Develop Innovative Methods in Secondary Analyses of Child Welfare Databases
نویسنده
چکیده
-“Children’s Bureau Discretionary Grants Program” Grantee’s Final Report Shenyang Guo, Ph.D., Principal Investigator 1 April 24, 2006 I. Executive Summary This project proposes to develop and test four innovative approaches to analyses of child welfare survey databases, and to provide products that can be used by other child welfare scholars. It has four specific aims: 1) Demonstrate the capacity of hierarchical linear modeling (HLM) to improve the quality of information from multiple raters by: (a) simultaneously analyzing the ratings of multiple raters; (b) correcting for changes in raters that occur at multiple points in time; and (c) imputing missing ratings using “true scores”; 2) Apply a propensity score matching (PSM) approach to the evaluation of the impact of child welfare services on children’s safety, permanency, and well-being, in order to control for causal-effect heterogeneity and demonstrate the capacity of nonexperimental evaluation methods; 3) Apply marginal survival models to analyzing complex sampling data, and demonstrate the capacity of these models that correct for autocorrelation of event times within the primary sampling unit; And 4) demonstrate the use of structural equation modeling (SEM) with complex sampling data, and clarify the capacities and limitations of such analyses with existing software programs. It reviews recent advances in statistical models and tests how these advances can be applied to overcoming challenges encountered by child welfare researchers. Two databases were employed in the project to test and demonstrate the application of innovative statistical methods. The first is the
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