Prospectus

nl en

Multivariate Data Analysis

Course
2024-2025

Students of the Dutch bachelor’s programme, see Multivariate data-analyse

Entry requirements

  • To be admitted to the Multivariate Data Analysis (MVDA) course, students must have successfully completed the Introduction to Methodology and Statistics and Inferential Statistics courses.

  • The MVDA course forms an admission requirements for the third-year bachelors’ project.

Description

This course provides students with an overview of the standard models for the multivariate analysis of psychological research data. Different models are suitable for different types of data. Examples of such models include regression analysis and analysis of variance, as well as more advanced versions of these models. Students learn how to answer a research question by using a model. In addition, they learn to work with relevant statistical software.

Course objectives

At the end of the course:

  • the student has knowledge and understanding of the key concepts and foundational principles of various multivariate techniques (Regression, ANOVA, ANCOVA, logistic regression, MANOVA, repeated measures ANOVA, and mediation analysis);

  • the student is able to determine which analytical method should be used to answer a particular type of research question; and

  • the student is able to carry out the statistical techniques discussed in the course using statistical software package R;

  • the student is able to give substantive interpretations of the output of the multivariate techniques, produced by the statistical software package.

Timetable

For the timetable of this course please refer to MyTimetable

Registration

Education

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

Exams

You must register for each exam in My Studymap at least 10 days before the exam date. Don’t forget! For more information, see the enrolment procedure.
You cannot take an exam without a valid registration in My Studymap.

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

Students who take this course as part of a LDE minor or a premaster programme, exchange students and external guest students will be informed by the education administration about the current registration procedure.

Mode of instruction

7 2-hour lectures, 7 1-hour computer practicals and 7 2-hour work group sessions. Recordings of the lectures are available as weblectures.

Attendance at the computer practicals is mandatory. See Brightspace for more information.

The lectures

Each course week begins with a lecture to introduce and explain course material. The lectures also cover additional and new topics that are included in the examination. As preparation for the lectures students are required to study the chapters assigned for that week. The lectures primarily focus on course objectives 1 and 2.

The computer practicals

The computer practicals and work group sessions take place on the day following the lectures. During the computer practicals students practice data analysis on the basis of exercises. Comparable assignments are also used in the R-skills tests. The computer practicals primarily contribute to course objectives 3 and 4. Attendance is compulsory for the computer practicals. A missed practical has to be compensated at a later time.

Information for retakers:
Students who retake the R test also have to re-attend the R practicals.

The workgroups

Besides the lectures, workgroup sessions are provided in which workgroup exercises will be discussed. Presence at these workgroups is not mandatory, but when students decide attend the workgroups, they are expected to make the specific workgroup assignments in advance. In addition, they are offered the opportunity to practice new exercises during the work group sessions. At the end of each week, a short elaboration of the exercises is published on Brightspace. The work group sessions contribute to course objectives 1, 2 and 3.

Assessment method

The assessment consists of two components:
1. A written examination, each with 4 alternatives, covering both theory and statistical calculations from the literature, the work group sessions, and the lectures (Course objectives 1 and 2).
2. An R skills test covering the various aspects of students’ skills in working with R (Course objective 3) as well as in describing and interpreting statistical output (Course objective 4).

The final grade is a weighted average of the examination grade (60%) and the grade for the R skills test (40%).
Important notice: students who did not fulfill the attendance requirements of the computer practicals will not be allowed to participate in the R test.

The Institute of Psychology uses fixed rules for grade calculation. It also follows the policy of the Faculty of Social and Behavioural Sciences to systematically check student papers for plagiarism with the help of software. All students are required to take and pass the Scientific Integrity Test with a score of 100% in order to learn about the practice of integrity in scientific writing. Students are given access to the quiz via a module on Brightspace. Disciplinary measures will be taken when fraud is detected. Students are expected to be familiar with and understand the implications of these two policies.

Reading list

Texts in the MVDA Exercise book and additional articles on Brightspace. In addition to assignments for the practicals and workgroup sessions, this workbook also contains various texts, all of which are also part of the examination material. The workbook can be ordered from Readeronline.

Contact information