Statistics IV: Multivariate Data Analysis

6 ECTS credits
180 h study time

Offer 1 with catalog number 1009170BNW for working students in the 2nd semester at a (B) Bachelor - advanced level.

Semester

2nd semester

Enrollment based on exam contract

Impossible

Grading method

Grading (scale from 0 to 20)

Can retake in second session

Yes

Enrollment Requirements

For this course you have to meet certain enrolment requirements. For an overview of the enrolment requirements check https://www.vub.be/en/studying-vub/practical-info-for-students/study-guidance/study-path/individual-study-path#paragraph--id--71647 Students must have taken ‘Statistics I: measurement Scales and descriptive Statistics’ (not for students in a shortened study program), 'Statistics II: probability Theory and inductive Statistics' and 'Statistics III: univariate Data Analysis', before they can enroll in ‘Statistics IV: Multivariate Data Analysis’. Students who are enrolled in a shortened study program can take this course unit. Registration for this course is only possible for working students. Day students can register for courses whose code ends with an R. At Inschrijven / studentenadministratie@vub.be you must be registered at the VUB as a working student for the current academic year.

Taught in

Dutch

Faculty

Faculty of Psychology and Educational Sciences

Department

Experimental and Applied Psychology

Educational team

Alain Isaac
Olivier Mairesse (course titular)

Activities and contact hours

26 contact hours Lecture
39 contact hours Seminar, Exercises or Practicals
63 contact hours Independent or External Form of Study

Course Content

Preliminary descriptive data analysis (analysis of missing data, checking assumptions, detection of outliers, data transformation, bootstrapping)
Multiple and logistic regression
Introduction to mediation and moderation analysis
One-way and two-way ANOVA, Repeated-measures ANOVA, Mixed ANOVA
Factor analysis (PCA, PFA; including introduction to functional PCA)
Cluster analysis (including introduction to functional cluster analysis)
Structural Equation Modeling (PA, CFA, ...)
Introduction to network analysis (Gaussian graphical models, Ising models, Directional Acyclic Graphs, ...)

Course material

Course text (Required) : Multivariate data-analyse, Theuns - Van Den Bussche - Isaac - Muscarella, VUB, 2220170003047, 2016
Handbook (Recommended) : Statistiek in de Praktijk, Theorieboek, Moore & McCabe, 5de herziene druk, Academic Service, 9789039523605, 2006
Digital course material (Required) : Slides, datasets, etc., Canvas
Handbook (Recommended) : Multivariate data analysis, Hair, Black, Babin, & Anderson, 8de, Cengage, 9781473756540, 2018

Additional info

Syllabus:
Theuns, P., Van den Bussche, E., Isaac, A., & Muscarella, C. Multivariate data-analyse. Brussel: VUB-uitgaven

Additional material:
Slides, datasets, scientific articles, etc. are made available through the learning platform.

Complementary study material:

1) (basic) Moore & McCabe (2012). Statistiek in de Praktijk (5th ed.). Academic Service (ISBN 9789039523605).
This book is recommended only as supplementary to the lectures. It covers all required previous knowledge and the first chapters of the present course (regression analysis and analysis of variance).

2) (advanced) Hair, Black, Babin, & Anderson (2014). Multivariate data analysis (7th ed.). Pearson (ISBN 0139305874).
This textbook covers only (advanced) multivariate data-analysis. It covers most of the course and can be used for further reference on methods that are not covered in the present course.

For more information about the practical organization of the course: see the learning platform.

Learning Outcomes

General competences

- The student is able to independently perform a data cleaning of a realistic dataset and motivate the choices made in the process.
- The student is able to independently conduct a basic analysis of missing data processes and understands the basic principles and risks of imputation methods.
- The student understands the theoretical background of the methods covered (especially multiple and logistic regression analysis, multiple analysis of variance, repeated measures analysis of variance, factor analysis, cluster analysis, basic functional data analysis, structural equation models, mediation and moderation analysis, and basic network analyses).
- The student is able to independently apply the covered data analytical methods to realistic data from psychological research using statistical software.
- The student proposes appropriate data analysis methods (from the covered methods) based on the research design and the data.
- The student is able to interpret the results of a data analysis and report them correctly as required in scientific publications.
- The student critically reflects on the applicability of the covered methods to data that deviates more or less from the proposed working hypotheses.

Grading

The final grade is composed based on the following categories:
Written Exam determines 60% of the final mark.
Practical Exam determines 40% of the final mark.

Within the Written Exam category, the following assignments need to be completed:

Schriftelijk examen with a relative weight of 1 which comprises 60% of the final mark.

Note: Mandatory written exam (theory and exercises "by hand"). Written exam tests are done with closed book, a formularium (see learning platform) is provided with each exam.

Within the Practical Exam category, the following assignments need to be completed:

Praktisch examen PC-oefeningen with a relative weight of 1 which comprises 40% of the final mark.

Note: Mandatory practical exam: exercises on computer.

Additional info regarding evaluation

Students need to pass for both exam components (minimum score of 10/20 for theory and for practice). However, students who scored more than 10/20 for an exam component in the first session can transfer this result to the second session, provided that the teacher is informed and agrees. Partial results cannot be transferred to a following academic year.

Allowed unsatisfactory mark

The supplementary Teaching and Examination Regulations of your faculty stipulate whether an allowed unsatisfactory mark for this programme unit is permitted.

Academic context