When your dataset contains missing values, Amos does not report sample moments (i.e., sample means, covariances and correlations) or residual moments.

Sample moments are not reported because there is not a single agreed-upon way to compute them when the dataset contains missing values. Amos could calculate sample moments using listwise deletion or pairwise deletion, or after some form of data imputation. Each one of these methods of taking missing values into account would yield a different set of "sample moments". In fact, when missing values are present, Amos does not fit the model to sample moments at all. Instead, the model is fitted to the raw data. Sample moments are never computed.

Residual covariances are defined as the difference between the sample covariances and the model-implied covariances. Residual covariances cannot be calculated because sample covariances are not calculated. Similarly, residual means cannot be calculated because sample means are not calculated.