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.

中文翻译：

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当一个尺寸不适合所有人时：对低收入学龄前儿童数学技能的潜在概况分析。

平均而言，来自低收入家庭的学龄前儿童在符号数字任务上的表现比来自中等和高收入家庭的学龄前儿童差。尽管最近的许多研究已经开发和测试了针对低收入学龄前儿童的数学干预措施，但这一人群中的变异性却很少受到关注。本研究的目的是使用以人为本的分析来描述低收入儿童数学技能的可变性。我们对六项学龄前儿童（N = 115，平均年龄 = 4.6 岁）的数字能力（非符号量级比较、语言计数、对象计数、基数、数字识别和符号量级比较）进行了潜在概况分析。结果显示了不同的优势和劣势模式，并揭示了四种数字技能概况：(a) 所有数值测量的数学能力较差 (n = 13), (b) 所有数值测量的数学能力强 (n = 41), (c) 所有数值测量的中等能力 (n = 35), 和 (d ) 强大的计数和数字技能，但量级技能较差 (n = 26)。儿童的年龄、工作记忆和抑制控制显着预测了他们的档案成员。我们发现了配置文件之间定量和定性差异的证据，例如一些配置文件在任务中的表现比其他配置文件更高，但整体表现模式因评估的不同数字技能而异。(d) 强大的计数和数字技能，但量级技能较差 (n = 26)。儿童的年龄、工作记忆和抑制控制显着预测了他们的档案成员。我们发现了配置文件之间定量和定性差异的证据，例如一些配置文件在任务中的表现比其他配置文件更高，但整体表现模式因评估的不同数字技能而异。(d) 强大的计数和数字技能，但量级技能较差 (n = 26)。儿童的年龄、工作记忆和抑制控制显着预测了他们的档案成员。我们发现了配置文件之间定量和定性差异的证据，例如一些配置文件在任务中的表现比其他配置文件更高，但整体表现模式因评估的不同数字技能而异。