Date of Award

Spring 5-12-2017

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Early Childhood Education

First Advisor

Gary E. Bingham

Second Advisor

Mona Matthews

Third Advisor

Nicole Patton Terry

Fourth Advisor

Cynthia Puranik

Fifth Advisor

Chenyi Zhang

Abstract

Whereas interest in young children’s writing has increased in recent years, the focus has centered more around transcription skills (i.e., letter formation and spelling) and less upon children’s early composing. Of the limited research that does exist on early composing, there is little shared understanding around the conceptualization of the construct or how it should be measured. Research to date has utilized varied and sometimes conflicting scoring and assessment techniques to assess early composing skills in young children. In order to more fully conceptualize this construct, the first study synthesizes extant literature focused upon early writing and composing to understand the nature and measurement of emergent composing. The second study aims to examine composing by exploring 160 prekindergarten children’s performance on a testing battery including a number of pre-existing and alternative writing assessments in order to explore the nature, measurement, and variability of early composing skills. In addition, this study will examine language, literacy, and executive function skills as they relate to children’s performance on early composition tasks. Partial correlations, controlling for children’s age, will be used to determine the degree of relation among children’s composing scores. Partial correlations will also be used to examine the associations of composing scores and children’s language, literacy, and cognitive skills. Multiple linear regressions will be utilized to determine the nature and degree of relation among children’s language, literacy, and executive function skills and early composing. Research of this nature has implications for a more comprehensive understanding of early composing and will lead to a deeper understanding of how to more effectively measure this construct.

DOI

https://doi.org/10.57709/10112819

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