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2019-2020

Course Objective

After this course, the students will understand the basic principles of
multilevel analysis and longitudinal data analysis. Furthermore, they
will be able to perform these techniques with standard software
packages. In addition, they will understand the principles of open
science and the current debate about null hypothesis significance
testing.
Specific goals are that:
• The student is able to explain and apply multilevel analysis for
cross-sectional data
• The student is able to explain and apply the basic principles of the
advanced techniques for longitudinal data.
• The student is able to explain the differences between different
methods and models of analysing clustered data and to motivate a choice
for one of these models in the context of epidemiological datasets/
research examples.
• The student can interpret results from the various methods and models
in the context of epidemiological datasets/ research examples
• The student is capable of performing the advanced techniques using
Stata
• The student can explain the principles of open science and is able to
relate these to quantitative data analyses
• The student can explain pitfalls of null hypothesis significance
testing (NHST) and can draw conclusions without reliance on NHST
The student is capable of performing the advanced techniques using
Stata
• The student can deliver an oral presentation following a scientific
format on a data-analysis assignment involving correlated data focusing
on the data-analyses, results and conclusion.
• The student can write the data-analysis, results and conclusion
section of a short scientific paper demonstrating he/she is able to
reflect on the results of the advanced analyzing techniques

Course Content

This course focuses on advanced methodology mostly focussing on analysis
techniques for correlated
data. These techniques will be introduced in lectures and subsequently
practiced by the students in the computer practicals using
Stata software. In the second part of the course, the students will
receive a dataset and will have to answer a research
question based on the data provided. The results of their analyses
are reported in a 'short' paper consisting of a statistical
analysis, results and discussion
section.

Teaching Methods

Lectures (9 times 3 hours)
Computer practical (8 times 3 hours)
Oral presentation (1 time 3 hours)
Preparing and writing a scientific paper
self study

Method of Assessment

Written exam (70%)
Oral presentation (0%; formative test)
Paper (30%)
The written exam and the paper must have been graded at least a 5.5.

Entry Requirements

Students must have knowledge of epidemiology and 'standard' linear,
logistic and Cox-regression analysis.

Literature

- Sheets of the lectures
- Twisk JWR. Applied longitudinal data analysis for epidemiology. A
practical guide. Cambridge University Press, Cambridge, UK, 2003.
- Twisk JWR. Applied multilevel analysis. A practical guide. Cambridge
University Press, Cambridge, UK, 2006
- specific literature, provided during the course

Target Audience

This course is aimed at students from the Msc Health Sciences. Students
from other Master programs can only enter the course when they can show
that they have enough back ground knowledge and skills in epidemiology
and statistics (see also under entry level).

General Information

Course Code AM_470826
Credits 6 EC
Period P2
Course Level 600
Language of Tuition English
Faculty Faculty of Science
Course Coordinator dr. M.R. de Boer
Examiner dr. M.R. de Boer
Teaching Staff dr. M.R. de Boer
prof. dr. J.W.R. Twisk

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Lecture, Computer lab
Target audiences

This course is also available as: