Future academic resilience work should add resilience measures to longitudinal LD datasets and combine person-centered (e.g., LPA) with variable-centered analyses¶

Strategy · All strategies
Evidence · 5 claims (5 for) · 1 study (1 theoretical), q2 · 0 of 1 report an effect size · 5 claims rest on one study

Description¶

The paper recommends longitudinal methods because resilience is a dynamic process, suggesting existing longitudinal LD datasets "incorporate additional measures of potential resilience factors" with explicit tests of main and interactive effects. For cumulative designs, it endorses combining person-centered methods—such as a latent profile analysis deriving resilient subgroups—with variable-centered regressions or ANOVAs testing promotive and protective effects, and notes network modeling (e.g., the hybrid symptoms-and-resilience factor network) as an innovative option for LD research.

Design Implications¶

Context¶

Requirements¶

• Longitudinal datasets with repeated academic outcome measures
• Sufficient sample structure to derive and then analytically test resilience subgroups

Constraints¶

• The paper notes count variables in cumulative designs may not capture variability in the strength of associations between factors and outcomes, citing Narayan et al. (2023)

Target Learners¶

• children across the risk gradient for learning disabilities

Target Learning Goals¶

• identifying how and when resilience mechanisms emerge and their stability over time

Affordances¶

• Dp Resilience Framework Ld

Claims¶

• Emerging reading and math resilience literatures suggest candidate resilience factors across cognitive, emotion/motivation, social/environmental, and neural levels, most not yet classified as promotive or protective [+W]
• Explicit, instructional home numeracy activities acted as protective (not promotive) factors for 1st–2nd grade math performance in Torppa (2025) [+W]
• Promotive factors are associated with positive outcomes equally in low- and high-risk contexts, producing gap-maintaining effects identified via main effects [+W]
• Protective factors attenuate the effects of risk as risk rises, producing gap-closing effects represented analytically as Risk × Protective Factor interactions [+W]
• Vocabulary was a promotive but not protective factor for word reading: its interaction with phonological awareness was skill-enhancing, benefiting the lower-risk group [+W]

Related Strategies¶

• Develop complementary LD interventions that strengthen promotive and protective factors alongside interventions remediating core weaknesses
• Future research should clarify construct boundaries, strengthen content-validity evidence, and directly compare competing measurement models

Examples¶

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Key Sources¶

• Slomowitz R. F., Narayan A. J., & McGrath L. M. (2025). Resilience research in learning disabilities: Guiding principles from developmental psychopathology. Learning and Instruction. https://doi.org/10.1016/j.learninstruc.2025.102090