Statistics IV: Multivariate Data Analysis

6 ECTS credits
180 h study time

Offer 2 with catalog number 1009171BNR for all 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.

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

1) Preparatory descriptive data analysis (analysis of missing data, checking assumptions, detection of outliers, data transformation, bootstrapping)

2) General introduction to the General Linear Model

3) (Multiple) linear regression analysis

4) Logistic regression analysis

5) One-way ANOVA

6) Two-way ANOVA, Repeated-measures ANOVA, Mixed ANOVA

7) Factor analysis (PCA, PFA) including introduction to functional PCA

8) Cluster analysis including introduction to functional cluster analysis

9) Structural Equation Modeling (PA, CFA, ...)

Course material

Course text (Required) : Multivariate data-analyse, Theuns - Van Den Bussche - Isaac - Muscarelle, 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, 7de, Pearson, 9781473756540, 2018

Additional info

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

Additional material:
Slides, datasets, 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 can perform a data-cleaning of a realistic dataset independently and motivate the made decisions.
- The student can execute an elementary analysis of the missing data process independently and knows the basics and risks of imputation methods.
- The student understands the theoretical background of the methods considered (those are: multiple and logistic regression, multiple ANOVA, factor analysis, cluster analysis and structural equation modeling).
- The student can perform the considered analyses independently for realistic data from psychological research using statistical software.
- The student suggests proper data analysis methods considering the research design and the data.
- The student can interpret the results of data analyses and report the findings correctly as is required for scientific publications.
- The student reflects critically about the applicability of the considered methods to data that do or do not violate the working hypotheses for the method.

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:

Written exam with a relative weight of 1 which comprises 60% of the final mark.

Note: Written exam: theory and exercises. Schriftelijke examenproeven gebeuren met gesloten boek, een formularium (zie PointCarré) wordt bij elk examen ter beschikking gesteld.

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

Practical exam: exercises PC with a relative weight of 1 which comprises 40% of the final mark.

Note: Practical exam: exercises on the computer

Additional info regarding evaluation

The result on either the theoretical or practical exam obtained during the first exam term, will not be transferred to the second term. Students who have more than 10/20 on one of these two exams, can choose to transfer that score to the second term, given that they notify the lecturer by email. Scores on one of the two exams cannot be transferred to the next 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