A key issue for clinical screening practice and for this study is whether it is possible to determine the critical ages and measures for identification of an individual child's risk for reading disability (RD). Findings of meta-analyses (Scarborough, 1998, 2001) and recent familial dyslexia follow-up studies (Carroll & Snowling, 2004; De Jong & Van der Leij, 2003; Elbro, Borstrom, & Petersen, 1998; Gallagher, Frith, & Snowling, 2000; Pennington & Lefly, 2001; Snowling, Gallagher, & Frith, 2003) as well as longitudinal data from the Jyväskylä Longitudinal Study of Dyslexia (Lyytinen et al., 2001, 2004, 2006) show that the best predictors of preschoolers’ and kindergarteners’ later reading achievement are the measures which require the processing of print followed by oral language proficiency measures as well as performance-IQ and familial history of dyslexia. The present study concerns prediction of 2nd grade reading disability (RD) and non-RD outcomes in children with and without familial risk for dyslexia. A battery of key dyslexia predictors (i.e., phonological awareness, short-term memory, rapid naming, expressive vocabulary, pseudoword repetition, letter naming) at three age points (3.5, 4.5 and 5.5 years) was used.

Earlier studies have suggested that individual screening has usually not been as successful when preschool-age predictors rather than measures derived from an age closer to school entry have been employed. Some researchers have combined several kindergarten variables with the guidance of statistical procedures and thus improved predictability impressively, e.g., recently Pennington and Lefly (2001) used discriminant function analysis and Elbro et al. (1998) employed a logistic regression analysis procedure. In the recent past, only one reading study (Catts, Fey, Zhang, & Tomblin, 2001) has tried to implement the findings from logistic regression modelling into clinical screening practice. Catts et al. found that the performance of the children (with early language problems) at the age of 5 years in letter knowledge, sentence imitation, phoneme/syllable deletion and rapid naming, together with mother's education, made a significant contribution to predicting the risk of reading comprehension difficulties in the 2nd grade. They also presented practical suggestions on the interpretation of results and on implementing the logistic equation models by calculating the exact individual probability scores for the purpose of clinical use.

The reports in the literature typically focus on the group-level differences in the predictors using either risk/control groups or RD/nRD samples. It is often the case that although a significant difference in group means emerges on a measure, it does not necessarily discriminate and predict skills at the individual level. The goodness of the predictor is determined by its ability to ‘catch’ the true positive cases (TP; i.e., those who are predicted to show RD and who turn out to be RD at school age) and to ‘avoid’ the false positive cases (FP; i.e., those who are predicted to show RD but who do not at school age). These two accounts are inversely related and by changing the cutoff point of the predictor the rates of both TP and FP shift. This is utilised in the *ROC (receiver operating characteristic)* analyses. The ROC curve is a plot of the TP-rate (sensitivity) against the FP-rate (1–specificity) for different cutoff points of a predictor. The method is often used in medical research to explore a measure's ability to discern individuals who have a disorder from those who do not have it (Greiner, Pfeiffer, & Smith, 2000; Grunkemeier & Jin, 2001; Obuchowski, 2003). The ROC scores (area under ROC) can be interpreted to express the measure's overall *prediction probability* of a disorder.

In the present study, a large battery of the key behavioural level dyslexia predictors was assessed during three successive years starting at the age of 3.5 years. The measures of phonological awareness, short-term memory, rapid naming of objects, expressive vocabulary, pseudoword repetition and letter naming as well as performance IQ and the familial risk of dyslexia were examined in the prediction of a specific reading disability.

The challenging goal of the present study is to be able to present a clinically usable and parsimonious procedure for evaluating an individual child's risk for RD. We first employed the logistic regression modelling approach to explore what combinations of measures are the most sensitive and specific in predicting an individual child's risk for RD at the different age phases and across ages, and then utilised the ROC analyses in the estimation of the achieved prediction probabilities from the age of 3.5 to 5.5 years. Information from the logistic models was further utilised in building the probability curve presentations which offer a powerful way to illustrate an individual child's risk of RD. Some suggestions and guidelines for screening are also offered.