Cluster analytic classification of reading disability subtypes

1989 ◽  
Vol 1 (2) ◽  
pp. 163-177 ◽  
Author(s):  
George P. Vogler ◽  
Laura A. Baker ◽  
Sadie N. Decker ◽  
J. C. Defries ◽  
David H. Huizinga
Keyword(s):  
Reading Disability ◽  
Analytic Classification ◽  
Cluster Analytic

Analytic Classification of Fuchsian Singular Points

2004 ◽  
Vol 76 (3/4) ◽  
pp. 348-357 ◽  
Author(s):  
V. A. Kleptsyn ◽  
B. A. Rabinovich
Keyword(s):  
Singular Points ◽  
Analytic Classification

scholarly journals Analytic classification of saddle nodes

2005 ◽  
Vol 66 ◽  
pp. 85-103
Author(s):  
S. M. Voronin ◽  
Yu. I. Meshcheryakova
Keyword(s):  
Analytic Classification

scholarly journals Reading Profiles in Multi-site Data with Missingness

2018 ◽  
Author(s):  
Mark A. Eckert ◽  
Kenneth I. Vaden ◽  
Mulugeta Gebregziabher ◽  
Keyword(s):  
Missing Data ◽  
Reading Disability ◽  
Phonological Processing ◽  
Oral Language ◽  
Cognitive Abilities ◽  
Missing Values ◽  
Independent Set ◽  
Classification Error ◽  
Reading Profiles

AbstractChildren with reading disability exhibit varied deficits in reading and cognitive abilities that contribute to their reading comprehension problems. Some children exhibit primary deficits in phonological processing, while others can exhibit deficits in oral language and executive functions that affect comprehension. This behavioral heterogeneity is problematic when missing data prevent the characterization of different reading profiles, which often occurs in retrospective data sharing initiatives without coordinated data collection. Here we show that reading profiles can be reliably identified based on Random Forest classification of incomplete behavioral datasets, after the missForest method is used to multiply impute missing values. Results from simulation analyses showed that reading profiles could be accurately classified across degrees of missingness (e.g., ~5% classification error for 30% missingness across the sample). The application of missForest to a real multi-site dataset (n = 924) showed that reading disability profiles significantly and consistently differed in reading and cognitive abilities for cases with and without missing data. The results of validation analyses indicated that the reading profiles (cases with and without missing data) exhibited significant differences for an independent set of behavioral variables that were not used to classify reading profiles. Together, the results show how multiple imputation can be applied to the classification of cases with missing data and can increase the integrity of results from multi-site open access datasets.

Sign in / Sign up

Export Citation Format

Share Document