See Dick RAN: Rapid Naming and the Longitudinal Prediction of Reading Subskills in First and Second Graders

TLDR

RAN is a widely used task that reliably predicts reading achievement, with its power attributed to arbitrary print‑sound associations and overlapping components distinct from phoneme awareness. The study examined how RAN, verbal ability, and phonological awareness predict phonological and orthographic skills from first to second grade, using a computational model to clarify task relationships. A Seidenberg & McClelland word‑recognition model was employed to analyze the RAN task and its contribution to reading skill prediction. RAN and phoneme awareness independently accounted for later reading scores; RAN was a stronger predictor for orthographic‑dependent tasks, while phoneme awareness better predicted nonword reading and paragraph comprehension.

Abstract

The rapid automatic naming (RAN) task has been widely used in studies of reading acquisition and found to be reliably related to reading achievement. This study addressed the basis for this observed relation, providing new empirical evidence and an analysis of the RAN task within a computational model of word recognition in reading (Seidenberg & McClelland, 1989). The contributions of RAN, verbal ability, and phonological awareness to the prediction of phonological and orthographic skills from the 1st to the 2nd grade were examined. Both RAN (digits and letters) and phoneme awareness accounted for independent variance in later reading scores, even when vocabulary and prior reading skill were entered 1st in the regression analysis. RAN was a stronger predictor than phoneme awareness for 3 tasks in which orthographic information is critical (orthographic choice, word-likeness judgment, and exception word pronunciation), whereas the opposite held true for nonword reading and paragraph comprehension. The reading model suggests that the RAN task accounts for distinct variance in reading when compared to phoneme awareness because RAN involves arbitrary associations between print and sound (e.g., a digit and its name), whereas phoneme awareness is more related to the learning of systematic spelling-sound correspondences. In addition to the arbitrariness factor, RAN contains a number of other components that overlap with reading and, hence, collectively make it a good predictor of reading skills. In this article, we illustrate how explicit computational models of reading can clarify relations among tasks commonly used in reading research.