What are the assumptions of a structural equation model?
A structural equation model is a statistical technique used to evaluate the relationships between latent variables and their observed indicators. The assumptions of a structural equation model include linearity of the relationships between variables, normally distributed errors, uncorrelated error terms, and a sample size that is sufficient to estimate the model parameters accurately. Additionally, structural equation models assume that latent variables are independent and that their relationships with the observed variables are identified. These assumptions are crucial for ensuring that the structural equation model accurately represents the relationships between variables and produces meaningful results.
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