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Measurement error modelling through SEM: Applications in epidemiology and health

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If you have a question about this talk, please contact Mandy Carter.

Abstract Structural Equation Modelling (SEM) is a statistical framework for modelling with sets of simultaneous regression equations (path analysis) and latent variables (constructs that can only be defined indirectly, from indicators). Within SEM is it possible, and extraordinarily useful to understand and account for measurement error in “observed” variables, and to study the reliability and validity of target constructs that can only be measured as “latent” variables. SEM provides a framework for estimating scores on construct that otherwise cannot be measured, and in doing so corrects for error (through a model-based approach). SEM also deals with multiple response problems (multivariate outcomes and longitudinal data) and can be extended to account for missing and multilevel data and most recently to accomodate selection mechanisms that might be essential in dealing with confounding effects.SEM examples will be discussed.

This talk is part of the Graduate Programme in Cognitive and Brain Sciences series.

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