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

Authors: Sayibu, Abdul Badi
Keywords: gender;data disaggregation;inclusive education;COVID-19;logic tree;television education lessons;education in emergencies
Issue Date: Jun-2022
Publisher: Inter-agency Network for Education in Emergencies
Citation: Sayibu, Abdul Badi. 2022. “Data Disaggregation for Inclusive Quality Education in Emergencies: The COVID-19 Experience in Ghana.” Journal on Education in Emergencies 8 (2): 170-82.
Series/Report no.: Volume 8;Number 2
Abstract: The process of data analysis provides, undoubtedly, some of the major challenges facing organizations during the implementation of interventions in emergencies. The challenges are primarily due to the lack of direct access to beneficiaries and the rapidly evolving nature of emergencies. This paper outlines how Plan International’s Making Ghanaian Girls Great! (MGCubed) project used phone-based surveys to assess the uptake of a Ghana Learning TV (GLTV) programme implemented in partnership with the government. Due to the emergency context and the need for real-time information to guide the implementation of this intervention, 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 using a logic tree technique to gain valuable insights from the survey data. The method allowed for exploring the insights of the data set in real-time without requiring more complex and time-consuming analysis. All views expressed in this article are the author’s and not of FCDO.
ISSN: 2518-6833
Rights: The Journal on Education in Emergencies, published by the Inter-agency Network for Education in Emergencies (INEE), is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, except where otherwise noted.
Appears in Collections:Volume 8, Number 2

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