Please update your browser
We have detected that you are using an outdated browser that will prevent you from using
certain features. An update is required to improve your browsing experience.
-
Here are a few ways to ensure that your team is abiding by the honesty principle. Ensure that the data you present is accurate.
Here are a few ways to ensure that your team is abiding by the honesty principle.
Ensure that the data you present is accurate.
Obviously, this means that you should not present data that you know is inaccurate. However, it also means that you will need to constantly check the accuracy of your data. In Module 3, you will learn about data quality assessments, one technique for ensuring that your data is accurate.
Ensure that findings from your M&E are accurately represented.
Even if you collect your data flawlessly, it can still be misrepresented. Presenting conclusions that are too strongly worded or simply inaccurate is unethical behavior.
Example: An organization is providing counseling services to girls. They collect and present some data about the effects of their program.
The data shows that the girls who receive counseling through their program have an adolescent pregnancy rate lower than the national average. They publish a paper titled Counseling Program Causes Lower Adolescent Pregnancy Rate.
The organization is presenting a conclusion that they cannot prove. They have not proven that their program causes lower adolescent pregnancy rates. It is possible, perhaps even probable, that their program has this effect.
However, there are many other ways to explain their data. For example, it may be that the organization is only working in schools that already have a below-average adolescent pregnancy rate. Instead of saying that their Counseling Program Causes Lower Adolescent Pregnancy Rate, they might simply say Counseling Program Graduates Have An Adolescent Pregnancy Rate Below National Average. It is a less exciting title, but it is honest.
Share the limitations of your M&E strategies
Your team will not be able to collect perfect, absolutely complete data. Nor will you be able to state conclusions with complete certainty. Be honest about these limitations with donors, beneficiaries or anyone else who might be interested in your work. Ideally, share these limitations before your project starts, so that people do not develop unrealistic expectations.
For example, your organization might only have the resources to survey a small group of people. Make sure to share this fact when the data is presented. This way, anyone reading about the data can accurately assess the meaning of the data.
Share any areas where you might have a personal or professional interest in a certain outcome (a conflict of interest).
A conflict of interest happens when you or your organization might gain something useful if a certain outcome is reached.
For example, imagine an organization that receives donations from a pharmaceutical company. If they publish data that shows that one of that pharmaceutical company’s drugs is effective, their donor might be pleased with them and give them more money.
Of course, changing their data to make their donor look good would be deeply dishonest and, therefore, unethical. However, even if the data were completely accurate, it would still be unethical for this organization to publish any data about this drug without first disclosing their relationship with the donor. If you are ever worried that your organization might have a conflict of interest, make sure that you share this information when you present the data.
