A general structural equation model is fitted on a panel data set that consists of $I$ correlated samples. The correlated samples could be data from correlated populations or correlated observations from occasions of panel data. We consider cases in which the full pseudo-normal likelihood cannot be used, for example, in highly unbalanced data where the participating individuals do not appear in consecutive years. The model is estimated by a partial likelihood that would be the full and correct likelihood for independent and normal samples. It is proved that the asymptotic standard errors (a.s.e.'s) for the most important parameters and an overall-fit measure are the same as the corresponding ones derived under the standard assumptions of normality and independence for all the observations. These results are very important since they allow us to apply classical statistical methods for inference, which use only first- and second-order moments, to correlated and nonnormal data. Via a simulation study we show that the a.s.e.'s based on the first two moments have negligible bias and provide less variability than the a.s.e.'s computed by an alternative robust estimator that utilizes up to fourth moments. Our methodology and results are applied to real panel data, and it is shown that the correlated samples cannot be formulated and analyzed as independent samples. We also provide robust a.s.e.'s for the remaining parameters. Additionally, we show in the simulation that the efficiency loss for not considering the correlation over the samples is small and negligible in the cases with random and fixed variables.