Home/Vakken/Advanced Statistics II: Introduction in multilevel and structural equation modelling for EdSci
2006000277.5 ECTSEnglishMaster
Advanced Statistics II: Introduction in multilevel and structural equation modelling for EdSci
Faculteit—
NiveauMaster
Studiejaar2026-2027
Beschrijving
Course goals
TESTING AND COURSE AIMS:
In this course there are two tests and two assignments (consisting of multiple exercises spread over multiple weeks):
• Assignment 1 (10%):
a) Students develop knowledge and understanding of multilevel modeling (Knowledge and understanding)
b) Students develop knowledge and understanding of specialized software for multilevel modeling (Knowledge and understanding)
b) Students develop knowledge and understanding of specialized software for multilevel modeling (Knowledge and understanding)
c) Students learn to translate research questions into (aspects of) multilevel models, and specify relevant multilevel models in specialized software (Knowledge and understanding)
d) Students can translate research questions into sequence of models to be run (Learning skills)
e) Students can use specialized software for multilevel modeling (Learning skills)
f) Students can interpret software output in terms of relevance and statistical significance (Applying knowledge and understanding)
g) Students can relate software output to the research question (Applying knowledge and understanding)
• Test 1 (40%):
a) Students develop knowledge and understanding of multilevel modeling (Knowledge and understanding)
b) Students develop knowledge and understanding of specialized software for multilevel modeling (Knowledge and understanding)
b) Students develop knowledge and understanding of specialized software for multilevel modeling (Knowledge and understanding)
c) Students learn to translate research questions into (aspects of) multilevel models, and specify relevant multilevel models in specialized software (Knowledge and understanding)
d) Students can translate research questions into sequence of models to be run (Learning skills)
e) Students can use specialized software for multilevel modeling (Learning skills)
f) Students can interpret software output in terms of relevance and statistical significance (Applying knowledge and understanding)
g) Students can relate software output to the research question (Applying knowledge and understanding)
• Assignment 2 (10%):
a) Students develop knowledge and understanding of structural equation modeling (Knowledge and understanding)
b) Students develop knowledge and understanding of specialized software for structural equation modeling (Knowledge and understanding)
c) Students learn to translate research questions into (aspects of) structural equation models, and specify relevant structural equation models in specialized software (Knowledge and understanding)
d) Students can translate research questions into sequence of models to be run (Learning skills)
e) Students can use specialized software (Learning skills)
f) Students can interpret software output in terms of relevance and statistical significance (Applying knowledge and understanding)
g) Students can interpret software output in terms of model fit (Applying knowledge and understanding)
g) Students can interpret software output in terms of model fit (Applying knowledge and understanding)
h) Students can relate software output to the research question (Applying knowledge and understanding)
• Test 2 (40%):
a) Students develop knowledge and understanding of structural equation modeling (Knowledge and understanding)
b) Students develop knowledge and understanding of specialized software for structural equation modeling (Knowledge and understanding)
c) Students learn to translate research questions into (aspects of) structural equation models, and specify relevant structural equation models in specialized software (Knowledge and understanding)
d) Students can translate research questions into sequence of models to be run (Learning skills)
e) Students can use specialized software (Learning skills)
f) Students can interpret software output in terms of relevance and statistical significance (Applying knowledge and understanding)
g) Students can interpret software output in terms of model fit (Applying knowledge and understanding)
g) Students can interpret software output in terms of model fit (Applying knowledge and understanding)
h) Students can relate software output to the research question (Applying knowledge and understanding)
Content
Two techniques that are often encountered are multilevel modeling (MLM) and structural equation modeling (SEM). MLM is appropriate for handling nested data, for instance, patients in hospitals, or occasions in people. MLM can be used to study the within cluster and the between cluster relationships between an outcome variable and predictors. In the lab meetings specialized software for multilevel modeling will be used. SEM covers both factor analyses and path analyses. It can be used to investigate the underlying factor structure and compare this across groups (i.e., measurement invariance), more complex mediation models, longitudinal data, and to compare distinct theories. In the lab meetings specialized software for SEM is used.
There are options to follow this course as a non-EdSCi student (eg Elective student, PhD student etc). Please contact the teacher about these options on time, ie before July 15 (for courses in semester 1) / December 15 (for courses in semester 2). You will need written approval from the teacher (an email is sufficient) in order to register for this course at the Faculty’s Student Information Point (Faculty Student Desk). Note that for external parties, costs for participation may be involved.
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