Dataset Information

Data on preschool children?s math, patterning, and spatial knowledge.

ABSTRACT: Initial participants were 79 children who were recruited from six preschool programs in the U.S. Full assessment data was available for 73 children (average age of 4 years 7 months), including demographic data (gender, ethnicity, financial need, language(s) spoken at home and special education status). Children?s math, repeating patterning, spatial and verbal skills were assessed at the beginning of the pre-kindergarten year. Assessments included the brief version of the Research-Based Early Mathematics Assessment, two measures of repeating patterning skills, three measures of spatial skills (the Block Design subtest of the Wechsler Preschool and Primary Scale of Intelligence, the Position in Space subtest of the Developmental Test of Visual Perception, and a Corsi Block Tapping Task), the Picture Vocabulary Test from the NIH Toolbox app and a backward letter span task. Near the end of the school year, their math knowledge was re-assessed using the same math measure, as was their memory span (forward and backward digit span task from the Wechsler Intelligence Scale for Children). Findings on the relations between patterning, spatial and math skills are published elsewhere (Rittle-Johnson et al., 2018) [1].

SUBMITTER: Rittle-Johnson B

PROVIDER: S-EPMC6092477 | biostudies-other | 2018 Oct

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Data on preschool children׳s math, patterning, and spatial knowledge.

Rittle-Johnson Bethany B Zippert Erica L EL Boice Katherine L KL

Data in brief 20180731

Initial participants were 79 children who were recruited from six preschool programs in the U.S. Full assessment data was available for 73 children (average age of 4 years 7 months), including demographic data (gender, ethnicity, financial need, language(s) spoken at home and special education status). Children׳s math, repeating patterning, spatial and verbal skills were assessed at the beginning of the pre-kindergarten year. Assessments included the brief version of the <i>Research-Based Early ...[more]

PMID: 30112435

Similar Datasets

Project description:ImportanceChild-directed mobile applications (apps) have been found to collect digital identifiers and transmit them to third-party companies, a potential violation of federal privacy rules. This study seeks to examine the differences in app data collection and sharing practices by evaluating the sociodemographic characteristics of the children who play them.ObjectiveTo examine data collection and sharing practices of 451 apps played by young children and to test associations with child sociodemographic characteristics.Design, setting, and participantsThis study used data from the baseline phase of the Preschooler Tablet Study, a prospective cohort study conducted from August 2018 to January 2020. This study used a population-based sample. A convenience sample of the parents of preschool-aged children was recruited from pediatric offices, childcare centers, social media posts, and an online participant registry. Eligibility criteria included (1) parent or guardian of a child aged 3 to 5 years, (2) parent or guardian who lived with the child at least 5 days per week, (3) participants who spoke English, and (4) a child who used an Android (Google LLC) device. All interactions with participants were through email, online surveys, and mobile device sampling.ExposuresSociodemographic characteristics were assessed by parental report.Main outcomes and measuresThis study tested the hypothesis that data transmissions to third-party domains are more common in apps played by children from low-socioeconomic-status homes. Child app usage was assessed via a mobile sampling app for an average of 9 days. Persistent identifier data transmissions to third-party domains were quantified for each app using an instrumented Android environment with monitoring of network traffic; for each child, the counts of total data transmissions were calculated, and the total third-party domains were detected for the apps they played.ResultsOur sample comprised 124 children who used Android devices (35 tablets, 89 smartphones; 65 girls [52%]; mean [SD] age, 3.85 [0.57] years; 87 non-Hispanic White [71%]). One hundred twenty of participating parents (97%) were women. Of 451 apps tested, 303 (67%) transmitted persistent identifiers to 1 to 33 third-party domains. Child data transmission counts ranged from 0 to 614 (median [interquartile range], 5.0 [1-17.5]) and third-party domain counts from 0 to 399 (4.0 [1-12.5]). In multivariable negative binomial regression models, higher transmission and third-party domain rates per app were positively associated with older age (rate ratio, 1.67 [95% CI, 1.20-2.33]; P = .002 and 1.69 [95% CI, 1.26-2.27]; P < .001, respectively) and lower parent educational attainment (eg, high school or General Educational Development or less rate ratio, 2.29 [95% CI, 1.20-4.39]; P = .003 and 2.05 [95% CI, 1.13-3.70]; P < .02, respectively), but not with household income.Conclusions and relevanceThis study found that apps used by young children had a high frequency of persistent identifier transmissions to third-party companies, suggesting that federal privacy rules are not being enforced. Older children, those with their own devices, or those from lower-education households may be at higher risk of potential privacy violations.

Project description:BackgroundThe core symptoms of autism spectrum disorder (ASD) are widely theorized to result from altered brain connectivity. Diffusion-weighted magnetic resonance imaging (DWI) has been a versatile method for investigating underlying microstructural properties of white matter (WM) in ASD. Despite phenotypic and etiological heterogeneity, DWI studies in majority male samples of older children, adolescents, and adults with ASD have largely reported findings of decreased fractional anisotropy (FA) across several commissural, projection, and association fiber tracts. However, studies in preschool-aged children (i.e., < 30-40 months) suggest individuals with ASD have increased measures of WM FA earlier in development.MethodsWe analyzed 127 individuals with ASD (85♂, 42♀) and 54 typically developing (TD) controls (42♂, 26♀), aged 25.1-49.6 months. Voxel-wise effects of ASD diagnosis, sex, age, and their interaction on DWI measures of FA, mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) were investigated using tract-based spatial statistics (TBSS) while controlling mean absolute and relative motion.ResultsCompared to TD controls, males and females with ASD had significantly increased measures of FA in eight clusters (threshold-free cluster enhancement p < 0.05) that incorporated several WM tracts including regions of the genu, body, and splenium of the corpus callosum, inferior frontal-occipital fasciculi, inferior and superior longitudinal fasciculi, middle and superior cerebellar peduncles, and corticospinal tract. A diagnosis by sex interaction was observed in measures of AD across six significant clusters incorporating areas of the body, genu, and splenium of the corpus collosum. In these tracts, females with ASD showed increased AD compared to TD females, while males with ASD showed decreased AD compared to TD males.ConclusionsThe current findings support growing evidence that preschool-aged children with ASD have atypical measures of WM microstructure that appear to differ in directionality from alterations observed in older individuals with the condition. To our knowledge, this study represents the largest sample of preschool-aged females with ASD to be evaluated using DWI. Microstructural differences associated with ASD largely overlapped between sexes. However, differential relationships of AD measures indicate that sex likely modulates ASD neuroanatomical phenotypes. Further longitudinal study is needed to confirm and quantify the developmental relationship of WM structure in ASD.

Dataset Information

Data on preschool children?s math, patterning, and spatial knowledge.

Publications

Data on preschool children׳s math, patterning, and spatial knowledge.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets