PERSONAL AND SCHOOL RELATED FACTORS PREDICTING RESILIENCE IN STUDENTS WITH LEARNING DISABILITIES

This study was conducted to investigate factors that contribute to resilience in students with learning disabilities (LD). The risk-resilience framework provided the theoretical base for selecting school and personal factors that might predict resilience. School and personal data were requested from large, culturally and linguistically diverse samples of individuals diagnosed with LD. A 12 variable model and three cluster models (combined variables) were developed. Discriminant analysis and tests of significance of hit rates were conducted to assess the accuracy of the full model (all 12 variables) to the prediction of resilience, and full versus restricted model testing was done to assess individual variable and cluster (combinations of some variables) contributions to the model. Additionally, analyses of environmental, intrapersonal, and interpersonal cluster models were investigated to determine their relative contribution to the prediction of resilience in relation to the others. Results of the full model analysis and subsequent tests of significance of hit rate indicated modest cross validated classification accuracy for the total group, resilient group, and non-resilient group. However, the model was not significantly better than chance, overall, at predicting resilience and non-resilience in students with LD. Results of the analysis of individual predictor variables’ and clusters’ contributions to the model’s classification accuracy indicated that no individual variable within the full model, nor cluster of interrelated variables contributed significant
incremental improvement in classification accuracy above and beyond that which is available from all other variables contained in the full model. The independent analysis of interrelated personal and school related factors clustered as environmental, interpersonal, and intrapersonal clusters revealed that, as unique and separate models, classification accuracy of cross-validated group cases were less than optimal for each cluster. The results further demonstrate that resilience is affected by both internal and external factors. Although the results also demonstrate that factors work together, a great deal is still to be learned regarding factors affecting resilience as well as their interplay in clusters of factors that affect resilience.