Structural Equation Models

7.5 credits

Course, Master's level, 2ST114

Expand the information below to show details on how to apply and entry requirements.

Location
Uppsala
Pace of study
50%
Teaching form
On-campus
Instructional time
Daytime
Study period
24 March 2026–7 June 2026
Language of instruction
English
Entry requirements

120 credits including 90 credits in statistics, or 120 credits including 60 credits in statistics and 30 credits in mathematics and/or computer science.

Selection

Higher education credits (maximum 285 credits)

Fees
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
  • First tuition fee instalment: SEK 12,500
  • Total tuition fee: SEK 12,500

Read more about fees.

Application deadline
15 October 2025
Application code
UU-76631

Admitted or on the waiting list?

Registration period
18 December 2025–18 January 2026
Information on registration from the department

Expand the information below to show details on how to apply and entry requirements.

Location
Uppsala
Pace of study
50%
Teaching form
On-campus
Instructional time
Daytime
Study period
25 March 2027–6 June 2027
Language of instruction
English
Entry requirements

120 credits including 90 credits in statistics, or 120 credits including 60 credits in statistics and 30 credits in mathematics and/or computer science.

Selection

Higher education credits (maximum 285 credits)

Fees
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
  • First tuition fee instalment: SEK 15,750
  • Total tuition fee: SEK 15,750

Read more about fees.

Application deadline
15 October 2026
Application code
UU-76631

Admitted or on the waiting list?

Registration period
25 February 2027–18 March 2027
Information on registration from the department

About the course

The course introduces the core methods of structural equation modelling (SEM), a family of statistical approaches that explore complex relationships between and among latent (unobserved) and observed variables. In addition, the course emphasises the empirical applications of SEM and latent variable techniques to broadly address relevant questions in the different disciplines. Course lectures, readings, and assignments will reflect this applied focus. At the end of the course, you are expected to understand the essential issues within SEM and be able to carry out analysis independently.

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