Collecting disability data in education at scale: Learnings from integration of Washington Group questions on disability into Pakistan's Annual School Census

Background Washington Group (WG) questions are increasingly used to generate critical data on children with disabilities within educational systems. There is little research on WGquestion performance when used at scale in an Annual School Census (ASC), or on how data collection implementation shapes the quality and nature of the data generated. Objectives This paper explores factors shaping the quality, accuracy and value of the data generated through Pakistan's integration of four WG Short Set (WG-SS) questions into their 2023/24 ASC. Methods This mixed-method study analysed the ASC data in Excel and Stata. Functional difficulty (FD) was defined using standard WG-SS cutoffs. Aggregate provincial FD prevalence and variations by province, domain and child sex were explored, alongside school-level FD prevalence distribution and variations. In-depth interviews were conducted with 11 federal and 16 provincial stakeholders, and 32 school representatives, and focus group discussions with 128 teachers. Findings Many challenges were observed in implementing the WG-SS questions, including rushed preparation and form design, and limited provision of guidance to schools. FD prevalence generated by the ASC was very low across all provinces and domains. Only 10% of schools reported any child with FD. Although stakeholders were broadly supportive of collection of disability data, little data analysis or use was identified. Conclusion While Pakistan's ASC disability data collection demonstrates commitment to inclusive education, data quality and use require attention. This paper identifies key areas for attention in education system disability data collection at scale.