Confirmatory factor analysis of the supports intensity scale for children.
The SIS-C is multidimensional—use its subscale scores separately when planning supports for children with ID.
01Research in Context
What this study did
The team ran a confirmatory factor analysis on the Spanish SIS-C.
They wanted to see if the children’s version keeps the same sub-scales found in the adult tool.
All participants were Spanish-speaking kids with intellectual disability.
What they found
The child scale is multidimensional, just like the adult one.
Seven linked first-order factors fit the data best.
Use each sub-scale score on its own when you plan supports.
How this fits with other research
Giné et al. (2017) later showed the Catalan SIS-C measures the same way the U.S. version does.
Patton et al. (2020) repeated the seven-factor model in Icelandic youth and added local age norms.
Together the papers say the SIS-C keeps its structure across languages, so you can trust the sub-scales no matter which translation you hold.
Why it matters
You no longer have to treat the SIS-C total score as one big number.
Pull the seven sub-scale scores instead and write goals that match the exact area of need.
This move makes your support plans tighter and your justification to funders clearer.
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02At a glance
03Original 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.
Research in developmental disabilities, 2016 · doi:10.1016/j.ridd.2015.11.022