Surveys are widely used, e.g. to estimate drug and alcohol use. Increasingly, surveys are used for analytic purposes, to augment their traditional descriptive role. In the analytic role, survey data are used to investigate relationships between variables. Procedures for handling data from complex surveys should account for structure which may include stratification, clustering, or unequal probability sampling. In this SBIR project, we propose to create software for structural equation modeling that can be applied to complex surveys. In Phase I, we focus on factor analysis. We investigate a model based approach already successfully applied in regression problems, and investigate a variety of algorithms including EM and iterative simulation. In the process, we investigate practical issues including efficient algorithms, handling missing data, and bias that may arise from unequal selection probabilities. Our proposed work will provide a cost effective solution to correctly modeling complex survey data. The costs of current practices can be measured in both scientific and financial terms. The scientific costs are in misleading inferences; the financial costs are associated with the resources required to write one-off programs for each study or model. PROPOSED COMMERCIAL APPLICATION: Structural equation modeling software that handles both missing and complex survey data - implemented in a modern interactive statistical language and graphics system such as S-PLUS - will find a ready market, especially in the social sciences and biology. This research will also lead to short courses, books, videos and other educational material.