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. 2011 Jan;41(1):66-72.
doi: 10.1007/s10803-010-1024-9.

Autistic traits and autism spectrum disorders: the clinical validity of two measures presuming a continuum of social communication skills

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Autistic traits and autism spectrum disorders: the clinical validity of two measures presuming a continuum of social communication skills

Sven Bölte et al. J Autism Dev Disord. 2011 Jan.

Abstract

Research indicates that autism is the extreme end of a continuously distributed trait. The Social Responsiveness Scale (SRS) and the Social and Communication Disorders Checklist (SCDC) aim to assess autistic traits. The objective of this study was to compare their clinical validity. The SRS showed sensitivities of .74 to .80 and specificities of .69 to 1.00 for autism. Sensitivities were .85 to .90 and specificities .28 to.82 for the SCDC. Correlations with the ADI-R, ADOS and SCQ were higher for the SRS than for the SCDC. The SCDC seems superior to the SRS to screen for unspecific social and communicative deficits including autism. The SRS appears more suitable than the SCDC in clinical settings and for specific autism screening.

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Figures

Fig. 1
Fig. 1
Receiver Operating Characteristics (ROC) curve of the Social Responsiveness Scale (SRS) for the condition autism spectrum disorder (n = 148) versus typical development (n = 77); area under the curve = .98 (p < .0001)
Fig. 2
Fig. 2
Receiver Operating Characteristics (ROC) curve of the Social Responsiveness Scale (SRS) for the condition autism spectrum disorder (n = 148) versus clinical controls (n = 255); area under the curve = .81 (p < .0001)
Fig. 3
Fig. 3
Receiver Operating Characteristics (ROC) curve of the Social and Communication Disorders Checklist (SCDC) for the condition autism spectrum disorder (n = 148) versus typical development (n = 77); area under the curve = .93 (p < .0001)
Fig. 4
Fig. 4
Receiver Operating Characteristics (ROC) curve of the Social and Communication Disorders Checklist (SCDC) for the condition autism spectrum disorder (n = 148) versus clinical controls (n = 255); area under the curve = .64 (p < .0001)

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