Structural Equation Modelling
Key Facts
| Module Code |
POLI4123 |
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| Module Convenor |
Cees van der Eijk |
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| Teaching Pattern |
Intensive Block
Two full days of computer-room-based instruction which contain both lecture-like elements and hands-on practical training. |
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| Semester Taught |
Spring Semester (usually in February or March) |
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| Method of Assessment |
Written assignment |
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| Pre Requisites |
NURS4048 Fundamentals of Quantitative Analysis, or equivalent to be approved by the convenor. |
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| Module Administrator |
Rosemary McCabe |
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Teaching on this module uses STATA software.
Structural Equation Modelling (SEM) is a form of multivariate analysis, just like, e.g., regression analysis is. The main difference is that in a regression model variables are either independent, or dependent. In a SEM model variables can also be mediating: they are dependent with respect to some other variables, yet independent for yet other ones. Obviously, this comes much closer than a regression model to represent many of our substantive theories that propose networks of causal relationships between variables, and chains of causation. The module covers the methodological background of SEM, practical considerations in actual applications, empirical examples from different disciplines, and hands-on training (using AMOS).
The measurement elements of structural equation modelling (i.e., confirmatory factor analysis or CFA) will be discussed, but much of this is addressed in more detail in another Advanced Training module (POLI4122 Measurement Models). Although there is no requirement to do so, there is therefore a distinct benefit in taking both modules.
Please note, this is an interdisciplinary module that is open to students from across the social sciences. If you have any doubts about whether the course is suitable for your needs or level of study, please contact the module convenor before registering.