ArticleFrontiers in psychology2018
Reading Profiles in Multi-Site Data With Missingness.
Article in Frontiers in psychology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Comparing the phonological, musical, and general cognitive profiles of early-emerging poor, average, and good readers of Chinese.Frontiers in psychology · 2025Article
- Executive Function Associations With Audibility-Adjusted Speech Perception in Noise.Journal of speech, language, and hearing research : JSLHR · 2024Article
- Cortical asymmetries at different spatial hierarchies relate to phonological processing ability.PLoS biology · 2022Article
- From BDNF to reading: Neural activation and phonological processing as multiple mediators.Behavioural brain research · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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 (
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Registered trials
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