MAE4101 – Measurement models
Schedule, syllabus and examination date
In this course, you will get acquainted with the fundamental theories and applications of measurement models and their roles in structural equation models. The focus will be on using these methods for applied research.
You also gain practical competency in statistical software for analyzing data.
The course covers the following key topics:
- Overview of latent variable models and measurement error
- Path diagram, causality, and matrix notation
- Model fit and comparison
- Confirmatory and exploratory factor analysis
- Moderation and mediation
- Multigroup analysis and measurement invariance
- Recognize the general principles of measurement models
- Understand the key assumptions that underlie these models and methods
- Understand what violations of their assumptions can mean for model selection and associated inferences
- Select, apply, and interpret the parameters of a measurement model for the research question at hand, for instance, in the context of structural equation modeling
- Test key assumptions and offer possible solutions to violations
- Write up the results of an analysis in an appropriate way
- Analyze data with the help of existing statistical software packages
- Demonstrate a facility with measurement models to answer well-defined research questions, for instance, in the context of structural equation modeling
- Interpret published scientific research that uses these models and methods
- Evaluate the tenability of associated inferences and knowledge claims
Obligatory for students in the Assessment, Measurement and Evaluation master program. Students from other master’s programs at UiO may be considered if there is capacity. Questions about this can be sent to firstname.lastname@example.org
Ph.d. candidates can take the Ph.d. version of the course: UV9297
Recommended previous knowledge
5 credit overlap with MAE4110 Measurement Models
5 credit overlap with MAE4111 Structural Models
This course combines lectures and seminars with data analysis tasks in statistical software environments.
Obligatory course components:
- 80% attendance requirement
- Completion of two individual written assignments
The exam consists of a 4-hour individual written examination covering all course material and topics.
Please note: in the 2020 spring semester, the 4-hour written exam will be given as a home exam because of restrictions due to the corona virus.
You need to have successfully completed the obligatory course components before being allowed to sit the exam. If you do not fulfill these requirements, you must submit a written request to apply for an additional assignment prior to sitting the exam. The application must document stated reasons for absence beyond your control.
The written examination is conducted in the digital examination system Inspera. You will need to familiarize yourself with the digital examination arrangements in Inspera.
Examination support material
No examination support material is allowed.
Language of examination
The examination text is given in English, and you submit your response in English.
Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. Read more about the grading system.
Explanations and appeals
Resit an examination
Withdrawal from an examination
It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.
Special examination arrangements
Application form, deadline and requirements for special examination arrangements.
The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.