Prospectus

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Multivariate Data Analysis

Course
2021-2022

Entry requirements

  • To be admitted to the Multivariate Data Analysis (MVDA) course, students must have successfully completed the Introduction to Methods 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 variance analysis, 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

  • Knowledge and understanding of the key concepts and foundational principles of the standard models for multivariate data analysis;

  • Learning which analytical method to use to answer a particular type of research question; and

  • Acquiring skills in working with statistical software for multivariate data analysis.

Timetable

For the timetable of this course please refer to MyTimetable

Registration

NOTE As of the academic year 2021-2022, you must register for all courses in uSis. You do this twice a year: once for the courses you want to take in semester 1 and once for the courses you want to take in semester 2.
Registration for courses in the first semester is possible from July. Registration for courses in the first semester is possible from December.
The exact date on which the registration starts will be published on the website of the Student Service Center (SSC). First year Bachelor students as well as premaster students will be registered by the Student Service Center; they do not need to register themselves.

The registration period for all courses closes five calendar days before the start of the course.
Also read the complete 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.

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 practise data analysis on the basis of exercises. Comparable assignments are also used in the SPSS-skills tests. The computer practicals primarily contribute to course objective 3. Attendance is compulsory for the computer practicals. A missed practical has to be compensated at a later time.

The workgroups

Students are expected to prepare for the work group sessions by completing a number of assignments and handing them in prior to the session as a part of the attendance requirements. The completed assignments are discussed during the work group sessions. Students are also expected to give a minimum of one presentation in a small group. In addition, they are offered the opportunity to practise new erxercises during the work group sessions. At the end of each week, a short elaboration of the exercises is published on Blackboard. The work group sessions contribute to course objectives 1, 2 and 3. Attendance is compulsory for the workgroups. Contrary to the computer practicals it is not possible to compensate for a missed workgroup.

Assessment method

The assessment consists of two components:
1. A written examination consisting of 40 multiple-choice questions, 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 SPSS skills test covering the various aspects of students’ skills in working with SPSS as well as in describing and interpreting statistical output (Course objective 3).

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

The Institute of Psychology uses fixed rules for grade calculation and compulsory attendance. 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. Disciplinary measures will be taken when fraud is detected. Students are expected to be familiar with and understand the implications of these three policies.

Reading list

Texts in the MVDA Exercise book and additional articles on Blackboard. 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