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

Autor/in	Sayibu, Abdul Badi
Titel	Data Disaggregation for Inclusive Quality Education in Emergencies: The COVID-19 Experience in Ghana
Quelle	In: Journal on Education in Emergencies, 8 (2022) 2, S.170-182 (13 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2518-6833
Schlagwörter	Foreign Countries; Data Analysis; Barriers; Educational Television; COVID-19; Pandemics; Telephone Surveys; School Closing; Elementary Secondary Education; Access to Education; Inclusion; Equal Education; Visual Aids; At Risk Students; Students with Disabilities; Gender Bias; Social Bias; Language Usage; Early Parenthood; Ghana + Suchen Sie Ihr Suchwort? Ausland; Auswertung; Bildungsfernsehen; Schulfernsehen; Telephone interview; Telefoninterview; School closings; Schule; Schließung; Schließung (von Schulen); Education; Access; Bildung; Zugang; Bildungszugang; Inklusion; Anschauungsmaterial; Student; Students; Disability; Disabilities; Schüler; Schülerin; Studentin; Behinderung; Geschlechterstereotyp; Sprachgebrauch
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. (As Provided).
Anmerkungen	Inter-agency Network for Education in Emergencies. 122 East 42nd Street, 14th Floor, New York, NY 10168. e-mail: journal@inee.org; Web site: https://inee.org/evidence/journal
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01