freely estimated, so long as the model is statistically identified (e.g., the number of freely estimated parameters does not exceed the number of nonredundant elements in the genetic covariance matrix being modeled). A set of parameters (θ) is estimated such that the fit function indexing the discrepancy between the model-implied covariance matrix, ∑(θ), and the empirical covariance matrix, S, estimated in Stage 1 is minimized. Model fit is considered good when ∑(θ) closely approximates S. In the main text of the article, we highlight results from weighted least squares (WLS) estimation that weights the discrepancy function using the inverse of the diagonal elements of the sampling covariance matrix, and produces model SEs using the full sampling covariance matrix. In the Supplementary Results, we additionally report results from an alternative normal theory maximum likelihood (ML) estimation method.
