An algorithm for obtaining initial values for the minimization process in covariance structure analysis is developed that is more generally applicable for computing parameters connected to latent variables than the currently existing ones. The algorithm is formulated in terms of the RAM model but can be extended. (SLD)

It is suggested that using an unbiased estimate of the weight matrix may eliminate the small or intermediate sample size bias of the asymptotically distribution-free (ADF) test statistic. Results of simulations show that test statistics based on the biased estimator or the unbiased estimate are highly similar. (SLD)

A method is proposed for partially analyzing additive ipsative data (PAID). Transforming the PAID according to a developed equation preserves the density of the transformed data, and maximum likelihood estimation can be carried out as usual. Simulation results show that the original structural parameters can be accurately estimated from PAID. (SLD)