Identification of Simple Measurement Models with Multiple Latent Variables and Correlated Errors

Although computer programs may estimate values for unidentified parameters, a parameter must be identified in order for there to exist a unique point estimate of its value. We provide a series of rules that can be applied easily to measurement models of complexity one to demonstrate the identifiability of their parameters. These rules can be applied to models that contain one or more latent variables and that contain observed variables with correlated measurement errors. If the model is not identified, the rules pinpoint the parameters that are not identified and, thus, help researchers formulate a testable model.