Latent class analysis of children with math difficulties and/or math learning disabilities: Are there cognitive differences?

Abstract

This study investigated whether a latent class of children with math difficulties (MD) or math learning disabilities (MLD) emerged within a heterogeneous sample of learners. A latent class analysis was computed on children (N = 447) in Grade 3 who were administered a battery of math, reading, and cognitive measures. The analysis yielded four important findings. First, a discrete latent class of children with MD (15% of the sample) or MLD (10% of the sample) emerged when setting cut-off scores at or below the 25th and 11th percentile, respectively. Second, model testing yielded a high probability of finding children with MD or MLD with reading problems as well as a latent class of low problem solvers with average reading and calculation scores. Third, knowledge of problem solving component processes, estimation and the executive component of WM were significant and unique correlates of latent classes at both cut-off points. Finally, children defined as MD at 25th percentile cut-off but not 11th percentile cut-off yielded high effect sizes on measures of reading, but not on cognitive measures, when compared with children identified at risk at both cut-off points. The results suggest that a statistically distinct latent class of children at risk for MD or MLD can be separated from a heterogeneous sample of children who vary in math, reading and fluid intelligence.

Linear and nonlinear profiles of weak behavioral and neural differentiation between numerical operations in children with math learning difficulties

“…i.e., a standardized score below 90) on the WIAT-II NumOps subtest, 21 children were classified as having math learning difficulties (MLD group), while 24 children whose scores were at or above 90 on the same test formed the TD group (Table S1). The 25 th percentile was chosen based on the extant literature of cohorts identified as having math learning difficulties (Lambert & Spinath, 2018;Schwartz et al, 2018;Skeide et al, 2018;Swanson et al, 2018;Tolar et al, 2016;Wong & Chan, 2019). We also chose this criterion to ensure an appropriate N of children falling within the low-end of the distribution of abilities (N = 21) in order to conduct categorical analysis of nonlinearity-effects associated with individual differences in math problem-solving skills, and to ensure that individuals with low math abilities did not have comorbid disabilities in general cognitive functions, such as low IQ or impaired working memory compared to TD.…”

How the Language of Instruction Influences Mathematical Thinking Development in the First Years of Bilingual Schoolers

“…Therefore, it seems clear that quite a few authors propose different approaches to show the high influence of language on mathematical thinking. However, the studies carried out by Swanson et al (2019) show how the influence of certain aspects such as reading and vocabulary does not have as much influence on problem solving as working memory does, where a great correlation between these results is obtained in bilingual children, in contrast with monolingual children, also shown in the studies by Swanson et al (2018). Bernardo and Calleja (2005) also showed how the students' linguistic competence in the language of instruction directly influenced the development of their own competences, specifically in mathematics.…”

Working memory and numeracy training for children with math learning difficulties: Evidence from a large-scale implementation in the classroom.

“…Difficulties with numerical magnitude processing and the concept of number have also been found to characterize children with MLD (e.g., Geary, 2011). For instance, Bull et al (2021; see also, Swanson et al, 2018) found that poor number line estimation skills in kindergarten characterized children at risk of developing MLD in elementary school. Indeed, MLD screeners feature tasks that evaluate basic skills like numerical comparison and nonsymbolic-to-symbolic mapping (e.g., Butterworth & Laurillard, 2010; Nosworthy et al, 2013).…”