How Cognitive Strengths Compensate Weaknesses Related to Specific Learning Difficulties in Fourth-Grade Children

The goal of the present study was to investigate whether children's cognitive strengths can compensate the accompanied weaknesses related to their specific learning difficulties. A Bayesian multigroup mediation SEM analysis in 281 fourth-grade children identified a cognitive compensatory mechanism in children with mathematical learning difficulties (n = 36): Children with weak number sense, but strong rapid naming performed slightly better on mathematics compared to peers with weak rapid naming. In contrast, a compensatory mechanism was not identified for children with a comorbid mathematical and reading difficulty (n = 16). One explanation for the latter finding could relate to the lack of ability to compensate, because of the difficulties these children experience in both academic domains. These findings lead to a new direction in research on learning difficulties in mathematics and/or reading by suggesting that children with a learning disability each have a unique profile of interrelated cognitive strengths and weaknesses. Children might compensate with these strengths for their weaknesses, which could lead to (small) learning gains in the affected domain.

Implications of Error Analysis Studies for Academic Interventions

We reviewed 13 studies that focused on analyzing student errors on achievement tests from the Kaufman Test of Educational Achievement–Third edition (KTEA-3). The intent was to determine what instructional implications could be derived from in-depth error analysis. As we reviewed these studies, several themes emerged. We explain how a careful analysis of errors is key to planning the most appropriate instructional interventions.

Patterns of Strengths and Weaknesses on the WISC-V, DAS-II, and KABC-II and Their Relationship to Students’ Errors in Oral Language, Reading, Writing, Spelling, and Math

This study investigated the relationship between specific cognitive patterns of strengths and weaknesses and the errors children make on oral language, reading, writing, spelling, and math subtests from the Kaufman Test of Educational Achievement–Third Edition (KTEA-3). Participants with scores from the KTEA-3 and either the Wechsler Intelligence Scale for Children–Fifth Edition (WISC-V), Differential Ability Scales–Second Edition (DAS-II), or Kaufman Assessment Battery for Children–Second Edition (KABC-II) were selected based on their profile of scores. Error factor scores for the oral and written language tests were compared for three groups: High Gc paired with low processing speed, long-term memory, and/or reasoning abilities; Low Gc paired with high speed, memory, and/or reasoning; and Low orthographic and/or phonological processing. Error factor scores for the math tests were compared for three groups: High Gc profile; High Gf paired with low processing speed and/or long-term memory; and Low Gf paired with high processing speed and/or long-term memory. Results indicated a difference in Oral Expression and Written Expression error factor scores between the group with High Gc paired with low processing speed, long-term memory, and/or reasoning abilities; and the group with Low Gc paired with high speed, memory, and/or reasoning.

Error Analysis

This commentary will take an historical perspective on the Kaufman Test of Educational Achievement (KTEA) error analysis, discussing where it started, where it is today, and where it may be headed in the future. In addition, the commentary will compare and contrast the KTEA error analysis procedures that are rooted in psychometric methodology and the process approach to error analysis which is derived primarily from cognitive neuropsychology.

How Achievement Error Patterns of Students With Mild Intellectual Disability Differ From Low IQ and Low Achievement Students Without Diagnoses

This study investigated the differences in error factor scores on the Kaufman Test of Educational Achievement–Third Edition between individuals with mild intellectual disabilities (Mild IDs), those with low achievement scores but average intelligence, and those with low intelligence but without a Mild ID diagnosis. The two control groups were matched with the Mild ID clinical cases on demographic variables including age, gender, and parental education. Results showed significant differences between the groups on several error factors, particularly between the Mild ID group and the two control groups, and no significant differences between all three groups on six error factors. In addition, the two control groups differed significantly on four error factors. Implications for intervention selection, diagnostic considerations, and future directions for achievement test creation are discussed.

Commentary

The articles presented in this Special Issue provide evidence for many statistically significant relationships among error scores obtained from the Kaufman Test of Educational Achievement, Third Edition (KTEA)-3 between various groups of students with and without disabilities. The data reinforce the importance of examiners looking beyond the standard scores when analyzing results. Although the data in these articles are powerful by themselves, this commentary explores the potential advantages of considering additional information to increase the practicality of these results. Although statistical significance may provide evidential validity of the results, and the articles inform clinical practice and offer valuable leads for further research that should be pursued, the present authors question whether the data as presented provide sufficient information to determine the predictive and practical utility of these initial results. The next step, we believe, is to extend these novel approaches to data from larger, carefully defined samples of students with specific learning challenges and disabilities.

Do Cognitive Patterns of Strengths and Weaknesses Differentially Predict Errors on Reading, Writing, and Spelling?

This study investigated the relationship between specific cognitive patterns of strengths and weaknesses (PSWs) and the errors children make in reading, writing, and spelling tests from the Kaufman Test of Educational Achievement–Third Edition (KTEA-3). Participants were selected from the KTEA-3 standardization sample based on five cognitive profiles: High Crystallized Ability paired with Low Processing Speed and Long-Term Retrieval (High Gc), Low Crystallized Ability paired with High Processing Speed and Long-Term Retrieval (High Gs/ Glr), Low Orthographic Processing (Low OP), Low Phonological Processing (Low PP), and Low Phonological Processing paired with Low Orthographic Processing (Low PP_OP). Error factor scores for all five groups were compared on Reading Comprehension and Written Expression; the first four groups were compared on Letter & Word Recognition, Nonsense Word Decoding, and Spelling, and the first three groups were compared on Phonological Processing. Significant differences were noted among the patterns of errors demonstrated by the five groups. Findings support the notion that students with diverse cognitive PSWs display different patterns of errors on tests of academic achievement.