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Reading Profiles in Multi-Site Data With Missingness
Children 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 affe...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5952106/ https://www.ncbi.nlm.nih.gov/pubmed/29867632 http://dx.doi.org/10.3389/fpsyg.2018.00644 |
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author | Eckert, Mark A. Vaden, Kenneth I. Gebregziabher, Mulugeta |
author_facet | Eckert, Mark A. Vaden, Kenneth I. Gebregziabher, Mulugeta |
author_sort | Eckert, Mark A. |
collection | PubMed |
description | Children 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 with missingness (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. |
format | Online Article Text |
id | pubmed-5952106 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-59521062018-06-04 Reading Profiles in Multi-Site Data With Missingness Eckert, Mark A. Vaden, Kenneth I. Gebregziabher, Mulugeta Front Psychol Psychology Children 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 with missingness (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. Frontiers Media S.A. 2018-05-08 /pmc/articles/PMC5952106/ /pubmed/29867632 http://dx.doi.org/10.3389/fpsyg.2018.00644 Text en Copyright © 2018 Eckert, Vaden, Gebregziabher and Dyslexia Data Consortium. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychology Eckert, Mark A. Vaden, Kenneth I. Gebregziabher, Mulugeta Reading Profiles in Multi-Site Data With Missingness |
title | Reading Profiles in Multi-Site Data With Missingness |
title_full | Reading Profiles in Multi-Site Data With Missingness |
title_fullStr | Reading Profiles in Multi-Site Data With Missingness |
title_full_unstemmed | Reading Profiles in Multi-Site Data With Missingness |
title_short | Reading Profiles in Multi-Site Data With Missingness |
title_sort | reading profiles in multi-site data with missingness |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5952106/ https://www.ncbi.nlm.nih.gov/pubmed/29867632 http://dx.doi.org/10.3389/fpsyg.2018.00644 |
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