Confirmatory factor analysis of the supports intensity scale for children

Abstract: Support needs assessment instruments and recent research related to this construct have been more focused on adults with intellectual disability than on children. However, the design and implementation of Individualized Support Plans (ISP) must start at an early age. Currently, a project f...

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Detalles Bibliográficos
Autores: Verdugo Alonso, Miguel Ángel, Guillén-Martín, Verónica Marina|||0000-0003-2465-6082, Arias, Benito, Vicente, Eva, Badia, Marta
Tipo de recurso: artículo
Fecha de publicación:2016
País:España
Institución:Universidad de Cantabria (UC)
Repositorio:UCrea Repositorio Abierto de la Universidad de Cantabria
Idioma:inglés
OAI Identifier:oai:repositorio.unican.es:10902/10868
Acceso en línea:http://hdl.handle.net/10902/10868
Access Level:acceso abierto
Palabra clave:Intellectual disability
Support needs
Assessment
Confirmatory factor analysis
Support intensity scale for children
Descripción
Sumario:Abstract: Support needs assessment instruments and recent research related to this construct have been more focused on adults with intellectual disability than on children. However, the design and implementation of Individualized Support Plans (ISP) must start at an early age. Currently, a project for the translation, adaptation and validation of the supports intensity scale for children (SIS-C) is being conducted in Spain. In this study, the internal structure of the scale was analyzed to shed light on the nature of this construct when evaluated in childhood. A total of 814 children with intellectual disability between 5 and 16 years of age participated in the study. Their support need level was assessed by the SIS-C, and a confirmatory factor analysis (CFA), including different hypotheses, was carried out to identify the optimal factorial structure of this scale. The CFA results indicated that a unidimensional model is not sufficient to explain our data structure. On the other hand, goodness-of-fit indices showed that both correlated first-order factors and higher-order factor models of the construct could explain the data obtained from the scale. Specifically, a better fit of our data with the correlated first-order factors model was found. These findings are similar to those identified in previous analyses performed with adults. Implications and directions for further research are discussed.