Identifying Differences in Early Mathematical Skills Among Children in Head Start
- 325 Downloads
The purpose of this study was to examine early mathematical skill differences among preschool children in US Head Start classrooms. Latent class analysis based on six early mathematical subtest scores (i.e. counting aloud, measurement, counting objects, numbers and shapes, pattern recognition, and grouping) from a sample of 279 Head Start children yielded evidence for a high-achieving class, a typical-achieving class, and a low-achieving class, relative to other children attending Head Start. Average skill profiles of the three latent classes were in general parallel to one another, reflecting that most of the differences across latent classes were in level rather than type of skills. Changes in subtest scores over a 3-month interval indicated that the skill levels of the low-achieving class at time 2 were still below those of the typically achieving class at time 1. These findings provide evidence for skill variability among children enrolled in Head Start and a group of children who appear unlikely to demonstrate the skill level of their peers without additional instruction or intervention.
Keywordsearly childhood Head Start latent class analysis mathematical difficulties mathematical skills
Unable to display preview. Download preview PDF.
- Administration for Children and Families (2002). Head Start history. Retrieved December 16, 2005, from http://acf.hhs.gov/programs/hsb/about/history.htm.
- Denton, K. & West, J. (2002). Children’s reading and mathematics achievement in kindergarten and first grade. Washington, DC: National Center for Education Statistics.Google Scholar
- DiPerna, J. C., Morgan, P. L. & Lei, P.-W. (2007). Development of Early Arithmetic, Reading, and Learning Indicators for Head Start (The EARLI Project). Semi-Annual Performance Report to the U.S. Department of Health and Human Services Administration for Children and Families (Award No. 90YF0047/01).Google Scholar
- Lazarsfeld, P. & Henry, N. (1968). Latent structure analysis. New York: Houghton-Mifflin.Google Scholar
- Muthén, L. & Muthén, B. (2010). Mplus 6.12 [Computer software]. Los Angeles, CA: Muthén & Muthén.Google Scholar
- National Council of Mathematics Educators (2004). Principles and Standards for School Mathematics. Retrieved May 15, 2004, from http://www.nctm.org/standards.
- Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen: Danish Institute for Educational Research.Google Scholar
- Reardon, S. F. (2011). The widening academic achievement gap between rich and poor: New evidence and possible explanations. In G. J. Duncan & R. J. Murnane (Eds.), Whither opportunity? Rising inequality, schools, and children’s life chances (pp. 91–115). New York: Russell Sage.Google Scholar
- Stein, M., Silbert, J. & Carnine, D. (1997). Designing effective mathematics instruction: A direct instruction approach (3rd ed.). Upper Saddle River, NJ: Merrill Prentice Hall.Google Scholar
- U.S. Department of Health and Human Services, Administration for Children and Families (2005). Head Start Impact Study: First year findings. Washington, DC.Google Scholar
- Woodcock, R. W., McGrew, K. J. & Mather, N. (2001). Woodcock-Johnson III tests of cognitive abilities and achievement. Chicago: Riverside Publishing.Google Scholar