Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

RATIONALE: Phonological awareness, letter knowledge, oral language (including sentence recall) and rapid automatised naming are acknowledged within-child predictors of literacy development. Separate research has identified family factors including socio-economic status, parents' level of education and family history. However, both approaches have left unexplained significant amounts of variance in literacy outcomes. This longitudinal study sought to improve prospective classification accuracy for young children at risk of literacy failure by adding two new family measures (parents' phonological awareness and parents' perceived self-efficacy), and then combining the within-child and family factors. METHOD: Pre-literacy skills were measured in 102 four year olds (46 girls and 56 boys) at the beginning of Preschool, and then at the beginning and end of Kindergarten, when rapid automatised naming was also measured. Family factors data were collected at the beginning of Preschool, and children's literacy outcomes were measured at the end of Year 1 (age 6-7 years). RESULTS: Children from high-risk backgrounds showed poorer literacy outcomes than low-risk students, though three family factors (school socio-economic status, parents' phonological awareness, and family history) typically accounted for less Year 1 variance than the within-child factors. Combining these family factors with the end of Kindergarten within-child factors provided the most accurate classification (i.e., sensitivity = .85; specificity = .90; overall correct = .88). IMPLICATIONS: Our approach would identify at-risk children for intervention before they began to fail. Moreover, it would be cost-effective because although few at-risk children would be missed, allocation of unnecessary educational resources would be minimised.

Original publication




Journal article


PLoS One

Publication Date





Child, Demography, Education, Family, Female, Humans, Male, Regression Analysis, Risk Factors, Schools