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Challenging existing Raven's norms based on children attending government schools in India
Margreet Vogelzang1,2, Mandy Wigdorowitz1,3,4, Ianthi M Tsimpli1
1Department of Theoretical and Applied Linguistics, University of Cambridge, 9 West Road, Cambridge, CB3 9DA UK.
Abstract:
Intelligence measures, such as the Raven's Coloured Progressive Matrices (CPM), rely on norm-referenced scores to interpret individual performance and guide educational or clinical interventions. However, when norm comparisons result in a severely skewed distribution, the validity of the norm sample must be scrutinized rather than accepting implausible interpretations. In this study, we challenge existing norms and improve the suitability of the CPM for Indian children attending government schools by developing new norms that better reflect their socio-demographic and educational contexts. Our sample includes 1,752 children in Delhi, Hyderabad, and Patna from two large-scale projects. Using existing CPM norms for India, we found that 78.5% of our sample scored in the bottom 10th percentile with an IQ score of 80 or lower, highlighting a heavily skewed distribution. Upon reviewing the existing norm sample, we identified several limitations: its size was five times smaller than our sample, the sample's linguistic background was underspecified, and it predominantly represented children from higher socio-economic backgrounds attending private schools. This norm sample fails to represent the majority of Indian pupils, particularly those in government schools, who constitute approximately two-thirds of the pupil population. In response, we developed alternative norms across four age categories (8, 9, 10, 11) that better align with the demographic realities of this group. These norms demonstrate an expected distribution of scores and provide a better benchmark for evaluating intelligence, specifically visuo-spatial reasoning, of Indian children attending government schools. We encourage researchers, educators and clinicians to use these norms as appropriate.
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