This workshop introduces recent and emerging developments in the strategies and techniques for the analysis of large-scale databases, including data mining and predictive analytics that can be used with national census and epidemiological studies. It focuses on the logic of key design decisions, such as selection of unit of analysis, and techniques for the transformation of data, for example, creation of composite variables and the assessment of their reliability. Examples are from peer-reviewed published studies of the presenter’s over the last 25+ years, involving homelessness and psychiatric care, using SPSS for data preparation, supplemented by Excel, LISREL, and Maptitude. The following outlines the workshop: (1) Secondary Analysis as a Research Strategy: Types of data; advantages and disadvantages; costs, PC equipment, and feasible database sizes; the importance of theory; and typical steps in data analysis. (2) Selected Methods and Techniques: (i) Determining unit of analysis: This section will begin with discussion of the potentials for modeling variation in phenomena of interest between multiple jurisdictions. It considers the pros and cons of three strategies: data aggregation, spreading, and multi-level modeling, or some combination. (ii) Question of weighting by population in census studies of multiple jurisdictions: When, why, and how would this be done? (iii) The element of time: In databases organized on the basis of service episodes, use of lag variables for computing LOS, time between episodes and recidivism rates. How is this done? (iv) Computation of composite variables; (v) Assessment of reliability and validity. Key examples include: Assessing diagnostic reliability using Kappa; correlation of multiple indicators and measures. (3) Conclusion: Comments on major analytic procedures, e.g.: GIS and the mapping of bivariate correlations; the assessment of the overall fit in Cox proportional hazard models; and key considerations in use of SEM. (4) Format: Didactic, with discussion, Powerpoint, and SPSS examples.
