Evidence map›Paper›PMID 34089919›Full record

ArticleJournal of experimental child psychology2021

When one size does not fit all: A latent profile analysis of low-income preschoolers' math skills.

Nicole R Scalise, Emily N Daubert, Geetha B Ramani

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Article in Journal of experimental child psychology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Nicole R ScaliseDepartment of Cognitive Sciences, University of California, Irvine, Irvine, CA 92697, USA; Department of Human Development and Quantitative Methodology, University of Maryland, College Park, College Park, MD 20742, USA. Electronic address: nicole.scalise@uci.edu.
Emily N DaubertDepartment of Psychology, University of Hawai'i at Mānoa, Honolulu, HI 96822, USA; Department of Human Development and Quantitative Methodology, University of Maryland, College Park, College Park, MD 20742, USA.
Geetha B RamaniDepartment of Human Development and Quantitative Methodology, University of Maryland, College Park, College Park, MD 20742, USA.

Funding

Training Program in Social DevelopmentT32HD007542 · NICHD · UNIV OF MARYLAND, COLLEGE PARK · PI KILLEN, MELANIE ANN · 2003 to 2015
$1.9M
NICHD NIH HHS T32 HD007542
6 · The paper itself

Abstract

On average, preschoolers from lower-income households perform worse on symbolic numerical tasks than preschoolers from middle- and upper-income households. Although many recent studies have developed and tested mathematics interventions for low-income preschoolers, the variability within this population has received less attention. The goal of the current study was to describe the variability in low-income children's math skills using a person-centered analysis. We conducted a latent profile analysis on six measures of preschoolers' (N = 115, mean age = 4.6 years) numerical abilities (nonsymbolic magnitude comparison, verbal counting, object counting, cardinality, numeral identification, and symbolic magnitude comparison). The results showed different patterns of strengths and weaknesses and revealed four profiles of numerical skills: (a) poor math abilities on all numerical measures (n = 13), (b) strong math abilities on all numerical measures (n = 41), (c) moderate abilities on all numerical measures (n = 35), and (d) strong counting and numeral skills but poor magnitude skills (n = 26). Children's age, working memory, and inhibitory control significantly predicted their profile membership. We found evidence of quantitative and qualitative differences between profiles, such that some profiles were higher performing across tasks than others, but the overall patterns of performance varied across the different numerical skills assessed.

Indexed as

AttentionMemory, Short-TermChildChild, PreschoolHumansIncomeMathematicsPovertyCognitive developmentExecutive functionLatent profile analysisLow incomeNumerical knowledgePreschool mathematics

Identifiers

PMID34089919
PMCPMC9119525

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.