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EPI4923
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Advanced Statistical Analysis Techniques
Period 2: from 28-10-2024 to 20-12-2024
Co-requisites:
None
Coordinator:
Innocenti, F.
ECTS credits:
6
Language of instruction:
English
Publication dates timetable/results in the Student Portal
Deadline publication timetable
The date on which the timetable of this module is available:
not applicable
Deadline publication final result
The date on which the final grade of this module is available: 24-1-2025
Resit booking
Exam booking for a test in current academic year (resit)
You will be booked automatically for the resit in one of our resit periods. You may check our calenders to find out which modules can be retaken and when: https://intranet.maastrichtuniversity.nl/nl/fhml-studenten/studieverloop/wanneer-wat
As of one week before the resit test takes place, you can check in Student Portal if you are booked correctly: Student Portal > My Courses > More actions. The test will also be visible in your time table.
Exam booking for a test from a previous academic year (exam only)
All students who have not passed the test for this module in a previous academic year, will be booked automatically for the test during the regular block period. You will be enrolled in the new course in Canvas but not scheduled for a tutorial group and other educational activities.
If you do not wish to participate in this test at the end of the regular block period please de-register via askFHML.
Resit date: 7-3-2025
Though great care has been taken to assure the accuracy of the information on fhmlweb, the FHML cannot be held responsible for possible printing errors, incomplete information, or misinterpretations. Additionally, the FHML reserves the right to make changes to this information.
Course information
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Description:
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EN:
The primary objective of this course is to equip students with the skills needed to apply statistical techniques effectively in their professional work and beyond. Students will learn to use widely applied statistical methods responsibly and develop the ability to critically evaluate statistical aspects of research conducted by others. This course focuses on the practical application of advanced statistical techniques, emphasizing conceptual understanding and result interpretation, with mathematics being kept to a minimum. The course material is primarily based on SPSS. The use of R will only be briefly approached. Topics covered: - Analysis of variance and covariance
- Linear regression
- Logistic regression
- Survival analysis
- Analysis of repeated measures (i.e., linear multilevel models)
Each topic is covered through two lectures and two tutorials. The first lecture provides an overview of the theoretical foundations of the method, explaining its underlying assumptions and investigating the consequences of assumption violations. The second lecture takes a more applied approach, analyzing a real dataset to explore methodological choices and discuss how results can be interpreted and effectively summarized. The first tutorial explores theoretical concepts, while the second focuses on the interpretation of the results.
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Goals:
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EN:
After completing this unit, the participants should have acquired the knowledge and skills required for the independent use and critical assessment of various (multivariable) statistical analysis concepts, procedures and techniques which are prominent in epidemiological research: - Analysis of variance and covariance
- Linear regression
- Logistic regression
- Survival analysis
- Analysis of repeated measures (i.e., linear multilevel models)
For each of these statistical techniques, the participant should be able to deal with confounding, interaction and outliers, be aware of the assumptions underlying the use of the technique, know some advantages and disadvantages of the technique, interpret results and use dummy coding. The participant should also be able to choose an appropriate statistical analysis strategy, given a specific epidemiological research question and study design.
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Key words:
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EN:
Analysis of (co)variance, Linear regression, Logistic regression, Survival analysis, Analysis of repeated measures, Linear multilevel models
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Literature:
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This is the link to Keylinks, our online reference list.
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Teaching methods:
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- Assignment(s)
- Lecture(s)
- Problem Based Learning
- Training(s)
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Assessments methods:
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This page was last modified on:18-3-2025
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