Collecting high quality data is essential to the success of any project or initiative. Funding providers and, especially, local project managers, often play important roles in data collection. There are several things that need to be considered to ensure that data collection processes and measurement systems are stable and reliable. Incorporating these into a data collection plan will improve the likelihood that the data and measurements can be used to support the analysis.
Here are 3 tips for good data collection:
Tip 1. Identify the problem and encourage honesty
It is important to be clear about why data is being collected and what it will be used for. There can be legitimate fear amongst program managers that collecting data on a program’s performance is akin to conducting an audit. That is, if certain measures aren’t achieved a program may be deemed unsuccessful and further funding or support may cease. For example, when asking project managers to report on the number of project participants, take the time to explain why it is important to report honestly. They may need assurance that their funding or support will not be jeopardised if they don’t achieve a particular count.
Tip 2. Use consistent definitions
Accurate data collection is essential to maintaining the integrity of research and evaluation. When multiple teams or project managers are involved in collecting data from their own sphere or project, all parties responsible for the collection of data should be given consistent definitions. Otherwise, if two people interpret things differently, the differences might affect the data. For example, when reporting on a monthly basis, is each figure a count for the previous month, or is it an aggregate of all previous months? It is important to consider carefully how the definition of those two statistics would differ and which would be the more meaningful. This should be explained carefully to all data collectors involved.
Tip 3. Decide who will analyse, interpret and report the results
The person analysing, interpreting and report the results needs to know what analysis will be required, even before the first data point is collected. Analysis of the data must be carried out thoughtfully and carefully, thinking about what it actually means and whether the data can accurately explain it. Consideration should be given to whether there is enough data to draw particularly conclusions and care taken to not make assumptions as a result of incomplete data.
Conclusion
Data collection is too important to take lightly, particularly when program budgets hinge on numbers reported in research and evaluation reports. Good data collection will result in research questions being able to answered correctly, studies being able to repeated and validated, resources and budgets being used wisely and supporting sound decisions for the development of good public policy.