Data Disaggregation for Inclusive Quality Education in Emergencies: The COVID-19 Experience in Ghana

Organizations that are implementing interventions in emergencies undoubtedly face some major challenges in analysing the necessary data. This is primarily due to the organizations’ lack of direct access to beneficiaries and the rapidly evolving nature of emergencies. This paper outlines how the Plan International project called Making Ghanaian Girls Great!—generally known as MGCubed—used phone-based surveys to assess the uptake of a Ghana Learning TV programme that the project implemented in partnership with the government. Due to the need for real-time information to guide the implementation of this intervention in an emergency context, there was little time to undertake a major statistical analysis of survey data. This paper discusses how the MGCubed project adopted a simple data disaggregation method that used a logic tree technique to gain valuable insights from the phone-survey data. The method enabled the project partners to explore the insights the dataset provided in real time without conducting a more complex and time-consuming analysis.


The authors discuss their work in the Behind the Pages podcast episode embedded below:

Resource Info

Resource Type

Journal Article


Published by

Journal on Education in Emergencies (JEiE)

Authored by

Abdul Badi Sayibu


Research and Evidence

Geographic Focus