Dataset Information

A Bayesian Probabilistic Framework for Identification of Individuals with Dyslexia.

ABSTRACT:

Purpose

Bayesian-based models for diagnosis are common in medicine but have not been incorporated into identification models for dyslexia. The purpose of the present study was to evaluate Bayesian identification models that included a broader set of predictors and that capitalized on recent developments in modeling the prevalence of dyslexia.

Method

Model-based meta-analysis was used to create a composite correlation matrix that included common predictors of dyslexia such as decoding, phonological awareness, oral language, but also included response to intervention (RTI) and family risk for dyslexia. Bayesian logistic regression models were used to predict poor reading comprehension, unexpectedly poor reading comprehension, poor decoding, and unexpectedly poor decoding, all at two levels of severity.

Results

Most predictors made independent and substantial contributions to prediction, supporting models of dyslexia that rely on multiple rather than single indicators. RTI was the strongest predictor of poor reading comprehension and unexpectedly poor reading comprehension. Phonological awareness was the strongest predictor of poor decoding and unexpectedly poor decoding, followed closely by family risk.

Conclusion

Bayesian-based models are a promising tool for implementing multiple-indicator models of identification. Ideas for improving prediction and implications for theory and practice are discussed.

SUBMITTER: Wagner RK

PROVIDER: S-EPMC9851422 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

A Bayesian Probabilistic Framework for Identification of Individuals with Dyslexia.

Wagner Richard K RK Moxley Jerad J Schatschneider Chris C Zirps Fotena A FA

Scientific studies of reading : the official journal of the Society for the Scientific Study of Reading 20221222 1

<h4>Purpose</h4>Bayesian-based models for diagnosis are common in medicine but have not been incorporated into identification models for dyslexia. The purpose of the present study was to evaluate Bayesian identification models that included a broader set of predictors and that capitalized on recent developments in modeling the prevalence of dyslexia.<h4>Method</h4>Model-based meta-analysis was used to create a composite correlation matrix that included common predictors of dyslexia such as decod ...[more]

PMID: 36685047

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Dataset Information

A Bayesian Probabilistic Framework for Identification of Individuals with Dyslexia.

Purpose

Method

Results

Conclusion

Publications

A Bayesian Probabilistic Framework for Identification of Individuals with Dyslexia.

Similar Datasets

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