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Applied Multivariate Data Analysis (Fall) - Education and Child Studies

Vak 2017-2018

NB Language of instruction is English

Admission requirements

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

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 component analysis (using optimal scaling), (4) item response theory and (5) meta-analysis.
Topics of AMDA – Spring semester: (1) mediation and moderation, (2) quasiexperimental 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:
1. Exposition of the situations in which a particular technique should be used and why, illustrated with an example from actual research, wherever possible.
2. A summary exposition of the basic principles and the working of the technique and how it can be applied to real data.
3. Discussion of the output of computer programs designed to carry out the
analyses

Course objectives

After completion of this course, the student
1 Has a basic understanding of the techniques discussed in the different topics
2 Is able to use different software packages to perform statistical analyses for the techniques discussed
3 Is able to write a result section of a scientific manuscript on the basis of the output of the statistical analysis
4 Has the ability to choose the appropriate technique for a given research question with data
5 Knows which assumptions are made in the different analyses and is able to validate the analysis.

Timetable

Lectures

Blackboard

Blackboard

Mode of instruction

  • Semester I (Fall): 10 lectures of two hours and 15 computer labs (supervised) of two hours
  • Semester II (Spring): 11 lectures of two hours and 14 computer labs (supervised) of two hours
  • Independent study of the literature.

Assessment method

Participants have to hand in assignments individually. 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 a (weighted) average of the individual grades with the additional requirements that each assignment should be graded with at least a 4 and that each assignment passes the plagiarism check. Any form or level of detected plagiarism is handled by the Exam Committee.

In semester I there are 5 assignments.

In semester II there are 4 assignments.

Blackboard

During this course Blackboard will be used.

Reading list

Syllabus Applied Multivariate Data Analysis – Fall semester.

Syllabus Applied Multivariate Data Analysis – Spring semester.

Registration

Please note that separate uSis registration is mandatory for lectures, seminars, exams and re-exams. Student who do not register, cannot attend courses or take exams.

Courses

  • Registration for the lectures of the course is possible as of 100 calendar days through 10 calendar days before the first lecture at the latest;
  • Registration for the seminars of the course is possible as of 100 calendar days through 10 calendar days before the first seminar at the latest.

Exams

More information on exam registrationWritten assignments, no exam registration required.

Contact information

Fall semester
Dr. R.C.A. Rippe
• rrippe@fsw.leidenuniv.nl

Spring semester
Prof. Dr. M.J. De Rooij
• rooijm@fsw.leidenuniv.nlFall

Remarks

The course is a collaboration between the Institute of Education and Child Studies and the Institute of Psychology.