This dissertation explores the effects of proximal and distal influences on children’s early cognitive, literacy, mathematics, and social-emotional development. Specifically, it explores how child and family characteristics, collected across two systems (education and social services), provide a deeper understanding about which factors (administrative Head Start data) contribute to, or impede, kindergarten readiness. The sample comes from over five-years of administrative data collected by one Head Start (N = 1,094, M(age) = 5.2 years; 54% female). Half of the children in the sample are African American (50%), a majority are English speaking (84%), and 100% are considered low-income. The dissertation utilized multiple regression and latent class analyses to answer the following research questions: (1) Which child and family characteristics are associated with kindergarten readiness (cognitive, literacy, mathematics, and social-emotional development)? (2) What number and type of classes (based on Head Start administrative data) best describe the children in the sample? (3) To what extent does membership in these classes predict kindergarten readiness across cognitive, literacy, mathematics, and social-emotional development? Through the application of multiple regression analysis, findings suggest that if a child were homeless at intake, they were more likely to score lower than non-homeless children across literacy, mathematics and social-emotional development. Additionally, father involvement, enrollment in Medicaid, and passing the hearing screening at intake predicted higher scores on the cognitive assessment. Furthermore, results from the latent class analysis identified that children were best categorized into two classes: Class One. Family Risk and High Social Service Enrollment (49%) and Class Two. Family Strength and Low Social Service Enrollment (51%). Children in Class One were more likely to score lower across all developmental domains at kindergarten entry, except on the social-emotional kindergarten assessment. The findings from this study offer an important contribution to understanding the use of Head Start administrative data as one mechanism for identifying early risk and intervention opportunities across multiple ecological levels, prior to kindergarten.