Schedule, syllabus and examination date

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Course content

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:

  1. Overview of latent variable models and measurement error
  2. Path diagram, causality, and matrix notation
  3. Model fit and comparison
  4. Confirmatory and exploratory factor analysis
  5. Moderation and mediation
  6. Multigroup analysis and measurement invariance

Learning outcome


  • 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. Contact us at for registration.

Ph.d. candidates can take the Ph.d. version of the course: UV9297


Recommended previous knowledge

Recommended: having completed MAE4000 Data Science or equivalent. If you are unsure of whether your prior knowledge is sufficient, please contact CEMO.

Overlapping courses

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.

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.

Previous exams

Digital examination

The written examination is conducted in the digital examination system Inspera. You will need to familiarize yourself with the digital examination arrangements in Inspera.

Read more about written examinations using 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.

Grading scale

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.

Facts about this course




Every spring


Every spring

Teaching language