Prospectus

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Applied Multivariate Data Analysis (Spring) - Mini-Courses in Statistics

Course 2016-2017

Admission requirements

Only open to MSc Psychology (research) students and MSc students Education and Child Studies.

Description

The complete course Applied Multivariate Data Analysis which runs during an entire calendar year is a joint undertaking 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 the spring or the fall. The course is part of all Research Master programmes in Psychology and Education and Child Studies. Each part has meetings once a week during a whole semester (see the Course Schedules for precise details). Both parts 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”.

Topics of AMDA – Fall semester: (1) principal component analysis, confirmatory factor analysis and structural equation modeling, (2) logistic regression, (3) nonparametric regression and nonparametric principal components analysis (using optimal scaling), (4) item response theory and (5) meta-analysis.

Topics of AMDA – Spring semester: (1) mediation and moderation, (2) quasi-experimental design, (3) multilevel analysis and longitudinal analysis, and (4) missing data.

The treatment of each topic in the course will have a similar structure, in particular:
Exposition of the situations in which a particular technique should be used and why, illustrated with an example from actual research, wherever possible.

A summary exposition of the basic principles and the working of the technique and how it can be applied to real data.
Discussion of the output of computer programs designed to carry out the analyses.

Course objectives

After completion of this course, the student

  • has a basic understanding of the techniques discussed in the different topics
  • is able to use different software packages to perform statistical analyses for the techniques discussed
  • is able to write a result section of a scientific manuscript on the basis of the output of the statistical analysis
  • has the ability to choose the appropriate technique for a given research question with data; and
  • knows which assumptions are made in the different analyses and is able to validate the analysis.

Timetable

Semester 1:
Lectures

Semester 2:
Lectures

Blackboard

Blackboard

Course

Students need to enroll for lectures (and work group sessions). Please consult the instructions for registration.

Elective

Students have to enroll for each elective course separately.

Exchange/Study abroad

For admission requirements contact your exchange coordinator.

Examination

Students are not automatically enrolled for an examination. They can register via uSis from 100 to 10 calendar days before the date; students who are not registered will not be permitted to take the examination.

Mode of instruction

Semester I: 10 lectures of 2 hours and 15 computer labs of 2 hours
Semester II: 11 lectures of 2 hours and 14 computer labs of 2 hours

Assessment method

Participants have to hand in assignments. The assignments consist of a data analysis with the technique discussed in the current topic. Each assignment will be graded. The final grade for each semester will be an average of the individual grades with the additional requirement that each assignment should be graded with at least a 4.
In semester I there are 5 assignments.
In semester II there are 4 assignments.

The Faculty of Social Sciences has instituted that instructors use a software programme for the systematic detection of plagiarism in students’ written work. In case of fraud disciplinary actions will be taken. Please see the information concerning fraud.

Reading list

Syllabus Applied Multivariate Data Analysis – Fall semester.
Syllabus Applied Multivariate Data Analysis – Spring semester.

Contact information

Fall semester
Dr. Ralph Rippe
rrippe@fsw.leidenuniv.nl

Spring semester
Prof. dr. Mark de Rooij
rooijm@fsw.leidenuniv.nl