Early reading acquisition and its relation to reading experience and ability 10 years later.
Print exposure predicted reading comprehension growth. Early Reading Acquisition and Its Relation to Reading Experience and Ability 10 Years Later. Dev Psychol. Nov;33(6) Early reading acquisition and its relation to reading experience and ability 10 years later. Cunningham AE(1), Stanovich. Cunningham, A. E., & Stanovich, K. E. (). Early Reading Acquisition and Its Relation to Reading Experience and Ability 10 Years Later. Developmental.
The internal and external validity of these profiles was very favorable, suggesting homogeneous within- cluster membership and clear separation between the six core profiles. In addition, the six cluster solution was supported through cross-validation. These profiles can be described in terms of patterns of strengths and weaknesses as well as demographic prevalence rates.
Building an Integrated Model of Early Reading Acquisition
Three of the profiles i. These flat profiles were unremarkable in terms of strengths or weaknesses and appeared to be predominately defined by varying degrees of general reading ability. The remaining three profiles i. This procedure allowed us to capture the heterogeneous nature of children's reading abilities and identify homogeneous groups of children based on patterns of strengths and weaknesses.
The educational utility of these groupings comes by way of identifying profiles that are predictive of successful readers. In other words, what strength s are most important to reading acquisition? As we expand and deepen this work and gather more longitudinal data, our results will have the potential to assist teachers as they address the instructional needs of children with similar patterns of reading ability.
The second and third questions were addressed by comparing children with different profiles on four measures of basic reading skills and reading comprehension. Results of these analyses suggested that certain profiles were associated with greater success on external measures of reading achievement than others, and that the importance of certain reading abilities varied in accordance with the achievement being considered.
Children with strengths in all areas of crystallized ability, auditory processing, processing speed, and short-term memory i. On the other hand, children with weaknesses in all four reading ability constructs i. When the focus turns to children composing profiles defined primarily by general ability i.
Children in profile 1 i. These initial results are suggestive of a compensatory hypothesis, one that acknowledges that there is more than one route to successful, or at least to adequate, reading performance.
But each route carries with it some costs and benefits. Teasing out the most important abilities among children with average reading abilities depends, in part, on the reading outcome being considered.
Children with strengths in either auditory processing profile 5processing speed profile 6or crystallized ability and short-term memory profile 3 appear to do equally well on some measures of basic reading skills e. These findings suggest that children with average reading ability and one or more secondary strengths perform better than children of approximate average ability without such secondary strengths profile 1. The particular secondary strength, however, does not seem to be of much importance on these reading outcomes.
On the other hand, when different measures of basic reading skills and reading comprehension are considered, the particular secondary strength does appear to influence reading achievement. Namely, elevated auditory processing scores produce better performance on Word Attack, whereas elevated processing speed scores do not influence reading vocabulary as much as elevations on either crystallized ability and short-term memory or auditory processing.
Future Directions The results described above provide much needed information regarding the multivariate nature of children's reading ability patterns and their influence on various reading outcome measures. The findings provide insight into the difficulties children are likely to face in learning to read when they do not possess the necessary foundation.
Several questions remain unresolved and will be addressed over the next two years. The first concerns the stability of children's profile placement over time. That is, are children able to migrate to neighboring profiles and thereby increase their chances of becoming successful readers? What types of instruction might be responsible for such movement? Second, it is important to determine which profiles are likely to continue to be associated with reading success over time. In other words, are certain profiles more important to success at a given age?
To address these concerns, profiles identified in Phase I will serve as benchmarks against which children in our longitudinal sample will be linked, in a normative sense Phase II. To illustrate what we will be able to do in Phase II, we have conducted a sample analysis with some data from year one. The preliminary framework for Phase II is described below. Our specific interest concerns children that appear most at risk of failing to read.
As a result, the sample described below is composed primarily of lower achieving children. This part of the analysis involved linking a new sample of children to the profiles identified in Phase I.
It established a baseline for this sample so that profile changes and future success in external contexts of reading acquisition could be assessed over the remaining four years of the study. Children's profiles from our first year of data collection were matched to the normative profiles identified in Phase I. Data from year two has also been collected, and we are currently in the process of scoring their responses. As a result, the information provided below pertains only to our first year of data collection.
This procedure also allows for the identification of profiles that do not closely fit the core types. This latter procedure is prevalence-based so that any desired number of students with poor matches can be identified.
Thus, rare profiles i. Alternatively, we could link only those children that match reasonably well e. To date, children composing our longitudinal sample year one were matched to the core profiles.
Both procedures described above were followed here for future consideration. The first procedure matched each child to one of the core types by assuming all children could be reasonably matched to one of the core profiles identified in Phase I i. The first procedure will allow us to determine whether certain profiles are associated with success or failure.
The latter procedure is based on the hypothesis that there may be a small segment of the population that have more unique profiles than those identified in the normative sample Phase I. Identifying this group will allow us to determine whether children with these unique profiles are any more or less likely to perform differently on various reading outcomes.
One hundred fifty parent permission forms were sent to parents of children from six feeder preschools that indicated they would allow us to observe and test their children. One hundred six parents agreed to allow their child to participate. The sample contained both African-Americans Eighty-four percent of the sample qualified for free lunch, 2. All children were evaluated with the WRDB Woodcock, by one of four graduate students who had received training in the administration of this instrument.
One hundred ninety-two children were also evaluated in year two of our study Many of these children were previously tested in year one, as described above. As previously indicated, we are currently in the process of scoring and analyzing these data. Thus, the results described below pertain only to children in the first wave of data collection.
Prior to matching students from this sample to the normative profiles identified in Phase I, the accuracy of the D2 classifications was first investigated with the standardization sample. Children with D2 values of 1, or greater yielded the poorest match with any of the six normative clusters.
When all children were assigned to one of the existing core profiles identified in Phase I, the majority of the children were matched with cluster 2 The remaining children were matched with clusters 1 Of these children, most were again assigned to cluster 2 These are the types of analyses we will be able to do once we have more definitive longitudinal data.
These children's profiles will be evaluated in subsequent years to assess their stability and identify which profiles are most likely to be associated with reading problems. Progressive reading skills will be measured with both standardized instruments and qualitative observations.
We believe that we are on the verge of discovering and validating a new tool for evaluating growth patterns of young readers. This tool, in contrast to earlier ones, allows us, as researchers, and if validated would allow teachers, to get inside "achievement" and "growth" to learn more about the particular pathways, as indexed by profiles of skill strengths and weaknesses, that distinct groups of readers follow in arriving at particular levels of overall achievement or performance.
The hope is that what we learn about the course of development of these profiles over the primary years will offer us guidance for providing more appropriate and more individually tailored instruction. This approach has the additional virtue of teaching us, as scholars and theorists, something about the nature of reading development in these critical early years. Regularity effects and the phonological deficit model of reading disabilities: Journal of Educational Psychology, 90, Twenty-five years of research on the reading process: The grand synthesis and what it means for our field.
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