Upskilling CRY’s survey team with Statistics

By Priya Chetty on July 16, 2022
Image by Jannoon028 on Freepik

Child Rights and You (CRY) is one of the biggest non-governmental organisations in India. In 2020, CRY impacted over 7 lakh underprivileged children in India in education, health, and social welfare parameters. It is spread across 19 states and employs over 200 people who work on 99 key projects.

CRY is responsible for bringing to the forefront the issue of malnutrition, trafficking and bonded labour of children in India. The organisation’s network has been instrumental in identifying and assessing the key threats to children in post-Covid 19 India and has actively advocated for policy changes to improve their situation. With a vision to generate more actionable insights from their valuable data, we equipped their team with statistical tools.

What was the challenge?

Every year CRY collects primary data related to children’s socio-demographic parameters from across the country. This data is collected using different instruments and are raw and unstructured. Because of this, CRY could utilise it only for deriving basic insights such as the demographic composition. However, they wanted to dig deeper into the application of statistics to understand:

  1. How do the children’s demographics affect their behaviour during the lockdown?
  2. How have the children’s house conditions (financial and otherwise) affected their family’s ability to respond to Covid-19 emergencies?
  3. How will a continued lockdown affect the children’s well-being?

How did we help CRY?

More insightful findings need the right processing and the application of statistical tests. So our agenda was to conduct training sessions to upskill their ground team to:

What was the outcome?

Upskilling the existing ground team was easy and more efficient than replacing them with a skilled team. Not only did they gain a new skill, but they were also able to uncover graver patterns in the data. Since then the ground team have been able to:

  • Optimise their data collection instruments
  • Reduce errors in data
  • Craft better policy recommendations.