Challenging existing Raven's norms based on children attending government schools in India

Challenging existing Raven's norms based on children attending government schools in India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Challenging existing Raven's norms based on children attending government schools in India Margreet Vogelzang, Mandy Wigdorowitz, Ianthi Maria Tsimpli This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8902845/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Intelligence measures, such as the Raven's Coloured Progressive Matrices (CPM), rely on norm 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 accept 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 new 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. Raven's Coloured Progressive Matrices intelligence test visuo-spatial reasoning norm scores India government schools Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Having norms for measures of intelligence ( g ) and other psychological constructs serves as a reliable and valid way to fairly compare, position, and identify potentially vulnerable individuals against a representative sample. Norm scores provide a standard against which individual test scores can be interpreted, making it possible to determine how an individual's score fares against that of a similar group and, in turn, provides a marker of performance that can be used for educational or clinical intervention. Comparison of a sample against the respective norm group should follow a Gaussian distribution whereby the majority of the sample scores within an average range and a smaller proportion either excels or scores poorly. Norm scores capture this normal variation and can be used to appropriately classify the performance of individuals against which the norm sample claims to generalize. But what happens in cases where reference to norm scores disproportionately renders a large proportion of children as poor or vulnerable performers? Does such an interpretation suggest that a large group of children which are compared against the norms have intellectual challenges or risks, or are the norms themselves perhaps unrepresentative of the population they claim to represent? In this paper, we take the latter stance by (1) providing evidence showing that a group of Indian children attending government schools fare markedly poorer than expected against the Indian Raven’s norm scores, and in response, (2) creating more representative Raven's norms for this population of Indian children. We aim to improve the suitability of Raven's Coloured Progressive Matrices (CPM) for Indian government primary school pupils by establishing norms that are more reflective of their demographic, geographic, and educational backgrounds. Below we provide an overview of what the Raven’s Progressive Matrices measure, including how and for whom they have been used. We then explore the cultural factors that influence performance and examine the role of socio-demographic indicators. Additionally, we discuss the broader educational and socio-economic context for children in India, particularly those attending government schools, and highlight the issues with the current CPM norm scores. 1.1 Cultural and socio-economic status on Raven's performance The Raven’s Progressive Matrices (RPM) are measures of fluid intelligence, designed to capture (non-verbal) visuo-spatial reasoning without relying on test-takers’ prior knowledge, education, or experience. RPM does not rely on linguistic ability but rather uses visual material that requires the selection of a correct image from an array of options that adheres to a pattern. There are three standardised versions of the RPM, namely (1) the Standard Progressive Matrices (SPM) which is suitable for the general population for individuals aged 6 and over who have average intelligence, (2) the Advanced Progressive Matrices (APM) which is a more difficult version of the test and is suitable for people with above-average intelligence aged 12 and older, and (3) the Coloured Progressive Matrices (CPM) which is designed for children between the ages of 4 to 11 and can also be used to assess visuo-spatial reasoning of elderly individuals and people with learning difficulties (Raven, 2003 ). Each version has been designed as fit-for-purpose and appropriate for particular populations, ensuring that non-verbal intelligence can be measured for a wide range of age groups and ability levels. Scores positively increase with age, and it has been observed that people are obtaining higher intelligence test scores over time with an average increase of three points per decade (known as the ‘Flynn Effect’, Flynn, 2012 ). Since its development by John C. Raven in 1936 (Raven, 1936 , 1941 ), all versions of the RPM have been widely employed and normed across various global contexts for experimental, educational and clinical means to ensure its ecological validity (Brouwers et al., 2009 ; Cotton et al., 2005 ; Raven, 2000 ). Because of its non-verbal design and easy implementation, the RPM is thought to be applicable cross-culturally for children and adults and not confounded by cultural or national differences such as level of education, socio-economic status, and other exogenous factors (e.g., Rushton et al., 2004 , Valencia, 1984 ). However, it has been argued that the RPM cannot be culturally neutral because intelligence itself and its assessments are intrinsically cultural constructs (Frijda & Jahoda, 1966 ). Such cultural loading – the extent to which a test or test item reflects the values, language, information, and experiences of a specific cultural context – inherently plays a role in performance (Jensen, 1974 ). In fact, some studies suggest that visuo-spatial tests, like the RPM, are influenced by culture to a greater extent than verbal tests (Ardila & Moreno 2001 ; Rosselli & Ardila, 2003 ) and are significantly affected by training (Jaeggi et al., 2008 ), repeated testing (Ombrédane et al., 1956 ; Wing, 1980 ) and bilingualism (Bialystok & Shapero, 2005 ; Tsimpli et al., 2020a ). To address performance disparities and such method biases, a distinction has been drawn between the construct of intelligence itself and intelligence test scores , whereby competence can be seen as separate from performance (Van de Vijver & Leung, 1997 ). In response to ensuring culturally responsive testing practices, there is ample research investigating the psychometric soundness and applicability of the RPM across various populations and cultural contexts. However, its development, adaptation, and the majority of norming, educational, and clinical studies come from W.E.I.R.D. (Western, Educated, Industrialised, Rich and Democratic, Henrich et al., 2010 ) samples and contexts, predominantly in the Global North (Brouwers et al., 2009 ; Raven & Raven, 2000 ). In cases where the RPM is evaluated in countries or cultures that are not considered W.E.I.R.D., there are notable concerns questioning its cultural neutrality, fairness, and appropriateness (for a review, see Gonthier, 2022 ). This is largely because test-takers regularly perform suboptimally in comparison to norm reference groups or their W.E.I.R.D. test-taker counterparts. For example, in many African countries, RPM scores are considerably lower than expected, with some even falling below 1 standard deviation of the mean (see for example, South Africa: Knoetze et al., 2005 ; Libya: Al-Shahomee & Lynn, 2010 ; Ghana: Anum, 2014 ; Kenya: Costenbader & Ngari, 2001 ). Furthermore, some properties of the RPM do not hold up to psychometric standards in non-W.E.I.R.D. contexts, as illustrated in a review of the RPM in African samples (Wicherts et al., 2010 ). Cultural confounds have also been found to contribute to different scores for different ethnic and gender groups which have led to unsubstantiated and damaging conclusions about gender/race and intelligence in the past (see Lynn & Vanhanen, 2006 ; Raven & Raven, 2000 ; Wicherts et al., 2010 ). However, gender differences are not always observed (Kazem et al., 2009 ; Raven, 2012 ). In no way is the poorer performance of non-W.E.I.R.D. samples on the RPM indicative of innate difficulties in visuo-spatial reasoning; rather, it reflects biases and the cultural appropriateness of the test itself. Non-verbal intelligence tests rely on the manipulation of some type of visual material (e.g., puzzles), but if such material or its perceptual manipulations differ, are limited, or absent in home, pre-school, or school contexts, then it is no surprise that test-takers would be disadvantaged if they are required to transfer specific skills which have not been honed. Simply engaging with the RPM requires prior experience with visuo-spatial processes, gestalt reasoning and an understanding of test taking in general. From knowing the names and representations of varying geometric shapes to recognising numerical cues and more complex symbolic representations, a myriad of reference points are needed to decipher Raven’s items (Ardila & Moreno, 2001 ; Pontius, 1995 ). In addition, there are possible language barriers:although the test itself is considered non-verbal, language is essential for relaying the test instructions. Collectively, these processes rely on previous experience and engagement with a specific mode of representation such that greater experience translates to greater ease of item manipulation. Being at least critical and cautious about a blanket assumption of cultural fairness in intelligence testing is therefore warranted when evaluating scores either in their raw form or against a norm. While re-standardization and validation of assessments in local populations can reduce cultural biases and result in the development of local norms, such measures do not completely remove the effects of exogenous factors on test scores. One of the most significant predictors of RPM performance are socio-demographic indicators, such as socio-economic status (SES). SES, while measured in different ways, provides an estimate of social affluence and serves as a proxy for one's access to resources and opportunities within a society. SES has been widely studied and found to be positively related to language development, educational attainment, health outcomes and many other population-level variables (e.g., Brito & Noble, 2014 ; Broer et al., 2019 ). Importantly, SES has been linked to fluid intelligence performance, with individuals in lower SES groups underperforming those in higher SES groups (for a meta-analysis, see Peng et al., 2019 ). In a study investigating the impact of SES on fluid intelligence in Ghanaian children (6–12 years old), Anum ( 2022 ) found a disparity in performance on the CMP where children attending private schools (a proxy for higher SES) scored higher than those attending public schools (a proxy for lower SES). These findings demonstrate the strong influence that SES can have on the RPM performance in children, particularly in developing countries, and raises an important question about whether single and blanket norm scores can adequately capture visuo-spatial reasoning of children where disparities in SES are pronounced and evident in terms of educational access and equity. Such a distinction also exists in India, where schooling quality is largely determined by economic resources. Culturally appropriate norms for diverse groups of people in the context of India are therefore needed. In the next section, we review the educational system in India and discuss the development and application of the Indian CPM along with its limitations. 1.2 Education and Raven’s in India: insights and issues India is the world’s most populous country with an estimated population of nearly 1.5 billion people, of which approximately 25% (370 million) are children between the ages of 0–14 (The World Bank Group, 2025 ). Roughly two-thirds of school-going children in India attend government schools, with the remaining third attending private schools (Government of India, 2021 -22). India is a highly multilingual and diverse country and is home to over 424 indigenous languages, of which 22 are considered ‘scheduled’ and used for official purposes (e.g., Telugu, Kannada) with English serving as a link language alongside Hindi (Census of India, 2011 ; Eberhard et al., 2024 ). It also has one of the highest linguistic diversity indexes of 0.914 as reported by UNESCO ( 2009 ; range 0–1). About 27 languages, including English, are used as mediums of instruction across government schools depending on the location and local language(s) of the region (Eberhard et al., 2024 ; Mohanty, 2006 ). Private education, on the other hand, prioritises English as the primary medium of instruction because of its esteemed perception (a side-effect of colonial history) and association with economic and social mobility. 1 A standardised version of the Raven’s CPM (Ravens Educational CPM/CVS(India)) has been normed by Pearson Clinical India for Indian children (Raven, 2012 ; see Table 1 for an overview of the norm sample). Importantly, all children in the norm sample attended private schools, so children attending government schools were not included or represented in the norm population. In addition, 94.1% of the children’s parents had obtained a diploma/graduate degree or higher, suggesting that the norm population predominantly represented children from higher socio-economic standing (Ghosh & Dey, 2020 ). Finally, the linguistic background of the norm sample was underspecified and represents “the English speaking Indian population of children, attending English medium schools” (Raven, 2012 , p. 36). This description is rather vague and can be interpreted in many ways, including first language (L1) English speakers (who comprise less than 0.02% of the population; Census of India, 2011 ), second language (L2) English speakers (who comprise about 30% of the population and who may differ greatly in proficiency; Census of India, 2011 ), additional language English speakers (of a third, fourth etc. language), and speakers who use the Indian English variety (Sharma et al., 2017 ). Table 1 Comparison of the variables characterizing the sample used to create the Pearson India norms and our sample. Variables Pearson norm study Current study Sample size 338 1,752 Age 4–11;11 8–11;11 Gender 164 girls (48.5%), 174 boys (51.5%) 982 girls (56.1%), 770 boys (43.9%) Type of school Private Government Socio-economic status High; 94.1% of parents had a diploma/graduate degree or above Low; all children attended (free) government schools. Parents' occupations also suggest SES is not high Regions Bhopal, Delhi, Lucknow, Patna, Bengaluru, Chennai, Hyderabad, Kolkata, Ahmedabad, Mumbai, Guwahati Delhi, Hyderabad, Patna Official Medium of Instruction school English Regional languages (68.6%), English (21.6%), English and a regional language (9.8%) Languages spoken English speaking Indian population (other languages not specified) Children spoke a variety of mother tongues, including Hindi ( n = 749 monolingual speakers, n = 292 multilingual speakers with Hindi as their first language), Telugu ( n = 185 monolingual speakers, n = 277 multilingual speakers with Telugu as their first language), Bhojpuri ( n = 34), Lambadi ( n = 21), Kannada ( n = 13), and Rajasthani ( n = 10). Only 3 children reported speaking English as their first language. Language of instruction of test Not specified, but likely English (“Children were assessed only if they could read, write, and speak English”; Raven, 2012 , p. 39) Children instructed in the language they were most comfortable in, insofar as this was possible a a Non-English instructions, translated by the Research Assistants, were predominantly in Hindi (Delhi and Patna) and Tegulu (Hyderabad), and were limited by the languages the Research Assistants spoke. Since instructions for this test are relatively simple, we are confident that the translations did not affect the children’s understanding or performance on the test. Existing CPM India norms are therefore unrepresentative of a large proportion of Indian children, including those who attend government schools and who come from lower SES backgrounds. This was already argued by Tsimpli et al. ( 2020a ), who explicitly indicated that the standard scores from Raven ( 2012 ) may not be representative of their sample of low-SES children and therefore used raw (non-standardized) performance scores for their analyses. Following the recommendations from the International Test Commission ( 2015 ), “a test is obsolete when its underlying theory, item content, norms, or technical adequacy no longer meet the needs for its intended purpose, professional standards, or when its continued use would lead to inappropriate or inaccurate decisions or diagnoses” (p. 15). While we do not wish to discard the existing CPM test and Indian norms altogether, it is clear that they are not fit-for-purpose for Indian children attending government schools and are in fact obsolete as a reference for the majority of Indian children more generally. Nevertheless, the test itself still seems meaningful, for example because it showed a correlation of .27 with a different cognitive task, namely the n-back task, in a population of Indian children attending government schools (Tsimpli et al., 2020a ). Therefore, we will combine existing data from Tsimpli et al. ( 2020a ) a nd a follow-up study which both used the CPM with Indian children attending government schools to generate alternative CPM norms that more appropriately reflect the demographic background of this population. 2. Methodology 2.1 Sample For this study, data from two sources were combined. A large part of the dataset ( n = 1,581) is part of the Multilingualism and Multiliteracy in India (MultiLila) project (2016–2020; Tsimpli et al., 2019 ; 2020a ). Additional data from children in Hyderabad ( n = 171) are part of the follow-up project Supporting the development of Indian primary school children's reading comprehension skills: A SCaffolding-based Intervention (2021–2022; Vogelzang et al., 2024a ; henceforth referred to as the ASCI project) 2 . Our final sample consists of 1,752 children across four age categories (age 8, 9, 10, and 11; 982 girls, 770 boys, see Table 2 for an overview). All participants attended year 4 or 5 in government primary schools in India. The participating primary schools were located in Delhi, Hyderabad (highly urban), and Patna (more rural). These three cities were selected for the projects to reflect, at least to some extent, the geographical and cultural variation present in a country as large and diverse as India. Inclusion of schools in the projects was based on geographical region and type of school/SES. Concerning the latter, only government schools were recruited, which are free to attend and almost always have pupils from low SES backgrounds. This contrasts with the sample used in the original Raven's norms for India, which was based on private schools which are paid and typically have pupils from medium to high SES backgrounds (see Table 1 for a comparison between the two samples). The relatively low SES backgrounds of the children in our sample is confirmed when looking at their parents’ occupations. From the children for which information on this was accessible to us ( n = 872, 49.8%), for the overwhelming majority either one or both of the parents were laborers, with occupations such as cab or auto driver ( n = 124), some type of seller/vendor ( n = 103), mason or construction worker ( n = 86), painter ( n = 57), or general (day) laborer ( n = 46) for the fathers and maid or house help ( n = 137) or seamstress ( n = 22) for the mothers. These occupations formed the largest categories, but this is not an exhaustive list. Data from the ASCI project additionally show that 15% of children indicated that their father could not read or write and 27% indicated that this was true of their mother. In addition, 20% of children indicated that their father had not been to school (note that this does not refer to a diploma/degree, as in the existing Raven's norms, but to primary/secondary school), and 24% indicated this for their mother. Given all these factors, we are confident that these children represent a sample from relatively low SES backgrounds. Table 2 Overview of the number and percentage of children in our sample per age group and city. Age 8 Age 9 Age 10 Age 11 Total Delhi n 109 224 17 3 353 Girls 60 (55%) 109 (49%) 9 (53%) 0 (0%) Boys 49 (45%) 115 (51%) 8 (47%) 3 (100%) Hyderabad n 84 185 202 130 601 Girls 45 (54%) 112 (61%) 114 (56%) 66 (51%) Boys 39 (46%) 73 (39%) 88 (44%) 64 (49%) Patna n 82 261 309 146 798 Girls 45 (55%) 160 (61%) 180 (58%) 82 (56%) Boys 37 (45%) 101 (39%) 129 (42%) 64 (44%) Total 275 670 528 279 1,752 2.2 Procedure The Raven's CPM test (Raven et al., 2008) consists of 36 items (matrices). In each item children were presented with a pattern from which one piece is missing (reminiscent of a puzzle). They are asked to pick one of six possible 'puzzle' pieces (answer alternatives) to complete the pattern – only one answer is correct, the other five are incorrect. The 36 matrices are divided equally into three sets (A, AB, and B) and items become progressively more difficult. Each correct answer gains the child 1 point, with the maximum possible score on this test being 36. The matrices and answer alternatives were presented on a laptop, and children could indicate their choice by saying the number of the answer alternative or by pointing to it. Besides the explanation of the test, it is thus, or at least it has the potential to be, a non-verbal test. The tests were administered by research assistants from India who were proficient in local languages as well as English. The children were tested individually in a separate room or courtyard in their school. The test explanation was provided in the language they were most comfortable in, insofar as that was possible given the many languages spoken by the children. Demographic and language background information was obtained through an interview with the children, also in the language they were most comfortable with insofar as it was possible 3 . 2.3 Ethical considerations Ethical consent was collected as part of the two projects that the samples were taken from. Consent was given by the child (orally), as well as by the head teacher or principal of the participating schools. Many of the parents of lower-SES children in India are semi-literate or illiterate and their communication with the school is scarce. Parental consent was therefore ensured through the school principal and the children's teachers. The principal approached parents (mostly by phone) to ask them for consent. The studies were conducted in accordance with the Declaration of Helsinki, the ESRC’s Framework for Research Ethics (ESRC, 2010 ), and the guidelines of the Indian Council for Medical Research (ICMR, 2006 ). The protocol of the MultiLiLa project was approved by the Ethics Committees of the University of Cambridge (RG83665), the Jawaharlal Nehru University, and the National Institute of Mental Health and Neurosciences. The protocol of the ASCI project was approved by the Ethics Committee of the University of Cambridge (2019-20/65). The project additionally obtained permission to approach schools from the State Minister of Education in Telangana (the state in which Hyderabad is located). 2.4 Analysis The data from the two projects was prefiltered based on (1) availability of the Raven's CPM, (2) the age range of the children (i.e., children under 8 [ n = 14 of age 7] were excluded because of insufficient data, children over 11 were excluded because they were overaged for their grade level [ n = 159]), and (3) the type of school (the ASCI project included three low-cost private schools which were not included in the current sample). The new norms and all plots presented in this paper were created in the R software (version 4.3.1, R Core Team, 2019). We used the package stenR to calculate the new norms (Kosinski, 2022 ), where we defined a normal distribution with a range of 55 to 145, a mean of 100, and a standard deviation of 15. New norms were calculated for each age group (age 8, 9, 10, and 11). In order to do this, we first created frequency tables and then scoring tables as described in https://cran.r-project.org/web/packages/stenR/vignettes/usage.html (using the functions GroupedFrequencyTable and GroupedScoreTable and their default parameters). Norms were calculated for each integer on the normal distribution (from 55 to 145) but are presented in the manuscript more concisely (as in the Pearson's versions of Raven's norms) in increments/bins of 5. In doing so, numbers were rounded, for example, scores for 98 to 102 became bin 100. All plots were created with base-R. For the plots of the standard scores, standardized scores of 140 were coded as 145. We then calculated the means, ranges, and standard deviations of the Raven's scores for the sample. Skewness and Kurtosis values were calculated with the moments package (Komsta & Novomestky, 2022 ). As a rule of thumb, if the skewness is between − 0.5 and 0.5, the data are fairly symmetrical. If the skewness is between − 1 and − 0.5 or between 0.5 and 1, the data are moderately skewed. If the skewness is less than − 1 or greater than 1, the data are highly skewed (Bulmer, 1979 ). For kurtosis, the value reflecting an optimal normal distribution is 3. The greater the kurtosis, the higher the peak. We estimated the reliability of the Raven's CPM test for our sample of participants by examining the test-retest correlation of a subset of 629 children using Spearman's correlation. We additionally implemented a version of the split-halves reliability paradigm: We split the dataset of children for each age category in half and compared whether their means differed significantly with an independent samples (unpaired) t -test. To verify whether performance improved with age, we ran a linear regression with raw score as the dependent variable and age as the independent variable. Finally, we ran independent samples (unpaired) t -tests to compare the performance of girls and boys in each age group. The full dataset and analysis code for this paper can be found on the OSF [ https://osf.io/4tm3u/?view_only=2deb2e2bdcc54b4a93a019d57476cc06 ]. 3. Results We first discuss the current dataset and why the existing norms do not apply to it. We then present new norms for children attending government Indian schools. Finally, we present some measures of reliability and construct validity of the Raven's CPM for this sample of children. 3.1 The current dataset and existing norms Some core descriptives of the current dataset, including children’s raw scores on the CPM, per age category are presented in Table 3 . The mean raw scores on the test tend to increase with age, as expected. The skewness and kurtosis of the raw scores are within the acceptable range of normality. The raw scores indeed visually resemble a normal distribution (see Fig. 1 ). Table 3 Descriptive statistics of our sample ( N = 1,752) in the Raven's CPM test, including measures of skewness and kurtosis. Raw scores Standardized scores Age n Mean Range SD Skewness Kurtosis Skewness Kurtosis 8 275 17.19 5–35 5.87 .47 2.98 .68 2.87 9 670 17.89 2–35 6.06 .32 2.59 .76 2.86 10 528 17.82 4–35 6.07 .31 2.57 1.09 3.75 11 279 19.28 5–34 6.80 .10 2.18 .92 2.84 If the existing norms would be appropriate for this sample of children, the normed standard scores should also resemble a normal distribution. However, this is not the case (see Fig. 2 ). In fact, 1,375 of the children (78.5%) scored in the bottom 10th percentile, making the distribution heavily skewed and flagging the majority of children as potentially having intellectual challenges, with a standardized (IQ) score of 80 or lower (e.g., Wieland & Zitman, 2016 ). Measures of skewness (Table 3 ) confirm that the standardized scores are moderately to highly right-skewed. Thus, the existing norms are too stringent for this sample of children and leads to potentially meaningful distinctions between children's scores being lost when interpreted by reference to normed data. For example, children of age 11 who score 16 or below are all assigned the standard score < 60, with a percentile rank of 0.1, even though a child scoring 16/36 objectively performed better than a child scoring 10/36. 3.2 New norms for children attending government schools in India Based on the children’s raw scores, and the assumption that scores should be normally distributed, new norms were calculated for these 8-to-11-year-old children (see Table 4 ). When using these new norms to calculate standard scores, the scores approach a normal distribution (compare Fig. 2 to Fig. 3 ). For completeness, Fig. 4 shows the distributions of the standard scores for the four different age groups. Note, however, that there isn’t always a logical progression in performance with age in these norms, as they are data-driven and data is limited at the far ends of the distributions. For example, 8-year-old children with a score of 4 would receive a standard score of 60 whereas 9-year-old children with a score of 4 would receive a standard score of < 60. For that reason, we provide manually modified norms based on Table 4 that follow a logical age progression in the Supplementary Material (Table S1 ). Users might choose to use Table S1 instead of the purely data-driven Table 4 to standardize their data. Table 4 New Raven’s CPM norms table for 8-to-11-year-old children attending government schools in India Standard score Percentile rank Age 8 Age 9 Age 10 Age 11 < 60 0.1 - 0–4 0–4 - 60 0.4 0–5 5 5 0–5 65 1 - 6–7 6 6 70 2.3 6–8 8 7 7 75 5 9 9 8–9 8–9 80 9 10 10–11 10 10–11 85 16 11 12 11–12 12 90 25 12–13 13–14 13–14 13–14 95 37 14–15 15–16 15–16 15–17 100 50 16–17 17–18 17–18 18–20 105 63 18–20 19–20 19–20 21–23 110 75 21 21–23 21–23 24–25 115 84 22–24 24–25 24–25 26–27 120 91 25–26 26–27 26–27 28–30 125 95 27–28 28–30 28–29 31 130 97.7 29–33 31 30–31 32 135 99 34 32 32–33 33 140 99.6 - 33–34 34 34–36 > 140 99.9 35–36 35–36 35–36 - We additionally examined the reliability and construct validity of the Raven's CPM in our sample of children. Note that these investigations were performed on the children’s raw scores (out of 36) and are thus not a validation of the norms, but rather of the Raven's CPM test itself. 3.3 Reliability We analyzed the external reliability of the Raven's CPM in two ways. The first was through a test-retest paradigm. The Raven's CPM test was re-administered to a subsample of 629 children (338 from Delhi, 291 from Hyderabad; 334 girls, 295 boys) after approximately one year. The correlation coefficient between the two administrations was .52 (CI = [.46 − .57]). This is a little lower than was found for other studies (e.g., Kazem et al., 2009 ), but the time period in between the two tests was much larger in the current study. However, it is also lower than the previously reported one-year test-retest reliability (.71, Court & Raven, 1995 , as reported in Kazlauskaite & Lynn, 2002 ), and more in line with previously reported two-year test-retest reliability (of .499 in Lithuanian children, Kazlauskaite & Lynn, 2002 ). For a further investigation of reliability, we implemented a version of the split-halves reliability paradigm. That is, we split the dataset of children for each age category in half randomly and compared whether their means differed significantly. The results indicate that this was not the case for any of the age groups (see Table 5 ). Table 5 T-test results comparing the split-half groups on Raven’s CPM in the four different age categories. Age n 1st half Mean ( SD ) 2nd half Mean ( SD ) t df p 8 275 17.04 (5.51) 17.33 (6.22) − .41 270 .683 9 670 17.71 (5.83) 18.06 (6.28) − .75 664 .456 10 528 17.76 (5.83) 17.89 (6.32) − .25 523 .802 11 279 18.92 (6.29) 19.63 (7.28) − .87 272 .385 3.4 Construct validity We assessed the construct validity of the Raven's CPM for our sample by assessing whether performance improves with age and whether performance differs between boys and girls. A linear regression confirmed that raw scores significantly increased with age ( ß = .56; t = 3.55; p < .001) as expected. We then assessed the influence of gender in each age category. T-tests showed no difference in performance between girls and boys at age 8, but differences in performance between girls and boys at age 9, 10, and 11 were found, as well as when all ages were collapsed, with boys outperforming girls (see Table 6 ). Table 6 Comparison between performance on the Raven's CPM by boys and girls across the four age categories. Age Girls: Mean (SD) Boys: Mean (SD) t df p 8 16.95 (5.75) 17.47 (6.01) − .73 260 .468 9 17.09 (5.86) 18.93 (6.16) -3.92 603 < .001 *** 10 17.09 (5.95) 18.82 (6.10) -3.26 476 .001 ** 11 18.48 (6.35) 20.18 (7.19) -2.08 261 .039 * all 17.28 (5.96) 18.87 (6.35) -5.37 1600 < .001 *** * p < .05; ** p < .01; *** p < .001 4. Discussion This study challenged the existing Raven’s CPM norms for India (Raven, 2012 ) based on a sample of 8-to-11-year-old Indian children who attend government primary schools. We argued that the existing norms were developed based on children with higher SES attending private schools, whereas roughly two-thirds of school-going children in India attend government schools and are from lower socio-economic backgrounds (Government of India, 2021 -22). According to the existing norms, the vast majority of children from our sample substantially underperformed compared to the sample on which the existing norms were based (cf. Al-Shahomee & Lynn, 2010 ; Anum, 2014 ; Costenbader & Ngari, 2001 ; Knoetze et al., 2005 ). This interpretation is harmful and does not adequately identify children who are in fact intellectually at risk and who are performing in line with or better than their peers. We presented new norms (Table 4 ), which are more appropriate for our sample and potentially for other Indian children between 8–11 years old who attend government primary schools as well. We strongly encourage researchers who use CPM standardized scores to think about which norms are most appropriate for their datasets and for educators and clinicians to use the norms most appropriate on a case-by-case basis. Furthermore, we presented evidence that the Raven’s CPM test in our population is reasonably reliable based on test-retest and a split halves paradigms. We are thus confident that the test can be a useful tool for testing Indian children. Importantly, although we provided an overview of several issues with the Raven’s tests for diverse populations in the introduction, we are not advocating to abandon it altogether in non-W.E.I.R.D contexts given that it still provides some useful information about visuo-spatial reasoning. Rather, we are (1) providing updated norms that more appropriately reflect a large proportion of Indian children, and (2) cautioning researchers, educators, and clinicians to carefully consider what the outcomes of a Raven’s test in their participants may signify, ensuring they account for the broader (sociocultural) context and possible influences. Importantly, we support the view that tasks such as the Raven’s can give insights into intelligence test scores , which are separate from intelligence itself (Van de Vijver & Leung, 1997 ). However, to be more specific, and to avoid any potential misunderstanding, it is more accurate to regard the Raven’s CPM as assessing visuo-spatial reasoning by means of puzzle manipulation rather than intelligence ( g ) more broadly (Gonthier, 2022 ). Regarding construct validity, we found differences between boys and girls. This result is in contrast with previous research which has generally found invariance of performance across genders on Raven’s CPM (e.g., Kazem et al., 2009 ). In fact, the sample of children used in the original norms for India showed a non-significant difference in performance between boys and girls (collapsed over age groups, p = .44; Raven, 2012 ). However, the result is in line with Hervé et al. ( 2022 ), who found a similar gender gap in cognitive (and noncognitive) outcomes, including Raven’s, in Indian adolescents. They suggest that this is due to “an institutionalized gender bias in education against girls in India” (p. 85). Although we cannot know the cause of this for certain, we can speculate about why boys outperformed girls in the sample presented in the current study. Specifically, this might be a direct result of the culture within lower socio-economic groups in India, with education being more challenging for girls in terms of regular school attendance and continuity. However, we have no direct evidence for this in the current sample, and there were more girls ( n = 982) than boys ( n = 770) attending the schools from which our data were collected. Girls in India, especially from lower SES backgrounds, have been known to outperform boys in language subjects (Natta et al., 2017 ; Shenoy et al., 2023 ; UNICEF, 2012 ; Vogelzang et al., 2024b ). Interestingly, in previous research using a subsample of our study (Tsimpli et al., 2020a ), gender was not found to influence performance on an n-back cognitive task or Raven's CPM. So, the observed effect of gender needs further investigation to be substantiated. Note that the new norms presented in this paper are not all-encompassing either. Specifically, they may lead to potential skewness in the opposite direction when used on a sample of higher-SES children and/or those attending private schools or residing in different regions across India, causing researchers, educators, or clinicians to miss children who are indeed in need of intervention. A truly representative norm would be one that includes all children across socio-economic and geographic strata in India. Regardless, the norm scores we have provided are likely closer to that of the total child population than the original norm scores, as the majority of Indian children attend government schools (Government of India, 2021 -22). 4.1 Limitations We acknowledge that our sample, and therefore our analysis, has some limitations. Most notable, the participating children were from three different cities/regions, which leads to limited generalizability across a country as large and diverse as India. In addition, our sample only spanned between the ages of 8 and 11, in contrast with the original Indian norms, which started from the age of 4. Moreover, we had a distinct focus on children attending government schools, which has the advantage of being able to provide norms for this specific sub-population, but limits generalizability to other sub-samples of children. Nevertheless, we were able to present new norms based on a large sample size and valid representation of a subsample of the population. A second limitation of this research lies in the datasets. With respect to the demographic data, much information about children's backgrounds, parental occupations and parental education was incomplete. In addition, in contrast with many studies based in Western countries, the information about the children's backgrounds was not based on parental reports, but on children’s self-reports. In some cases, this made the information unreliable or less accurate (e.g., some children did not know their parents' profession, and some children reported speaking English whereas this was likely only reported because the child thought that this was a socially desirable response). No other data was available in the examined datasets, but future work might try to obtain parental reports as well. Note, however, that this may be challenging and labor-intensive in the instances in which parents cannot read or write. With respect to the Raven’s data, the datasets did not contain item-level data, so that analyses such as item difficulty or internal consistency could not be conducted (though note that these were not conducted in Raven, 2012 either; cf. Cotton et al., 2005 ). Finally, we reported the official medium of instruction of the schools that the children attended, but from previous research we know that many different languages are used in the classroom in government schools in India (Lightfoot et al., 2021 ). 4.2 Conclusion In conclusion, this study challenges the existing norms for the Raven’s CPM in India. We provide new norms, which better reflect government school children’s educational and socio-demographic backgrounds and are more suitable to use with similar children than existing norms. It is not necessary to replace the existing Raven’s CPM norms completely, but these new norms can be used by researchers, educators and clinicians where appropriate. Declarations Competing interests The authors declared no potential competing interest with respect to the research, authorship, and/or publication of this article. Compliance with Ethical Standards Ethical consent was collected as part of the two projects that the samples were taken from. The study was conducted in accordance with the Declaration of Helsinki, the ESRC’s Framework for Research Ethics (ESRC, 2010 ), and the guidelines of the Indian Council for Medical Research (ICMR, 2006 ). The protocol of the MultiLiLa project was approved by the Ethics Committees of the University of Cambridge (RG83665), the Jawaharlal Nehru University, and the National Institute of Mental Health and Neurosciences. The protocol of the ASCI project was approved by the Ethics Committee of the University of Cambridge (2019-20/65). The project additionally obtained permission to approach schools from the State Minister of Education in Telangana (the state in which Hyderabad is located). Funding For this study, data from two sources were combined. The MultiLila project (PI: Ianthi Tsimpli) was funded by the Department for International Development and the Economic and Social Research Council (ESRC-DFID, Grant Ref: ES/N010345/1). The project Supporting the development of Indian primary school children's reading comprehension skills: A SCaffolding-based intervention (ASCI) (PI: Ianthi Tsimpli) was funded by the British Academy (Grant Ref: TGC\200109). Author Contribution Conceptualization: M.V., M.W., and I.M.T. Formal analysis and investigation: M.V. Writing - original draft preparation: M.V. and M.W. Writing - review and editing: M.V., M.W., and I.M.T. Funding acquisition: I.M.T. All authors read and approved the final manuscript. Acknowledgement Acknowledgements We would like to thank all the research assistants, children and teachers for their participation and support. We are very grateful to Suvarna Alladi, Anusha Balasubramanian, Debanjan Chakrabarti, Ganesh Devy, Dhir Jhingran, Vasanta Duggirala, Amy Lightfoot, Theodoros Marinis, Rama Matthew, Ajit Mohanty, Lina Mukhopadhyay, Minati Panda, Bapi Raju, Abhigna Reddy, Pallawi Sinha, and Jeanine Treffers-Daller for their advice and support throughout the MultiLiLa project. We would like to thank Victoria Murphy and Lina Mukhopadhyay for their advice and support throughout the ASCI project. Data Availability The datasets analyzed during the current study are available in the Open Science Framework repository, along with all analysis code:[https://osf.io/4tm3u/?view_only=2deb2e2bdcc54b4a93a019d57476cc06](https:/osf.io/4tm3u/?view_only=2deb2e2bdcc54b4a93a019d57476cc06) References Al-Shahomee, A. A., & Lynn, R. (2010). Norms and sex differences for the standard progressive matrices in Libya. Mankind Quarterly , 51 , 97–107. http://doi.org/10.46469/mq.2010.51.1.7 Anum, A. (2014). 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Footnotes Such English idealism in low-cost private and government school contexts does not always translate to ‘pure’ English use in practice though (Lightfoot et al., 2022; Treffers-Daller et al., 2022 ; Tsimpli et al., 2020a ; 2020b ; Tsimpli & Balasubramanian, 2024 ). Nevertheless, the goal of private education is to implement English instruction, often without taking learners' diverse linguistic backgrounds and English exposure into account. One might be concerned about the temporal gap between the two datasets and the potential influence of COVID-19 on children’s schooling and cognitive performance. We therefore compared the ASCI dataset to age-matched data from Hyderabad from the MultiLila project. The children in the ASCI dataset scored higher (mean raw score: 23.7) than their MultiLila peers (mean raw score: 20.5; paired t-test: t = 5.47; df = 409; p < .001) on the CPM, not lower as might be expected after COVID-19. This performance difference may thus simply reflect naturally varying performance levels across regions, neighborhoods, schools, and individual children. Further investigation into this effect is beyond the scope of the current study. Where it was not possible for the research assistant to speak the language most comfortable to the child, they found a language in which both could understand each other and used that to communicate. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8902845","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593519555,"identity":"7a12f567-e223-4b7e-84a6-7fb799f23c39","order_by":0,"name":"Margreet Vogelzang","email":"data:image/png;base64,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","orcid":"","institution":"University of Cambridge","correspondingAuthor":true,"prefix":"","firstName":"Margreet","middleName":"","lastName":"Vogelzang","suffix":""},{"id":593519560,"identity":"7babab7a-8350-4a76-9e46-ebab49f921ca","order_by":1,"name":"Mandy Wigdorowitz","email":"","orcid":"","institution":"University of Alabama","correspondingAuthor":false,"prefix":"","firstName":"Mandy","middleName":"","lastName":"Wigdorowitz","suffix":""},{"id":593519568,"identity":"edb396f7-fafc-4ee4-b78d-bd27394ecb08","order_by":2,"name":"Ianthi Maria Tsimpli","email":"","orcid":"","institution":"University of Cambridge","correspondingAuthor":false,"prefix":"","firstName":"Ianthi","middleName":"Maria","lastName":"Tsimpli","suffix":""}],"badges":[],"createdAt":"2026-02-17 16:23:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8902845/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8902845/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103178131,"identity":"acd39d0f-c7a8-4309-8987-c503147925ff","added_by":"auto","created_at":"2026-02-22 16:59:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104493,"visible":true,"origin":"","legend":"\u003cp\u003eHistogram (A) and density plot (B) of the children's raw scores on the Raven's CPM test. The density plot includes estimates of the normality of the distribution, with the true distribution shown in blue and the idealized distribution based on the mean and standard deviation of the data shown in dark red\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8902845/v1/ef93e59381665e440f05cb1f.png"},{"id":103505039,"identity":"2867ef49-5008-46c5-9268-5d1f255c6e47","added_by":"auto","created_at":"2026-02-26 13:22:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":90101,"visible":true,"origin":"","legend":"\u003cp\u003eHistogram (A) and density plot (B) of the children's standard scores for all ages together according to the existing Raven's CPM norms for Indian children. The density plot includes estimates of the normality of the distribution, with the true distribution shown in blue and the idealized distribution based on the mean and standard deviation of the data shown in dark red\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8902845/v1/29a9877036121ff0b70decda.png"},{"id":103504516,"identity":"1c6b7dd6-403f-4302-a20e-41f4f5814a66","added_by":"auto","created_at":"2026-02-26 13:20:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":119757,"visible":true,"origin":"","legend":"\u003cp\u003eHistogram (A) and density plot (B) of the children's standard scores for all ages together after using the new Raven's CPM norms for Indian children. The density plot includes estimates of the normality of the distribution, with the true distribution shown in blue and the idealized distribution based on the mean and standard deviation of the data shown in dark red\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8902845/v1/0cbf6f5803effe35463bd61e.png"},{"id":103178133,"identity":"010851ce-6d72-4a86-a886-10b35bcfe5b7","added_by":"auto","created_at":"2026-02-22 16:59:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":135455,"visible":true,"origin":"","legend":"\u003cp\u003eHistograms of the children's standard scores at each age (8, 9, 10, 11) after using the new Raven's CPM norms for Indian children\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8902845/v1/2d5a6135fed15279dc74c874.png"},{"id":104397255,"identity":"0700bd94-5594-4907-9d48-30f6a35d90b8","added_by":"auto","created_at":"2026-03-11 11:45:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1598175,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8902845/v1/8361f4b7-eee8-4c8c-b079-976e916042f9.pdf"},{"id":103504863,"identity":"01062d95-3d42-4bc8-bb58-fd98cb61d9ce","added_by":"auto","created_at":"2026-02-26 13:21:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":91902,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8902845/v1/1ad1534c440d57b73e293a59.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Challenging existing Raven's norms based on children attending government schools in India","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHaving norms for measures of intelligence (\u003cem\u003eg\u003c/em\u003e) and other psychological constructs serves as a reliable and valid way to fairly compare, position, and identify potentially vulnerable individuals against a representative sample. Norm scores provide a standard against which individual test scores can be interpreted, making it possible to determine how an individual's score fares against that of a similar group and, in turn, provides a marker of performance that can be used for educational or clinical intervention. Comparison of a sample against the respective norm group should follow a Gaussian distribution whereby the majority of the sample scores within an average range and a smaller proportion either excels or scores poorly. Norm scores capture this normal variation and can be used to appropriately classify the performance of individuals against which the norm sample claims to generalize. But what happens in cases where reference to norm scores disproportionately renders a large proportion of children as poor or vulnerable performers? Does such an interpretation suggest that a large group of children which are compared against the norms have intellectual challenges or risks, or are the norms themselves perhaps unrepresentative of the population they claim to represent? In this paper, we take the latter stance by (1) providing evidence showing that a group of Indian children attending government schools fare markedly poorer than expected against the Indian Raven\u0026rsquo;s norm scores, and in response, (2) creating more representative Raven's norms for this population of Indian children. We aim to improve the suitability of Raven's Coloured Progressive Matrices (CPM) for Indian government primary school pupils by establishing norms that are more reflective of their demographic, geographic, and educational backgrounds. Below we provide an overview of what the Raven\u0026rsquo;s Progressive Matrices measure, including how and for whom they have been used. We then explore the cultural factors that influence performance and examine the role of socio-demographic indicators. Additionally, we discuss the broader educational and socio-economic context for children in India, particularly those attending government schools, and highlight the issues with the current CPM norm scores.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Cultural and socio-economic status on Raven's performance\u003c/h2\u003e \u003cp\u003eThe Raven\u0026rsquo;s Progressive Matrices (RPM) are measures of fluid intelligence, designed to capture (non-verbal) visuo-spatial reasoning without relying on test-takers\u0026rsquo; prior knowledge, education, or experience. RPM does not rely on linguistic ability but rather uses visual material that requires the selection of a correct image from an array of options that adheres to a pattern. There are three standardised versions of the RPM, namely (1) the Standard Progressive Matrices (SPM) which is suitable for the general population for individuals aged 6 and over who have average intelligence, (2) the Advanced Progressive Matrices (APM) which is a more difficult version of the test and is suitable for people with above-average intelligence aged 12 and older, and (3) the Coloured Progressive Matrices (CPM) which is designed for children between the ages of 4 to 11 and can also be used to assess visuo-spatial reasoning of elderly individuals and people with learning difficulties (Raven, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Each version has been designed as fit-for-purpose and appropriate for particular populations, ensuring that non-verbal intelligence can be measured for a wide range of age groups and ability levels. Scores positively increase with age, and it has been observed that people are obtaining higher intelligence test scores over time with an average increase of three points per decade (known as the \u0026lsquo;Flynn Effect\u0026rsquo;, Flynn, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince its development by John C. Raven in 1936 (Raven, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1936\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1941\u003c/span\u003e), all versions of the RPM have been widely employed and normed across various global contexts for experimental, educational and clinical means to ensure its ecological validity (Brouwers et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Cotton et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Raven, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Because of its non-verbal design and easy implementation, the RPM is thought to be applicable cross-culturally for children and adults and not confounded by cultural or national differences such as level of education, socio-economic status, and other exogenous factors (e.g., Rushton et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, Valencia, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). However, it has been argued that the RPM cannot be culturally neutral because intelligence itself and its assessments are intrinsically cultural constructs (Frijda \u0026amp; Jahoda, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1966\u003c/span\u003e). Such cultural loading \u0026ndash; the extent to which a test or test item reflects the values, language, information, and experiences of a specific cultural context \u0026ndash; inherently plays a role in performance (Jensen, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). In fact, some studies suggest that visuo-spatial tests, like the RPM, are influenced by culture to a greater extent than verbal tests (Ardila \u0026amp; Moreno \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Rosselli \u0026amp; Ardila, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and are significantly affected by training (Jaeggi et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), repeated testing (Ombr\u0026eacute;dane et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1956\u003c/span\u003e; Wing, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) and bilingualism (Bialystok \u0026amp; Shapero, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Tsimpli et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). To address performance disparities and such method biases, a distinction has been drawn between the construct of \u003cem\u003eintelligence\u003c/em\u003e itself and \u003cem\u003eintelligence test scores\u003c/em\u003e, whereby competence can be seen as separate from performance (Van de Vijver \u0026amp; Leung, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn response to ensuring culturally responsive testing practices, there is ample research investigating the psychometric soundness and applicability of the RPM across various populations and cultural contexts. However, its development, adaptation, and the majority of norming, educational, and clinical studies come from W.E.I.R.D. (Western, Educated, Industrialised, Rich and Democratic, Henrich et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) samples and contexts, predominantly in the Global North (Brouwers et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Raven \u0026amp; Raven, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). In cases where the RPM is evaluated in countries or cultures that are not considered W.E.I.R.D., there are notable concerns questioning its cultural neutrality, fairness, and appropriateness (for a review, see Gonthier, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This is largely because test-takers regularly perform suboptimally in comparison to norm reference groups or their W.E.I.R.D. test-taker counterparts. For example, in many African countries, RPM scores are considerably lower than expected, with some even falling below 1 standard deviation of the mean (see for example, South Africa: Knoetze et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Libya: Al-Shahomee \u0026amp; Lynn, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ghana: Anum, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kenya: Costenbader \u0026amp; Ngari, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Furthermore, some properties of the RPM do not hold up to psychometric standards in non-W.E.I.R.D. contexts, as illustrated in a review of the RPM in African samples (Wicherts et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Cultural confounds have also been found to contribute to different scores for different ethnic and gender groups which have led to unsubstantiated and damaging conclusions about gender/race and intelligence in the past (see Lynn \u0026amp; Vanhanen, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Raven \u0026amp; Raven, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Wicherts et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, gender differences are not always observed (Kazem et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Raven, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn no way is the poorer performance of non-W.E.I.R.D. samples on the RPM indicative of innate difficulties in visuo-spatial reasoning; rather, it reflects biases and the cultural appropriateness of the test itself. Non-verbal intelligence tests rely on the manipulation of some type of visual material (e.g., puzzles), but if such material or its perceptual manipulations differ, are limited, or absent in home, pre-school, or school contexts, then it is no surprise that test-takers would be disadvantaged if they are required to transfer specific skills which have not been honed. Simply engaging with the RPM requires prior experience with visuo-spatial processes, gestalt reasoning and an understanding of test taking in general. From knowing the names and representations of varying geometric shapes to recognising numerical cues and more complex symbolic representations, a myriad of reference points are needed to decipher Raven\u0026rsquo;s items (Ardila \u0026amp; Moreno, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Pontius, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). In addition, there are possible language barriers:although the test itself is considered non-verbal, language is essential for relaying the test instructions. Collectively, these processes rely on previous experience and engagement with a specific mode of representation such that greater experience translates to greater ease of item manipulation. Being at least critical and cautious about a blanket assumption of cultural fairness in intelligence testing is therefore warranted when evaluating scores either in their raw form or against a norm.\u003c/p\u003e \u003cp\u003eWhile re-standardization and validation of assessments in local populations can reduce cultural biases and result in the development of local norms, such measures do not completely remove the effects of exogenous factors on test scores. One of the most significant predictors of RPM performance are socio-demographic indicators, such as socio-economic status (SES). SES, while measured in different ways, provides an estimate of social affluence and serves as a proxy for one's access to resources and opportunities within a society. SES has been widely studied and found to be positively related to language development, educational attainment, health outcomes and many other population-level variables (e.g., Brito \u0026amp; Noble, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Broer et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Importantly, SES has been linked to fluid intelligence performance, with individuals in lower SES groups underperforming those in higher SES groups (for a meta-analysis, see Peng et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In a study investigating the impact of SES on fluid intelligence in Ghanaian children (6\u0026ndash;12 years old), Anum (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found a disparity in performance on the CMP where children attending private schools (a proxy for higher SES) scored higher than those attending public schools (a proxy for lower SES). These findings demonstrate the strong influence that SES can have on the RPM performance in children, particularly in developing countries, and raises an important question about whether single and blanket norm scores can adequately capture visuo-spatial reasoning of children where disparities in SES are pronounced and evident in terms of educational access and equity. Such a distinction also exists in India, where schooling quality is largely determined by economic resources. Culturally appropriate norms for diverse groups of people in the context of India are therefore needed. In the next section, we review the educational system in India and discuss the development and application of the Indian CPM along with its limitations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Education and Raven\u0026rsquo;s in India: insights and issues\u003c/h2\u003e \u003cp\u003eIndia is the world\u0026rsquo;s most populous country with an estimated population of nearly 1.5\u0026nbsp;billion people, of which approximately 25% (370\u0026nbsp;million) are children between the ages of 0\u0026ndash;14 (The World Bank Group, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Roughly two-thirds of school-going children in India attend government schools, with the remaining third attending private schools (Government of India, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e-22). India is a highly multilingual and diverse country and is home to over 424 indigenous languages, of which 22 are considered \u0026lsquo;scheduled\u0026rsquo; and used for official purposes (e.g., Telugu, Kannada) with English serving as a link language alongside Hindi (Census of India, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Eberhard et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It also has one of the highest linguistic diversity indexes of 0.914 as reported by UNESCO (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; range 0\u0026ndash;1). About 27 languages, including English, are used as mediums of instruction across government schools depending on the location and local language(s) of the region (Eberhard et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mohanty, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Private education, on the other hand, prioritises English as the primary medium of instruction because of its esteemed perception (a side-effect of colonial history) and association with economic and social mobility.\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eA standardised version of the Raven\u0026rsquo;s CPM (Ravens Educational CPM/CVS(India)) has been normed by Pearson Clinical India for Indian children (Raven, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for an overview of the norm sample). Importantly, all children in the norm sample attended private schools, so children attending government schools were not included or represented in the norm population. In addition, 94.1% of the children\u0026rsquo;s parents had obtained a diploma/graduate degree or higher, suggesting that the norm population predominantly represented children from higher socio-economic standing (Ghosh \u0026amp; Dey, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Finally, the linguistic background of the norm sample was underspecified and represents \u0026ldquo;the English speaking Indian population of children, attending English medium schools\u0026rdquo; (Raven, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, p. 36). This description is rather vague and can be interpreted in many ways, including first language (L1) English speakers (who comprise less than 0.02% of the population; Census of India, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), second language (L2) English speakers (who comprise about 30% of the population and who may differ greatly in proficiency; Census of India, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), additional language English speakers (of a third, fourth etc. language), and speakers who use the Indian English variety (Sharma et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the variables characterizing the sample used to create the Pearson India norms and our sample.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson norm study\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCurrent study\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSample size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u0026ndash;11;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u0026ndash;11;11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164 girls (48.5%), 174 boys (51.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e982 girls (56.1%), 770 boys (43.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of school\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGovernment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocio-economic status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh; 94.1% of parents had a diploma/graduate degree or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow; all children attended (free) government schools. Parents' occupations also suggest SES is not high\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBhopal, Delhi, Lucknow, Patna, Bengaluru, Chennai, Hyderabad, Kolkata, Ahmedabad, Mumbai, Guwahati\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDelhi, Hyderabad, Patna\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOfficial Medium of Instruction school\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegional languages (68.6%), English (21.6%), English and a regional language (9.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLanguages spoken\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnglish speaking Indian population (other languages not specified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChildren spoke a variety of mother tongues, including Hindi (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;749 monolingual speakers, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;292 multilingual speakers with Hindi as their first language), Telugu (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;185 monolingual speakers, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;277 multilingual speakers with Telugu as their first language), Bhojpuri (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34), Lambadi (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21), Kannada (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13), and Rajasthani (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10). Only 3 children reported speaking English as their first language.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLanguage of instruction of test\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot specified, but likely English (\u0026ldquo;Children were assessed only if they could read, write, and speak English\u0026rdquo;; Raven, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, p. 39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChildren instructed in the language they were most comfortable in, insofar as this was possible \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e \u003cem\u003ea\u003c/em\u003e \u003c/sup\u003e \u003cem\u003eNon-English instructions, translated by the Research Assistants, were predominantly in Hindi (Delhi and Patna) and Tegulu (Hyderabad), and were limited by the languages the Research Assistants spoke. Since instructions for this test are relatively simple, we are confident that the translations did not affect the children\u0026rsquo;s understanding or performance on the test.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eExisting CPM India norms are therefore unrepresentative of a large proportion of Indian children, including those who attend government schools and who come from lower SES backgrounds. This was already argued by Tsimpli et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e), who explicitly indicated that the standard scores from Raven (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) may not be representative of their sample of low-SES children and therefore used raw (non-standardized) performance scores for their analyses.\u003c/p\u003e \u003cp\u003eFollowing the recommendations from the International Test Commission (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), \u0026ldquo;a test is obsolete when its underlying theory, item content, norms, or technical adequacy no longer meet the needs for its intended purpose, professional standards, or when its continued use would lead to inappropriate or inaccurate decisions or diagnoses\u0026rdquo; (p. 15). While we do not wish to discard the existing CPM test and Indian norms altogether, it is clear that they are not fit-for-purpose for Indian children attending government schools and are in fact obsolete as a reference for the majority of Indian children more generally. Nevertheless, the test itself still seems meaningful, for example because it showed a correlation of .27 with a different cognitive task, namely the n-back task, in a population of Indian children attending government schools (Tsimpli et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). Therefore, we will combine existing data from Tsimpli et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e) \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003ea\u003c/span\u003end \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003ea\u003c/span\u003e follow-up study which both used the CPM with Indian children attending government schools to generate alternative CPM norms that more appropriately reflect the demographic background of this population.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sample\u003c/h2\u003e \u003cp\u003eFor this study, data from two sources were combined. A large part of the dataset (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,581) is part of the \u003cem\u003eMultilingualism and Multiliteracy in India (MultiLila)\u003c/em\u003e project (2016\u0026ndash;2020; Tsimpli et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). Additional data from children in Hyderabad (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;171) are part of the follow-up project \u003cem\u003eSupporting the development of Indian primary school children's reading comprehension skills: A SCaffolding-based Intervention\u003c/em\u003e (2021\u0026ndash;2022; Vogelzang et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; henceforth referred to as the \u003cem\u003eASCI\u003c/em\u003e project)\u003csup\u003e2\u003c/sup\u003e. Our final sample consists of 1,752 children across four age categories (age 8, 9, 10, and 11; 982 girls, 770 boys, see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for an overview). All participants attended year 4 or 5 in government primary schools in India. The participating primary schools were located in Delhi, Hyderabad (highly urban), and Patna (more rural). These three cities were selected for the projects to reflect, at least to some extent, the geographical and cultural variation present in a country as large and diverse as India. Inclusion of schools in the projects was based on geographical region and type of school/SES. Concerning the latter, only government schools were recruited, which are free to attend and almost always have pupils from low SES backgrounds. This contrasts with the sample used in the original Raven's norms for India, which was based on private schools which are paid and typically have pupils from medium to high SES backgrounds (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for a comparison between the two samples).\u003c/p\u003e \u003cp\u003eThe relatively low SES backgrounds of the children in our sample is confirmed when looking at their parents\u0026rsquo; occupations. From the children for which information on this was accessible to us (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;872, 49.8%), for the overwhelming majority either one or both of the parents were laborers, with occupations such as cab or auto driver (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;124), some type of seller/vendor (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;103), mason or construction worker (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;86), painter (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;57), or general (day) laborer (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;46) for the fathers and maid or house help (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;137) or seamstress (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22) for the mothers. These occupations formed the largest categories, but this is not an exhaustive list. Data from the \u003cem\u003eASCI\u003c/em\u003e project additionally show that 15% of children indicated that their father could not read or write and 27% indicated that this was true of their mother. In addition, 20% of children indicated that their father had not been to school (note that this does not refer to a diploma/degree, as in the existing Raven's norms, but to primary/secondary school), and 24% indicated this for their mother. Given all these factors, we are confident that these children represent a sample from relatively low SES backgrounds.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of the number and percentage of children in our sample per age group and city.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge 8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge 9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge 10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAge 11\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDelhi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e353\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e\u003ctd\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGirls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e\u003ctd\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eHyderabad\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e84\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e185\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e202\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e130\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003e601\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGirls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66 (51%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64 (49%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003ePatna\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e261\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e309\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e146\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003e798\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGirls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e180 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82 (56%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e129 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64 (44%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e275\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e670\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e528\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e279\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1,752\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Procedure\u003c/h2\u003e \u003cp\u003eThe Raven's CPM test (Raven et al., 2008) consists of 36 items (matrices). In each item children were presented with a pattern from which one piece is missing (reminiscent of a puzzle). They are asked to pick one of six possible 'puzzle' pieces (answer alternatives) to complete the pattern \u0026ndash; only one answer is correct, the other five are incorrect. The 36 matrices are divided equally into three sets (A, AB, and B) and items become progressively more difficult. Each correct answer gains the child 1 point, with the maximum possible score on this test being 36.\u003c/p\u003e \u003cp\u003eThe matrices and answer alternatives were presented on a laptop, and children could indicate their choice by saying the number of the answer alternative or by pointing to it. Besides the explanation of the test, it is thus, or at least it has the potential to be, a non-verbal test. The tests were administered by research assistants from India who were proficient in local languages as well as English. The children were tested individually in a separate room or courtyard in their school. The test explanation was provided in the language they were most comfortable in, insofar as that was possible given the many languages spoken by the children. Demographic and language background information was obtained through an interview with the children, also in the language they were most comfortable with insofar as it was possible\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Ethical considerations\u003c/h2\u003e \u003cp\u003eEthical consent was collected as part of the two projects that the samples were taken from. Consent was given by the child (orally), as well as by the head teacher or principal of the participating schools. Many of the parents of lower-SES children in India are semi-literate or illiterate and their communication with the school is scarce. Parental consent was therefore ensured through the school principal and the children's teachers. The principal approached parents (mostly by phone) to ask them for consent. The studies were conducted in accordance with the Declaration of Helsinki, the ESRC\u0026rsquo;s Framework for Research Ethics (ESRC, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and the guidelines of the Indian Council for Medical Research (ICMR, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The protocol of the \u003cem\u003eMultiLiLa\u003c/em\u003e project was approved by the Ethics Committees of the University of Cambridge (RG83665), the Jawaharlal Nehru University, and the National Institute of Mental Health and Neurosciences. The protocol of the \u003cem\u003eASCI\u003c/em\u003e project was approved by the Ethics Committee of the University of Cambridge (2019-20/65). The project additionally obtained permission to approach schools from the State Minister of Education in Telangana (the state in which Hyderabad is located).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Analysis\u003c/h2\u003e \u003cp\u003eThe data from the two projects was prefiltered based on (1) availability of the Raven's CPM, (2) the age range of the children (i.e., children under 8 [\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14 of age 7] were excluded because of insufficient data, children over 11 were excluded because they were overaged for their grade level [\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;159]), and (3) the type of school (the \u003cem\u003eASCI\u003c/em\u003e project included three low-cost private schools which were not included in the current sample).\u003c/p\u003e \u003cp\u003eThe new norms and all plots presented in this paper were created in the R software (version 4.3.1, R Core Team, 2019). We used the package stenR to calculate the new norms (Kosinski, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), where we defined a normal distribution with a range of 55 to 145, a mean of 100, and a standard deviation of 15. New norms were calculated for each age group (age 8, 9, 10, and 11). In order to do this, we first created frequency tables and then scoring tables as described in \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/packages/stenR/vignettes/usage.html\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/packages/stenR/vignettes/usage.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (using the functions \u003cem\u003eGroupedFrequencyTable\u003c/em\u003e and \u003cem\u003eGroupedScoreTable\u003c/em\u003e and their default parameters). Norms were calculated for each integer on the normal distribution (from 55 to 145) but are presented in the manuscript more concisely (as in the Pearson's versions of Raven's norms) in increments/bins of 5. In doing so, numbers were rounded, for example, scores for 98 to 102 became bin 100. All plots were created with base-R. For the plots of the standard scores, standardized scores of \u0026lt;\u0026thinsp;60 were coded as 55 and standardized scores of \u0026gt;\u0026thinsp;140 were coded as 145.\u003c/p\u003e \u003cp\u003eWe then calculated the means, ranges, and standard deviations of the Raven's scores for the sample. Skewness and Kurtosis values were calculated with the moments package (Komsta \u0026amp; Novomestky, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As a rule of thumb, if the skewness is between \u0026minus;\u0026thinsp;0.5 and 0.5, the data are fairly symmetrical. If the skewness is between \u0026minus;\u0026thinsp;1 and \u0026minus;\u0026thinsp;0.5 or between 0.5 and 1, the data are moderately skewed. If the skewness is less than \u0026minus;\u0026thinsp;1 or greater than 1, the data are highly skewed (Bulmer, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). For kurtosis, the value reflecting an optimal normal distribution is 3. The greater the kurtosis, the higher the peak.\u003c/p\u003e \u003cp\u003eWe estimated the reliability of the Raven's CPM test for our sample of participants by examining the test-retest correlation of a subset of 629 children using Spearman's correlation. We additionally implemented a version of the split-halves reliability paradigm: We split the dataset of children for each age category in half and compared whether their means differed significantly with an independent samples (unpaired) \u003cem\u003et\u003c/em\u003e-test.\u003c/p\u003e \u003cp\u003eTo verify whether performance improved with age, we ran a linear regression with raw score as the dependent variable and age as the independent variable. Finally, we ran independent samples (unpaired) \u003cem\u003et\u003c/em\u003e-tests to compare the performance of girls and boys in each age group.\u003c/p\u003e \u003cp\u003eThe full dataset and analysis code for this paper can be found on the OSF [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/4tm3u/?view_only=2deb2e2bdcc54b4a93a019d57476cc06\u003c/span\u003e\u003cspan address=\"https://osf.io/4tm3u/?view_only=2deb2e2bdcc54b4a93a019d57476cc06\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eWe first discuss the current dataset and why the existing norms do not apply to it. We then present new norms for children attending government Indian schools. Finally, we present some measures of reliability and construct validity of the Raven's CPM for this sample of children.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The current dataset and existing norms\u003c/h2\u003e \u003cp\u003eSome core descriptives of the current dataset, including children\u0026rsquo;s raw scores on the CPM, per age category are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The mean raw scores on the test tend to increase with age, as expected. The skewness and kurtosis of the raw scores are within the acceptable range of normality. The raw scores indeed visually resemble a normal distribution (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of our sample (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,752) in the Raven's CPM test, including measures of skewness and kurtosis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eRaw scores\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eStandardized scores\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIf the existing norms would be appropriate for this sample of children, the normed standard scores should also resemble a normal distribution. However, this is not the case (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In fact, 1,375 of the children (78.5%) scored in the bottom 10th percentile, making the distribution heavily skewed and flagging the majority of children as potentially having intellectual challenges, with a standardized (IQ) score of 80 or lower (e.g., Wieland \u0026amp; Zitman, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Measures of skewness (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) confirm that the standardized scores are moderately to highly right-skewed. Thus, the existing norms are too stringent for this sample of children and leads to potentially meaningful distinctions between children's scores being lost when interpreted by reference to normed data. For example, children of age 11 who score 16 or below are all assigned the standard score\u0026thinsp;\u0026lt;\u0026thinsp;60, with a percentile rank of 0.1, even though a child scoring 16/36 objectively performed better than a child scoring 10/36.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 New norms for children attending government schools in India\u003c/h2\u003e \u003cp\u003eBased on the children\u0026rsquo;s raw scores, and the assumption that scores should be normally distributed, new norms were calculated for these 8-to-11-year-old children (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). When using these new norms to calculate standard scores, the scores approach a normal distribution (compare Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e to Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For completeness, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the distributions of the standard scores for the four different age groups.\u003c/p\u003e \u003cp\u003eNote, however, that there isn\u0026rsquo;t always a logical progression in performance with age in these norms, as they are data-driven and data is limited at the far ends of the distributions. For example, 8-year-old children with a score of 4 would receive a standard score of 60 whereas 9-year-old children with a score of 4 would receive a standard score of \u0026lt;\u0026thinsp;60. For that reason, we provide manually modified norms based on Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e that follow a logical age progression in the Supplementary Material (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Users might choose to use Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e instead of the purely data-driven Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e to standardize their data.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNew Raven\u0026rsquo;s CPM norms table for 8-to-11-year-old children attending government schools in India\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentile rank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge 8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge 9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge 10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAge 11\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u0026ndash;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u0026ndash;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u0026ndash;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u0026ndash;14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u0026ndash;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u0026ndash;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u0026ndash;17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u0026ndash;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e105\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e110\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e115\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e84\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26\u0026ndash;27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e120\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u0026ndash;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u0026ndash;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u0026ndash;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e125\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u0026ndash;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e130\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e97.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u0026ndash;33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u0026ndash;31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e135\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32\u0026ndash;33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e140\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e99.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34\u0026ndash;36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u0026thinsp;140\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e99.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u0026ndash;36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u0026ndash;36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u0026ndash;36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe additionally examined the reliability and construct validity of the Raven's CPM in our sample of children. Note that these investigations were performed on the children\u0026rsquo;s raw scores (out of 36) and are thus not a validation of the norms, but rather of the Raven's CPM test itself.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Reliability\u003c/h2\u003e \u003cp\u003eWe analyzed the external reliability of the Raven's CPM in two ways. The first was through a test-retest paradigm. The Raven's CPM test was re-administered to a subsample of 629 children (338 from Delhi, 291 from Hyderabad; 334 girls, 295 boys) after approximately one year. The correlation coefficient between the two administrations was .52 (CI = [.46 \u0026minus;\u0026thinsp;.57]). This is a little lower than was found for other studies (e.g., Kazem et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), but the time period in between the two tests was much larger in the current study. However, it is also lower than the previously reported one-year test-retest reliability (.71, Court \u0026amp; Raven, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1995\u003c/span\u003e, as reported in Kazlauskaite \u0026amp; Lynn, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), and more in line with previously reported two-year test-retest reliability (of .499 in Lithuanian children, Kazlauskaite \u0026amp; Lynn, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor a further investigation of reliability, we implemented a version of the split-halves reliability paradigm. That is, we split the dataset of children for each age category in half randomly and compared whether their means differed significantly. The results indicate that this was not the case for any of the age groups (see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eT-test results comparing the split-half groups on Raven\u0026rsquo;s CPM in the four different age categories.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1st half Mean (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2nd half Mean (\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.04 (5.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.33 (6.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.683\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.71 (5.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.06 (6.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.76 (5.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.89 (6.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.802\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.92 (6.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.63 (7.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Construct validity\u003c/h2\u003e \u003cp\u003eWe assessed the construct validity of the Raven's CPM for our sample by assessing whether performance improves with age and whether performance differs between boys and girls. A linear regression confirmed that raw scores significantly increased with age (\u003cem\u003e\u0026szlig;\u003c/em\u003e = .56; \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.55; \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) as expected.\u003c/p\u003e \u003cp\u003eWe then assessed the influence of gender in each age category. T-tests showed no difference in performance between girls and boys at age 8, but differences in performance between girls and boys at age 9, 10, and 11 were found, as well as when all ages were collapsed, with boys outperforming girls (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison between performance on the Raven's CPM by boys and girls across the four age categories.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGirls: Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoys: Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.95 (5.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.47 (6.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.468\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.09 (5.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.93 (6.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.09 (5.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.82 (6.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.001 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.48 (6.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.18 (7.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.039 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.28 (5.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.87 (6.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e* \u003cem\u003ep\u003c/em\u003e \u0026lt; .05; ** \u003cem\u003ep\u003c/em\u003e \u0026lt; .01; *** \u003cem\u003ep\u003c/em\u003e \u0026lt; .001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study challenged the existing Raven\u0026rsquo;s CPM norms for India (Raven, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) based on a sample of 8-to-11-year-old Indian children who attend government primary schools. We argued that the existing norms were developed based on children with higher SES attending private schools, whereas roughly two-thirds of school-going children in India attend government schools and are from lower socio-economic backgrounds (Government of India, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e-22). According to the existing norms, the vast majority of children from our sample substantially underperformed compared to the sample on which the existing norms were based (cf. Al-Shahomee \u0026amp; Lynn, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Anum, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Costenbader \u0026amp; Ngari, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Knoetze et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This interpretation is harmful and does not adequately identify children who are in fact intellectually at risk and who are performing in line with or better than their peers. We presented new norms (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), which are more appropriate for our sample and potentially for other Indian children between 8\u0026ndash;11 years old who attend government primary schools as well. We strongly encourage researchers who use CPM standardized scores to think about which norms are most appropriate for their datasets and for educators and clinicians to use the norms most appropriate on a case-by-case basis.\u003c/p\u003e \u003cp\u003eFurthermore, we presented evidence that the Raven\u0026rsquo;s CPM test in our population is reasonably reliable based on test-retest and a split halves paradigms. We are thus confident that the test can be a useful tool for testing Indian children. Importantly, although we provided an overview of several issues with the Raven\u0026rsquo;s tests for diverse populations in the introduction, we are not advocating to abandon it altogether in non-W.E.I.R.D contexts given that it still provides some useful information about visuo-spatial reasoning. Rather, we are (1) providing updated norms that more appropriately reflect a large proportion of Indian children, and (2) cautioning researchers, educators, and clinicians to carefully consider what the outcomes of a Raven\u0026rsquo;s test in their participants may signify, ensuring they account for the broader (sociocultural) context and possible influences. Importantly, we support the view that tasks such as the Raven\u0026rsquo;s can give insights into \u003cem\u003eintelligence test scores\u003c/em\u003e, which are separate from \u003cem\u003eintelligence\u003c/em\u003e itself (Van de Vijver \u0026amp; Leung, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). However, to be more specific, and to avoid any potential misunderstanding, it is more accurate to regard the Raven\u0026rsquo;s CPM as assessing visuo-spatial reasoning by means of puzzle manipulation rather than intelligence (\u003cem\u003eg\u003c/em\u003e) more broadly (Gonthier, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding construct validity, we found differences between boys and girls. This result is in contrast with previous research which has generally found invariance of performance across genders on Raven\u0026rsquo;s CPM (e.g., Kazem et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In fact, the sample of children used in the original norms for India showed a non-significant difference in performance between boys and girls (collapsed over age groups, \u003cem\u003ep\u003c/em\u003e = .44; Raven, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, the result is in line with Herv\u0026eacute; et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who found a similar gender gap in cognitive (and noncognitive) outcomes, including Raven\u0026rsquo;s, in Indian adolescents. They suggest that this is due to \u0026ldquo;an institutionalized gender bias in education against girls in India\u0026rdquo; (p. 85). Although we cannot know the cause of this for certain, we can speculate about why boys outperformed girls in the sample presented in the current study. Specifically, this might be a direct result of the culture within lower socio-economic groups in India, with education being more challenging for girls in terms of regular school attendance and continuity. However, we have no direct evidence for this in the current sample, and there were more girls (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;982) than boys (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;770) attending the schools from which our data were collected. Girls in India, especially from lower SES backgrounds, have been known to outperform boys in language subjects (Natta et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Shenoy et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; UNICEF, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Vogelzang et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). Interestingly, in previous research using a subsample of our study (Tsimpli et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e), gender was not found to influence performance on an n-back cognitive task or Raven's CPM. So, the observed effect of gender needs further investigation to be substantiated.\u003c/p\u003e \u003cp\u003eNote that the new norms presented in this paper are not all-encompassing either. Specifically, they may lead to potential skewness in the opposite direction when used on a sample of higher-SES children and/or those attending private schools or residing in different regions across India, causing researchers, educators, or clinicians to miss children who are indeed in need of intervention. A truly representative norm would be one that includes all children across socio-economic and geographic strata in India. Regardless, the norm scores we have provided are likely closer to that of the total child population than the original norm scores, as the majority of Indian children attend government schools (Government of India, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e-22).\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Limitations\u003c/h2\u003e \u003cp\u003eWe acknowledge that our sample, and therefore our analysis, has some limitations. Most notable, the participating children were from three different cities/regions, which leads to limited generalizability across a country as large and diverse as India. In addition, our sample only spanned between the ages of 8 and 11, in contrast with the original Indian norms, which started from the age of 4. Moreover, we had a distinct focus on children attending government schools, which has the advantage of being able to provide norms for this specific sub-population, but limits generalizability to other sub-samples of children. Nevertheless, we were able to present new norms based on a large sample size and valid representation of a subsample of the population.\u003c/p\u003e \u003cp\u003eA second limitation of this research lies in the datasets. With respect to the demographic data, much information about children's backgrounds, parental occupations and parental education was incomplete. In addition, in contrast with many studies based in Western countries, the information about the children's backgrounds was not based on parental reports, but on children\u0026rsquo;s self-reports. In some cases, this made the information unreliable or less accurate (e.g., some children did not know their parents' profession, and some children reported speaking English whereas this was likely only reported because the child thought that this was a socially desirable response). No other data was available in the examined datasets, but future work might try to obtain parental reports as well. Note, however, that this may be challenging and labor-intensive in the instances in which parents cannot read or write. With respect to the Raven\u0026rsquo;s data, the datasets did not contain item-level data, so that analyses such as item difficulty or internal consistency could not be conducted (though note that these were not conducted in Raven, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e either; cf. Cotton et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Finally, we reported the official medium of instruction of the schools that the children attended, but from previous research we know that many different languages are used in the classroom in government schools in India (Lightfoot et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Conclusion\u003c/h2\u003e \u003cp\u003eIn conclusion, this study challenges the existing norms for the Raven\u0026rsquo;s CPM in India. We provide new norms, which better reflect government school children\u0026rsquo;s educational and socio-demographic backgrounds and are more suitable to use with similar children than existing norms. It is not necessary to replace the existing Raven\u0026rsquo;s CPM norms completely, but these new norms can be used by researchers, educators and clinicians where appropriate.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declared no potential competing interest with respect to the research, authorship, and/or publication of this article.\u003c/p\u003e \u003ch2\u003eCompliance with Ethical Standards\u003c/h2\u003e \u003cp\u003eEthical consent was collected as part of the two projects that the samples were taken from. The study was conducted in accordance with the Declaration of Helsinki, the ESRC\u0026rsquo;s Framework for Research Ethics (ESRC, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and the guidelines of the Indian Council for Medical Research (ICMR, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The protocol of the \u003cem\u003eMultiLiLa\u003c/em\u003e project was approved by the Ethics Committees of the University of Cambridge (RG83665), the Jawaharlal Nehru University, and the National Institute of Mental Health and Neurosciences. The protocol of the \u003cem\u003eASCI\u003c/em\u003e project was approved by the Ethics Committee of the University of Cambridge (2019-20/65). The project additionally obtained permission to approach schools from the State Minister of Education in Telangana (the state in which Hyderabad is located).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eFor this study, data from two sources were combined. The \u003cem\u003eMultiLila\u003c/em\u003e project (PI: Ianthi Tsimpli) was funded by the Department for International Development and the Economic and Social Research Council (ESRC-DFID, Grant Ref: ES/N010345/1). The project \u003cem\u003eSupporting the development of Indian primary school children's reading comprehension skills: A SCaffolding-based intervention (ASCI)\u003c/em\u003e (PI: Ianthi Tsimpli) was funded by the British Academy (Grant Ref: TGC\\200109).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: M.V., M.W., and I.M.T. Formal analysis and investigation: M.V. Writing - original draft preparation: M.V. and M.W. Writing - review and editing: M.V., M.W., and I.M.T. Funding acquisition: I.M.T. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAcknowledgements We would like to thank all the research assistants, children and teachers for their participation and support. We are very grateful to Suvarna Alladi, Anusha Balasubramanian, Debanjan Chakrabarti, Ganesh Devy, Dhir Jhingran, Vasanta Duggirala, Amy Lightfoot, Theodoros Marinis, Rama Matthew, Ajit Mohanty, Lina Mukhopadhyay, Minati Panda, Bapi Raju, Abhigna Reddy, Pallawi Sinha, and Jeanine Treffers-Daller for their advice and support throughout the MultiLiLa project. We would like to thank Victoria Murphy and Lina Mukhopadhyay for their advice and support throughout the ASCI project.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed during the current study are available in the Open Science Framework repository, along with all analysis code:[https://osf.io/4tm3u/?view_only=2deb2e2bdcc54b4a93a019d57476cc06](https:/osf.io/4tm3u/?view_only=2deb2e2bdcc54b4a93a019d57476cc06)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAl-Shahomee, A. A., \u0026amp; Lynn, R. (2010). 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Effects of mother tongue education and multilingualism on reading skills in the regional language and English in India. \u003cem\u003eTESOL Quarterly\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1002/tesq.3326\u003c/span\u003e\u003cspan address=\"10.1002/tesq.3326\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWicherts, J. M., Dolan, C. V., Carlson, J. S., \u0026amp; van der Maas, H. L. J. (2010). 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It is time to bring borderline intellectual functioning back into the main fold of classification systems. \u003cem\u003eBJPsych Bulletin\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(4), 204\u0026ndash;206. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1192/pb.bp.115.051490\u003c/span\u003e\u003cspan address=\"10.1192/pb.bp.115.051490\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWing, H. (1980). Practice effects with traditional mental test items. \u003cem\u003eApplied Psychological Measurement\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e,141\u0026ndash;155.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Such English idealism in low-cost private and government school contexts does not always translate to \u0026lsquo;pure\u0026rsquo; English use in practice though (Lightfoot et al., 2022; Treffers-Daller et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tsimpli et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Tsimpli \u0026amp; Balasubramanian, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nevertheless, the goal of private education is to implement English instruction, often without taking learners' diverse linguistic backgrounds and English exposure into account.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e One might be concerned about the temporal gap between the two datasets and the potential influence of COVID-19 on children\u0026rsquo;s schooling and cognitive performance. We therefore compared the ASCI dataset to age-matched data from Hyderabad from the MultiLila project. The children in the ASCI dataset scored higher (mean raw score: 23.7) than their MultiLila peers (mean raw score: 20.5; paired t-test: \u003cem\u003et\u0026thinsp;=\u003c/em\u003e\u0026thinsp;5.47; df\u0026thinsp;=\u0026thinsp;409; \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) on the CPM, not lower as might be expected after COVID-19. This performance difference may thus simply reflect naturally varying performance levels across regions, neighborhoods, schools, and individual children. Further investigation into this effect is beyond the scope of the current study.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eWhere it was not possible for the research assistant to speak the language most comfortable to the child, they found a language in which both could understand each other and used that to communicate.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cultural-cognitive-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cucs","sideBox":"Learn more about [Journal of Cultural Cognitive Science](http://link.springer.com/journal/41809)","snPcode":"41809","submissionUrl":"https://submission.nature.com/new-submission/41809/3","title":"Journal of Cultural Cognitive Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Raven's Coloured Progressive Matrices, intelligence test, visuo-spatial reasoning, norm scores, India, government schools","lastPublishedDoi":"10.21203/rs.3.rs-8902845/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8902845/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntelligence measures, such as the Raven's Coloured Progressive Matrices (CPM), rely on norm 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 accept 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\u0026rsquo;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 new 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.\u003c/p\u003e","manuscriptTitle":"Challenging existing Raven's norms based on children attending government schools in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-22 16:59:27","doi":"10.21203/rs.3.rs-8902845/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-11T13:57:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T15:55:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166343190726515421053036954805510035278","date":"2026-03-09T16:16:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-09T09:46:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"188909484175616397208641105885400473998","date":"2026-02-26T10:50:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-18T09:23:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-18T09:20:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-18T04:49:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cultural Cognitive Science","date":"2026-02-17T16:08:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cultural-cognitive-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cucs","sideBox":"Learn more about [Journal of Cultural Cognitive Science](http://link.springer.com/journal/41809)","snPcode":"41809","submissionUrl":"https://submission.nature.com/new-submission/41809/3","title":"Journal of Cultural Cognitive Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"39dab238-007a-49d6-8dec-331c863bff8e","owner":[],"postedDate":"February 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T10:07:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-22 16:59:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8902845","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8902845","identity":"rs-8902845","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}