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Structural Equation Modeling

Questions that the analysis can address:

  1. What is the process by which our communications strategy influences customers to act?
  2. What variables need to be taken into account to understand why some of our customers purchased one of our new brands and others did not?
  3. Why did this programme produce good outcomes for some participants but not for others?

Structural Equation Modeling (SEM), also known as causal modeling, simultaneous equation modeling, path analysis, among others, is a set of statistical procedures used to examine relationships between a set of predictor variables and criterion variables. The variables can be either latent factors or directly measured variables.

At URIKA Research, we use SEM to test models of the relationships among variables and how these variables influence behaviors and outcomes. For example, we evaluate models of how advertising influences brand perceptions and sales; models of how communications programmes work to influence important behaviors (e.g., smoking, voting, and compliance with medical instructions) and models of how customers come to decide whether to continue to use a product or to switch to a competitor product.

A primary strength of SEM is that it helps us understand more about the causal chain of events, and therefore helps our clients see how their strategies and initiatives impact key outcomes.