A general latent variable model is given which includes the specification of a missing data mechanism. This framework allows for an elucidating discussion of existing general multivariate theory bearing on maximum likelihood estimation with missing data. Here, missing completely at random is not a prerequisite for unbiased estimation in large samples, as when using the traditional listwise or pairwise present data approaches. The theory is connected with old and new results in the area of selection and factorial invariance. It is pointed out that in many applications, maximum likelihood estimation with missing data may be carried out by existing structural equation modeling software, such as LISREL and LISCOMP. Several sets of artifical data are generated within the general model framework. The proposed estimator is compared to the two traditional ones and found superior.

Recent advances in design, measurement, and analysis can have only a marginal impact on the integrity of evaluation studies because the evaluation of social programs is fundamentally dependent on the quality of the collected data. The effects of data collection procedures and their consequences on the integrity of evaluation conclusions are explicated. Data collection faults occurring in evaluation studies are enumerated and illustrated. A research agenda is proposed for improving data collection in social program evaluations.