The central goal of the Data Management and Analysis Core (DMAC) is to develop and implement database management and analysis policies and procedures to support research in Alzheimer's disease or related dementias (ADRD) As a result, a major focus of DMAC activities continues to be the development and maintenance of efficient and reliable data entry procedures. In addition, with the development of a large and complex longitudinal database, the provision of sophisticated data analysis services is now an equally important function of DMAC. Finally, the construction and dissemination of both general and specifically tailored data sets to investigators in the Cleveland area will be an enhanced responsibility of DMAC. The appointment of a new Director of DMAC in June 1998, and more recently a new Director of the Center, has afforded the Center the opportunity to make a fresh assessment of the Core's procedures and policies for the accomplishment of its goals. Consequently, a number of important new directives are now being implemented that build upon and enhance its prior capacities. New methods of data entry include direct data-entry via hand help Personal Data Assistants and the use of Teleform software to scan self- administered questionnaires directly into the computer. These methods will streamline the data entry process, cut costs, and reduce personnel turnover when fully implemented. New database entry and management software (SPSS) has been adopted in order to increase the efficiency and expertise of the DMAC personnel. Consequently, there will be a seamless integration of the data entry, data transformation, data retrieval, and data analysis functions of the Core. New policies for data dissemination to enhance the use of the Center database include the construction and thorough documentation of a comprehensive master SPSS data file. New procedures for advertising the data and expediting its dissemination will be enacted, as well as for receiving data analytic support from the Core. In addition, DMAC is now providing statistical consultation and assistance with several more advanced procedures in order in order to more fully exploit its rich longitudinal database, including growth curve analysis, survival analysis, and structural equation modeling with controls for random and systematic measurement errors.
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