The Covid-19 pandemic has disrupted the norm in all sectors and professions. The lockdown and other measures to ensure health and safety of citizens have meant that collection of data, especially that which was collected through person to person interactions was not possible. To the credit of many researchers, evaluators, data collectors, firms and institutions, there have been many and great innovations over the past two months to bridge the gap and ensure that there is data that can inform policy and decision-makers. However, it is important to ensure that the data that comes through is credible and reliable. Wrong, imprecise, incredible, unreliable data can undo the gains to have evidence-based policymaking. Also, the pressure to have data can lead to "bending" of procedures and protocols for data collection, analysis and inference. How then can we ensure the quality of the available data that is coming in? How can we evaluate the quality of analysis and subsequent inference to ensure that we influence the correct policy prescriptions? How can we promote collaboration and lesson sharing during these times?
RE: How can evaluation help improve data quality and policies on food security during Covid-19 pandemic?
The Covid-19 pandemic has disrupted the norm in all sectors and professions. The lockdown and other measures to ensure health and safety of citizens have meant that collection of data, especially that which was collected through person to person interactions was not possible. To the credit of many researchers, evaluators, data collectors, firms and institutions, there have been many and great innovations over the past two months to bridge the gap and ensure that there is data that can inform policy and decision-makers. However, it is important to ensure that the data that comes through is credible and reliable. Wrong, imprecise, incredible, unreliable data can undo the gains to have evidence-based policymaking. Also, the pressure to have data can lead to "bending" of procedures and protocols for data collection, analysis and inference. How then can we ensure the quality of the available data that is coming in? How can we evaluate the quality of analysis and subsequent inference to ensure that we influence the correct policy prescriptions? How can we promote collaboration and lesson sharing during these times?