Data is essential for effective philanthropy, but it can also be biased, misleading, or unethical. How can you collect and use data in a way that respects the rights, dignity, and privacy of the people and communities you serve? In this article, you will learn some practical tips and best practices for collecting unbiased data in philanthropy.
Before you collect any data, you need to have a clear purpose and scope for your data collection. What are the questions you want to answer, the outcomes you want to measure, and the decisions you want to inform? How will the data help you improve your impact, learning, and accountability? How will you ensure that the data is relevant, reliable, and valid? Defining your data needs will help you avoid collecting unnecessary or irrelevant data that could introduce bias or waste resources.
The biggest thing you want to answer here is what problem are you trying to solve? And then what data do you really need to solve that problem, who has that data and how do you get it? You don’t want a bunch of data just to have a bunch of data, especially with privacy laws. Make sure you’re only getting data that you need to solve the problem.
One of the best ways to collect unbiased data is to involve your stakeholders in the data collection process. Your stakeholders are the people and groups who are affected by or interested in your work, such as beneficiaries, partners, donors, staff, and experts. By engaging them in defining, designing, implementing, and analyzing your data collection, you can gain their trust, input, and feedback. You can also ensure that your data reflects their perspectives, needs, and preferences, and that you address any ethical or cultural issues that may arise.
En tant que concepteur de learning expeditions, je reconnais l'importance de l'engagement des parties prenantes dans la création d'expériences d'apprentissage immersives. Impliquer directement bénéficiaires et experts dans la conception de nos programmes assure que le contenu est non seulement pertinent mais aussi directement en lien avec les enjeux réels de chacun. Cela permet de créer des parcours formatifs authentiques et riches en enseignements. C'est selon moi la meilleure fa?on de créer une rétention durable des apprentissages et une application concrète dans les différents contextes professionnels ou personnels des apprenants.
The methods and tools you use to collect data can also influence the quality and bias of your data. You need to choose methods and tools that are appropriate for your data needs, your stakeholders, and your context. For example, you may use surveys, interviews, focus groups, observations, or experiments to collect quantitative or qualitative data. You may use paper, online, or mobile tools to collect, store, and manage your data. You need to consider the advantages and disadvantages of each method and tool, and how they may affect the validity, reliability, and accessibility of your data.
Utiliser des enquêtes numériques, des entretiens et observations garantit la diversité des données mais rien de mieux qu'une learning expedition pour s'assurer de la véracité de l'information et d'une participation élargie et active directement sur le terrain.
The methods and tools chosen must be standardized to ensure the consistency of collected data and reduce bias. It's crucial to define methodologies, procedures, and processes while considering the diverse populations and contexts within the organization. Based on my past experiences, this approach is essential for improving data reliability and validity.
Before you launch your data collection, you need to test and refine it to ensure that it works as intended and that it does not introduce any bias or errors. You can do this by piloting your data collection with a small sample of your stakeholders, or by conducting a quality assurance check on your data collection tools and processes. You can then use the feedback and results from your testing to improve your data collection design, questions, instructions, and procedures.
After you collect your data, you need to analyze and interpret it to draw meaningful and accurate insights. You need to use appropriate methods and tools to analyze your data, such as descriptive statistics, inferential statistics, or thematic analysis. You also need to apply critical thinking and ethical principles to interpret your data, such as avoiding confirmation bias, generalizing from small samples, or misrepresenting the data. You need to acknowledge the limitations and uncertainties of your data, and report your findings honestly and transparently.
The final step in collecting unbiased data is to share and use your data for your intended purpose. You need to communicate your data in a clear, engaging, and accessible way, using formats and channels that suit your audience and message. You also need to respect the privacy and confidentiality of your data, and follow any legal or ethical guidelines for data sharing and use. You need to ensure that your data is used for positive and constructive purposes, such as improving your programs, informing your strategies, or advancing your mission.
L'observation directe pendant les learning expeditions fournit également des données précieuses sur l'engagement et les interactions des participants, tandis que les expériences pratiques et les simulations peuvent être aussi analysées pour évaluer l'application des compétences en temps réel.