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Use of a Video Scoring Anchor for Rapid Serial Assessment of Social Communication in Toddlers
Published on: March 14, 2018
Evaluation of the Psychometric Properties of the Social Communication Questionnaire in Rural Kenya
Patricia Kipkemoi1,2, Jeanne E Savage3, Joseph Gona4
1Neuroscience Unit, KEMRI-Wellcome Trust Research Programme, P.O Box 230-80108, Kilifi, Kenya. pkipkemoi@kemri-wellcome.org.
Abstract:
Children can be reliably diagnosed with autism as early as 3 years of age, and early interventions are initiated. There is often a significant gap between the age of onset of symptoms (2-3 years) and diagnosis (8-10 years) in Africa. We conducted a study to validate the Social Communication Questionnaire (SCQ) as a screening instrument in a rural setting in Kenya. The study was conducted along the Kenyan Coast. Study participants included 172 children with a neurodevelopmental disorder (NDD) diagnosis (84 of which were autism) and 112 controls. Internal consistency was evaluated through the use of Cronbach's alpha, confirmatory factor analysis (CFA) with maximum likelihood procedure to assess the conceptual model for the SCQ. Additionally, the sensitivity and specificity of cut-off scores using ROC analysis and item difficulties and discrimination quality using an IRT framework were also assessed. Factor analysis revealed an adequate fitting model for the three-factor DSM-IV-TR (root mean squared error of approximation (RMSEA) = 0.050; Comparative Fit Index (CFI) = 0.974; Tucker-Lewis Index (TLI) = 0.973) and two-factor DSM-5 factor structure (RMSEA = 0.050; CFI = 0.972; TLI = 0.974). The reliability coefficient alphas for the whole group for all items (Cronbach's α = 0.90) and all three domains (Cronbach's α = 0.68-0.84) were acceptable to excellent. The recommended cut-off score of 15 yielded 72% sensitivity and 100% specificity in the ASD group compared to the typically developing group. We provide early evidence of the adequate factor structure and good internal consistency of the SCQ. We also note that the recommended cut-off yielded sufficient predictive validity.
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