Evaluating academic outcomes of Head Start: an application of general growth mixture modeling

This study intends to illustrate the utility of general growth mixture modeling (GGMM) for evaluation of early childhood education programs, using a sample of children with Head Start experience. In the first analysis of this study growth mixture modeling (GMM) found that children with Head Start experience had two distinct growth patterns. In the second analysis of this study general growth mixture modeling found that children with two or more years of program participation did not have faster achievement growth, on average, than children with only one year of program participation. This study also found that a gender gap in mathematics and an income gap in reading and mathematics were exclusively exhibited by the children with no preschool experience. Therefore, it was concluded that the Head Start program may be reducing both a gender gap in mathematics and an income gap in reading and mathematics. (C) 2003 Elsevier Science Inc. All rights reserved.