Exchange/Study abroad

For admission requirements contact your exchange coordinator.

Contact information