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

Area of Science:

• Developmental Psychology
• Cognitive Science
• Educational Psychology

Background:

• Preschoolers from lower-income households often exhibit lower performance on symbolic numerical tasks compared to peers from higher-income backgrounds.
• While math interventions exist, the heterogeneity within the low-income preschool population's numerical abilities remains understudied.

Purpose of the Study:

• To characterize the variability in numerical skills among low-income preschoolers using a person-centered approach.
• To identify distinct profiles of mathematical abilities within this demographic.

Main Methods:

• Latent profile analysis was employed on six numerical measures (nonsymbolic magnitude comparison, verbal counting, object counting, cardinality, numeral identification, symbolic magnitude comparison).
• Participants included 115 preschoolers (mean age = 4.6 years) from lower-income households.
• Predictors of profile membership included age, working memory, and inhibitory control.

Main Results:

• Four distinct numerical skill profiles emerged: (a) poor overall math abilities (n=13), (b) strong overall math abilities (n=41), (c) moderate overall math abilities (n=35), and (d) strong counting/numeral skills with poor magnitude skills (n=26).
• Significant predictors of profile membership were identified as children's age, working memory capacity, and inhibitory control.
• Quantitative and qualitative differences were observed across profiles, indicating varying levels of performance and distinct patterns of strengths and weaknesses.

Conclusions:

• Low-income preschoolers exhibit diverse numerical profiles, challenging the assumption of a monolithic deficit.
• Understanding these distinct profiles is crucial for tailoring effective educational interventions.
• Cognitive factors like age, working memory, and inhibitory control play a role in shaping these numerical skill profiles.