Dataset Information

Letter Features as Predictors of Letter-Name Acquisition in Four Languages with Three Scripts.

ABSTRACT: To expand our understanding of script-general and script-specific principles in the learning of letter names, we examined how three characteristics of alphabet letters-their frequency in printed materials, order in the alphabet, and visual similarity to other letters-relate to children's letter-name knowledge in four languages with three distinct scripts (English [N = 318; M age = 4.90], Portuguese [N = 366; M age = 5.80], Korean [N = 168; M age = 5.48], and Hebrew [N = 645; M age = 5.42]). Explanatory item response modeling analysis showed that the frequency of letters in printed materials was consistently related to letter difficulty across the four languages. There were also moderation effects for letter difficulty in English and Korean, and for discriminatory power of letters in Korean. The results suggest that exposure to letters as measured by letter frequency is a language-general mechanism in the learning of alphabet letters.

SUBMITTER: Kim YG

PROVIDER: S-EPMC8734658 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Letter Features as Predictors of Letter-Name Acquisition in Four Languages with Three Scripts.

Kim Young-Suk Grace YG Petscher Yaacov Y Treiman Rebecca R Kelcey Benjamin B

Scientific studies of reading : the official journal of the Society for the Scientific Study of Reading 20201016 6

To expand our understanding of script-general and script-specific principles in the learning of letter names, we examined how three characteristics of alphabet letters-their frequency in printed materials, order in the alphabet, and visual similarity to other letters-relate to children's letter-name knowledge in four languages with three distinct scripts (English [N = 318; M age = 4.90], Portuguese [N = 366; M age = 5.80], Korean [N = 168; M age = 5.48], ...[more]

PMID: 35002210

Similar Datasets

Project description:BackgroundProfiling patients on a proposed 'immunometabolic depression' (IMD) dimension, described as a cluster of atypical depressive symptoms related to energy regulation and immunometabolic dysregulations, may optimise personalised treatment.AimsTo test the hypothesis that baseline IMD features predict poorer treatment outcomes with antidepressants.MethodData on 2551 individuals with depression across the iSPOT-D (n = 967), CO-MED (n = 665), GENDEP (n = 773) and EMBARC (n = 146) clinical trials were used. Predictors included baseline severity of atypical energy-related symptoms (AES), body mass index (BMI) and C-reactive protein levels (CRP, three trials only) separately and aggregated into an IMD index. Mixed models on the primary outcome (change in depressive symptom severity) and logistic regressions on secondary outcomes (response and remission) were conducted for the individual trial data-sets and pooled using random-effects meta-analyses.ResultsAlthough AES severity and BMI did not predict changes in depressive symptom severity, higher baseline CRP predicted smaller reductions in depressive symptoms (n = 376, βpooled = 0.06, P = 0.049, 95% CI 0.0001-0.12, I2 = 3.61%); this was also found for an IMD index combining these features (n = 372, βpooled = 0.12, s.e. = 0.12, P = 0.031, 95% CI 0.01-0.22, I2= 23.91%), with a higher - but still small - effect size compared with CRP. Confining analyses to selective serotonin reuptake inhibitor users indicated larger effects of CRP (βpooled = 0.16) and the IMD index (βpooled = 0.20). Baseline IMD features, both separately and combined, did not predict response or remission.ConclusionsDepressive symptoms of people with more IMD features improved less when treated with antidepressants. However, clinical relevance is limited owing to small effect sizes in inconsistent associations. Whether these patients would benefit more from treatments targeting immunometabolic pathways remains to be investigated.

Dataset Information

Letter Features as Predictors of Letter-Name Acquisition in Four Languages with Three Scripts.

Publications

Letter Features as Predictors of Letter-Name Acquisition in Four Languages with Three Scripts.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets