Abstract Analysis Core (AnC) The Analysis Core will provide needed infrastructure for the training, research, and evaluation goals of the CHECA project, especially the pilot research projects for the early-career investigators. The Analysis Core members have a strong background in research design, sample size planning, exploratory and confirmatory data analysis, and technical and academic writing. These tasks are expected to be done in dialogue and collaboration with the researchers on the other cores, thus providing additional guidance and mentorship to assist the early-career researchers in developing their awareness and skill in these areas. The Analysis Core will also develop and disseminate training materials as needed. An important Analysis Core mission will be assisting with the rigorous collection and management of evaluation research data, and their analysis according to accepted statistical and methodological good practice, and their adaptation where appropriate for sharing without violation of confidentiality. This will be especially relevant in the development of behavioral intervention development training plans, which must combine substantive knowledge of the research questions with sound statistical principles to draw appropriate conclusions. Several themes are expected to be important in CHECA research, such as observational studies (including questionnaires and longitudinal surveys) for identifying individual and community strengths and challenges, and the evaluation of proposed interventions. Accordingly, a broad range of expertise is available for dealing with these disparate design and analysis challenges. Dr. Michael L. Berbaum, the head of the analysis core, has expertise in statistical analysis, writing and administration and coordination of research as well as in the mentoring of early-career researchers. He has extensive experience in research on racial and ethnic disparities on multiple health-related variables relevant to cognition and life functioning. Dr. Tianxiu Wang is an experienced biostatistician and has collaborated extensively on work related to cognitive decline and dementia in older adults. Dr. John J. Dziak has been a lead or co-author on multiple conceptual or applied papers on topics related both longitudinal research, causal inference, latent variable modeling, and intervention development and evaluation, including in clustered or multilevel contexts. He has written tutorials on the practical interpretation of statistical results for substantive researchers. Both Dr. Wang and Dr. Dziak are proficient in longitudinal data analysis, which is important for research based on a life course perspective. .