Data Management: Data Quality Assurance

This presentation on ‘Data Quality Assurance’ was recorded on 2022, February 15th as Session 2 of Module 2 of the Data Management course delivered by Statistics for Sustainable Development, and covers what data quality assurance is and why it is important, gives some specific tips for this in ODK, and goes over practices to ensure data quality. This session was presented by Ciara McHugh. This course was supported by the Global Collaboration for Resilient Food Systems. For our free resources, visit our website: https://stats4sd.org/resources The other sessions in this module are here:    • Data Management in Practice (Module Two)   Mira este video en español:    • Gestión de datos: Aseguramiento de la cali...   Statistics for Sustainable Development is a not-for-profit, social enterprise that provides: • Statistical and data management expertise • Support on research designs and methodology • Technical guidance related to collection and processing of data and information. As a team of technicians and statisticians, we believe that our best work is done when supporting organisations and communities across the world that share our vision of a more sustainable future for all. The videos on our channel aim to support students, researchers, and academics, and are accompanied by the free resources on our website. Stay updated on what the team is up to over on our LinkedIn:   / statistics-for-sustainable-development   Contents: 00:00 - What is Data Quality Assurance and Why is it Needed? 01:04 - What Will be Covered in This Video? 01:26 - Primary Data 01:53 - Defining Standards 03:06 - Using Pre-designed Indicators 03:50 - Tips for Ensuring Data Quality for ODK 04:24 - Using Constraints in ODK 05:50 - Using Required Fields in ODK 06:21 - Using Select Instead of Text in ODK 07:21 - Using Hints in ODK 07:56 - Using Acknowledgements in ODK 08:43 - Using Relevant in ODK 09:35 - Using Metadata in ODK 10:40 - Language of the Data Collection Tool 11:27 - Paper Questionnaires 12:15 - Testing Data Collection Tools 13:04 - Training 14:10 - Data Quality Checks 14:38 - Follow Up Interviews 15:22 - Secondary Data