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Short Course
Structural Equation Models (SEM)
The reason why the NWU wishes to offer the course is to give individuals the necessary tools to understand, analyse and interpret output obtained from Structural Equation Models. Structural Equation Modelling is a recent statistical development and is a powerful technique to identify underlying constructs and relationships between latent variables. Statistics plays an integral role in quantitative research, and researchers that develop skills related to more advanced statistical techniques will improve the quality of their research outputs.

Purpose of the course:

This short course aims to equip participants with the skills necessary to understand, analyse and interpret the statistical output obtained from software that implements Structural Equation Models. The participant will gain knowledge on how to approach the process of fitting a Structural Equation Model and interpret goodness-of-fit measures.

Admission requirements:

Admission requirements: 
Must have done an introductory Statistics short course or a Statistics module at a first year level.
Learning assumed to be in place: 
National Senior Certificate and at least an NQF level 5 qualification.

Course outcomes and assessment criteria :

Course outcomes and the associated assessment criteria: 

Study Unit


Assessment Criteria


After completion of this course, participants will:

  1. Demonstrate detailed knowledge of Structural Equation Models (SEM) and its special cases: path analysis, confirmatory factor analysis and structural regression models.
  2. Demonstrate an ability to select, apply, and interpret these models and results with the use of computer programs on various sets of data.
  3. Demonstrate an ability to report results in a reliable, accurate, and an ethical manner.

Students have mastered the outcomes if they are able to do the following:

  1. Discuss Structural Equation Modelling as a general statistical technique.
  2. Explain the different special cases of Structural Equation Modelling (path analysis, confirmatory factor analysis and structural regression models).
  3. Identify the most suitable technique for a given quantitative research problem.
  4. Design and construct path diagrams using special graphical notation to present models visually.
  5. Identify and apply latent variables on a multivariate dataset by using confirmatory factor analysis (with the use of appropriate computer software).
  6. Interpret goodness-of-fit measures to evaluate the reliability of the model and latent variables.

Interpret the output from the various statistical techniques and be able to report these results in a statistically sound and an ethical manner.


Participation in short course activities.
Method of assessment: 
100% attendance and informal formative assessment.

Additional information

Mode of delivery: 
Target group: 
Postgraduate students busy with research, researchers or any person who would like to know how to use and interpret Structural Equation Models.
Contact us
Prof JS Allison
Telephone number: 
018 299 2571