Interrelations of Growth in Letter Naming and Sound Fluency in Kindergarten and Implications for Subsequent Reading Fluency

Abstract

Although letter naming fluency (LNF) and letter sound fluency (LSF) measures are widely available to educators for assessing early literacy skills of kindergarten children, better understanding of the contributions of these skills to reading development can help improve the interpretation of LNF and LSF data for instructional decisions. This study investigated the interrelations of growth in LNF and LSF across the kindergarten year and their unique roles in predicting later reading fluency. Piecewise parallel-process growth models indicated that although LNF and LSF were highly correlated at kindergarten entry, fall LNF status was positively predictive of LSF growth across the fall. Bidirectional effects were present, as initial LSF was also a positive predictor of LNF growth across the fall; however, its effects were not as strong as those of initial LNF on LSF growth. More importantly, both initial status and growth in LNF and LSF were uniquely predictive of first-grade reading fluency, indicating the independent effects of each on subsequent text reading skills. Indirect effects were also observed for kindergarten LNF and LSF growth on reading fluency in second and third grades. Implications for kindergarten assessment and instruction are discussed.

Notes

1 The final sample of 532 was drawn from an initial data set that included 668 students present at the start of kindergarten. The average kindergarten LNF and LSF scores of the 136 students that withdrew from the school district prior to third grade, as indicated by t tests, did not statistically differ from average scores of students in the final sample.

Additional information

Notes on contributors

Nathan H. Clemens

Nathan H. Clemens, PhD, is an associate professor in the Department of Special Education at the University of Texas at Austin. His research is focused on reading skills development, with specific emphases in assessment and progress monitoring, improving interventions for students with reading difficulties, and data-based individualization.

Mark H. C. Lai

Mark H. C. Lai, PhD, is an assistant professor of quantitative methodology in the School of Education at the University of Cincinnati. His research focuses on the development and application of complex multilevel models in the social sciences and the integration of multilevel modeling with structural equation modeling, with topics of interest including measurement invariance, effect size estimation for multilevel data, and robust modeling methods.

Mack Burke

Jiun-Yu Wu, PhD, is an associate professor at National Chiao Tung University, Taiwan. He is a quantitative methodologist specializing in multilevel structural equation modeling with cross-sectional and longitudinal data. His research interests focus on students’ online reading behavior and performance as well as factors that motivate or hinder students’ selective attention during online learning.

Jiun-Yu Wu

Mack D. Burke, PhD, is an associate professor in the Department of Educational Psychology at Texas A&M University. His areas of research include emotional and behavioral disorders, integrated academic and behavioral interventions to address learning and behavior problems, and positive behavior support.