Prospectus

nl en

Applied multivariate data analysis (spring)

Course
2024-2025

Admission requirements

Open to MSc Psychology (Research) students and MSc students Education and Child Studies (Research).

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 flexibility in many fields.

Active preparation for the meetings is required; (online) materials will be provided. In the on-campus meetings we will discuss questions, doubts, pitfalls and choices to be made via discussion of examples and (actively prepared) exercises.

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 basics.

AMDA topics – Fall semester: (preparatory) R basic setup and skills, (1) principal component analysis, confirmatory factor analysis, (2) logistic regression and item response theory, (3) clustering and (4) meta-analysis.

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

Each topic in the course will consist of a dynamic mixture of the following aspects. We discuss situations in which a particular technique should be used and why. We will provide a summary exposition of the basic principles and the working of the technique and how it can be applied to real data. We work on exercises to gain hands-on experience with the techniques in each topic, followed by discussion of the output of computer programs designed to carry out the analyses, and choices to make along the way.

Learning objectives

After this course (semester), the student will be able to

  • discuss the concepts and assumptions of the different multivariate techniques

  • perform and validate the statistical analyses using software

  • interpret the results of the statistical analyses

  • communicate the results of the statistical analyses in a written report

  • choose the appropriate technique for a given research question with data

  • weigh advantages and disadvantages of alternative analyses to answer a research question

Timetable

For the timetables of your lectures, work group sessions, and exams, see the timetables page of your study program. You will also find the enrolment codes here. All students from all institutes can find the time tables here: My Timetable.

Mode of instruction

Semester I (Fall):
10 two-hour lectures
Weekly two-hour on-campus classes

Semester II (Spring):
10 two-hour lectures
14 two-hour (computer) workgroup sessions

Attendance at the workgroup sessions is mandatory. See Brightspace for more information.

Assessment method

Performance will be evaluated by both a final exam and by written assignments. Participants work on the written 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 (4 per semester) will be graded. The final grade for each semester will be determined by the individual exam grade (60%) and the average score for the assignments (40%). Both grades should be higher than 5.5 to pass the course. Both the exam and (any of the individual) assignments can be resit when that specific partial 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.

Exam materials

In order to prepare for the exam, you need to study the lectures, practical assignments and written home assignments.

Reading list

Digital Syllabus Applied Multivariate Data Analysis – Fall semester
Digital Syllabus Applied Multivariate Data Analysis – Spring semester.
Announced online reading materials

Registration

Education
Students must register themselves for all course components (lectures, tutorials and practicals) they wish to follow. You can register via My Studymap up to 5 days prior to the start of the course.

Elective (Only applicable to students Psychology (Research))Students have to enroll for each elective course separately.

Exchange/Study abroad
For admission requirements contact your exchange coordinator.

Exams
It is mandatory for all students to register for each exam. This is possible up to and including 10 calendar days prior to the examination. You cannot take an exam without a valid pre-registration.

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

Elective

Students have to enroll for each elective course separately.

Exchange/Study abroad

For admission requirements contact your exchange coordinator.

Contact information