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Wed 21 Feb, Wed 28 Feb 2024
09:00 - 13:00

Provided by: Cambridge Research Methods


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Structural Equation Modelling

Wed 21 Feb, Wed 28 Feb 2024


This intensive course on structural equation modelling will provide an introduction to SEM using the statistical software Stata. The aim of the course is to introduce structural equation modelling as an analytical framework and to familiarize participants with the applications of the technique in the social sciences.

The application of the structural equation modelling framework to a variety of social science research questions will be illustrated through examples of published papers. The examples used are drawn from recent papers as well as from publications from the early days of the technique; some use path analysis using cross-national data, others confirmatory factor analysis, and other still full structural models, to test particular hypotheses. Some example papers may be found below, though they should not be treated as the gold standard, rather as an illustration of the variety of approaches and reporting techniques within SEM.

  • Duff, A., Boyle, E., Dunleavy, K., & Ferguson, J. (2004). The relationship between personality, approach to learning and academic performance. Personality and individual differences, 36(8), 1907-1920.
  • Garnier, M., & Hout, M. (1976). Inequality of educational opportunity in France and the United States. Social Science Research, 5(3), 225-246.
  • Helm, F., Müller-Kalthoff, H., Mukowski, R., & Möller, J. (2018). Teacher judgment accuracy regarding students' self-concepts: Affected by social and dimensional comparisons?. Learning and Instruction, 55, 1-12.
  • Parker, P. D., Jerrim, J., Schoon, I., & Marsh, H. W. (2016). A multination study of socioeconomic inequality in expectations for progression to higher education: The role of between-school tracking and ability stratification. American Educational Research Journal, 53(1), 6-32.

Students will engage in a critique of such examples, with the aim of gaining a better understanding of the SEM framework, as well as its application to real-life data. To further facilitate this application focus, the theoretical introduction will be accompanied by practical examples based on real, publicly-available data.

Target audience
  • Postgraduate students and staff
  • Further details regarding eligibility criteria are available here

This course is intended for students who in their research may want to engage with the testing of multiple hypotheses, or with complex relationships between variables.

  • No prior knowledge of SEM is assumed, and only a basic familiarity with Stata (or any other command-based statistical software) is needed.
  • Students should have an understanding of the principles of multivariate regression
  • Background reading (see below)



Number of sessions: 2

# Date Time Venue Trainer
1 Wed 21 Feb   09:00 - 13:00 09:00 - 13:00 SSRMP pre-recorded lecture(s) on Moodle   Sonia Ilie
2 Wed 28 Feb   09:00 - 13:00 09:00 - 13:00 Faculty of Education, 184 Hills Road, GS5 map Sonia Ilie
Topics covered
  • Introduction to the general principles of SEM;
  • Latent variables, measurement models, and confirmatory factor analysis;
  • Path analysis and mediation analysis, with practical application in Stata;
  • Confirmatory factor analysis and latent variable models.
  • Schumacker, R.E. & Lomax, R.G. (fourth edition, 2016, but other editions will also be fine) A beginner's guide to structural equation modelling
    • Chapter: 1 (Introduction);
    • Chapter 5 (Path Models);
    • Chapter 6 (Factor Analysis).

The text is a particularly accessible introduction to SEM. It contains examples in a variety of software packages, although not in Stata©.

Students should focus on understanding the concepts of the technique rather than software issues in preparation for the course. Chapters 1, 5, and 6 provide the core concepts of structural equation modeling, and are required reading for everyone enrolling on the course.

Students who are less confident about their background in quantitative data analysis may want to also read Chapters 2 (Data Entry and Edit Issues), 3 (Correlation) and 4 (Regression Models).


There may be an online open-book test at the end of the module; for most students, the test is not compulsory.

How to Book

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Moodle is the 'Virtual Learning Environment' (VLE) that the SSRMP uses to deliver online courses.

SSRMP lecturers use Moodle to make teaching resources available before, during, and/or after classes, and to make announcements and answer questions.

For this reason, it is vital that all SSRMP students enrol onto and explore their course Moodle pages once booking their SSRMP modules via the UTBS, and that they do so before their module begins. Moodle pages for modules should go live around a week before the module commences, but some may be made visible to students, earlier.

For more information, and links to specific Moodle module pages, please visit our website


8 hours


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