Explicit and Implicit Calibration of Covariance and Mean Structures

Linear structural equation models (SEMs) are widely used to assess the validity and reliability of survey variables. When population means or totals are of interest, it is also important to assess whether the observed variables contain an intercept bias. Unfortunately, standard identification procedures for SEMs define an arbitrary metric for the latent variables, which prevents the estimation of valid latent means and intercepts in a single population. In this paper, it is shown how an audit sample may be used to estimate a non-arbitrary set of identification restrictions.