Combining Old and New for Better Understanding and Predicting Dyslexia

Autor/inn/en	Wagner, Richard K.; Edwards, Ashley A.; Malkowski, Antje; Schatschneider, Chris; Joyner, Rachel E.; Wood, Sarah; Zirps, Fotena A.
Titel	Combining Old and New for Better Understanding and Predicting Dyslexia
Quelle	In: New Directions for Child and Adolescent Development, (2019) 165, S.11-23 (13 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1520-3247
DOI	10.1002/cad.20289
Schlagwörter	Dyslexia; Learning Disabilities; Meta Analysis; Bayesian Statistics; Models; Correlation; Reading Difficulties; Definitions; Prediction + Suchen Sie Ihr Suchwort? Dyslexics; Legasthenie; Lese-Rechtschreib-Schwäche; Learning handicap; Lernbehinderung; Meta-analysis; Metaanalyse; Analogiemodell; Korrelation; Reading difficulty; Leseschwierigkeit; Begriffsbestimmung; Vorhersage
Abstract	Despite decades of research, it has been difficult to achieve consensus on a definition of common learning disabilities such as dyslexia. This lack of consensus represents a fundamental problem for the field. Our approach to addressing this issue is to use model-based meta-analyses and Bayesian models with informative priors to combine the results of a large number of studies for the purpose of yielding a more stable and well-supported conceptualization of reading disability. A prerequisite to implementing these models is establishing informative priors for dyslexia. We illustrate a new approach for doing so based on the known distribution of the difference between correlated variables, and use this distribution to determine the proportion of poor readers whose poor reading is unexpected (i.e., likely to be due to dyslexia) as opposed to expected. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01