IHMJ2112 Mixture modelling (2 cr)

Study level:
Postgraduate studies
Grading scale:
Pass - fail
Responsible organisation:
Faculty of Education and Psychology
Curriculum periods:
2020-2021, 2021-2022, 2022-2023, 2023-2024


The sample could supposed to have data from sub population having differences in mean or covariance structures. The Gaussian mixture model offers tool to find those sub populations. The course present theory and general concepts related to this method. It further presents the stages in mixture modelling with latent profile analysis and growth mixture modelling, which are two often used model specified in mixture modelling. The course present short examples of different options to specify mixture models and extensions of mixture modelling in multigroup and multilevel context.

Learning outcomes

Targeted learning outcomes: Students understand the theory and general concepts related to Gaussian mixture modelling. They have knowledge how to build and interpret latent profile model and growth mixture model.

Additional information

Completion mode: Accepted assignments those cover the course content.

Study materials

Video lecture and text material with exercises.
Gregory R. Hancock & Karen M. Samuelsen. Advances in latent variable mixture models.
Aunola, K., Tolvanen, A., Kiuru, N., Kaila, S., Mullola, S., & Nurmi, J.-E. (2015). A Person-Oriented Approach to Diary Data: Children’s Temperamental Negative Emotionality Increases Susceptibility to Emotion Transmission in Father-Child Dyads. Journal for Person-Oriented Research, 1 (1-2), 72-86. doi:10.17505/jpor.2015.08 Open access.

Completion methods

Method 1

Evaluation criteria:
will be agreed upon in each study attainment
Select all marked parts
Parts of the completion methods

Independent study (2 cr)

Independent study
Grading scale:
Pass - fail
No published teaching