• Data Quality

Data Quality

The data you collect will never be free of bias. Thus, you need to determine, with the help of your stakeholders, what quality and quantity of data is “good enough” for your decision-making, learning and accountability needs. As you begin to think about collecting MEAL data, it is useful to consider the following five data quality standards.

Validity Data are valid when they accurately represent what you intend to measure. In other words, the data you collect helps you measure the indicators you have chosen. When designing your collection methods, make sure they will collect data that will help you measure the indicators outlined in your PMP. Also, the mix of collection methods should meet your needs for triangulation.

Reliability Data are reliable when the collection methods used are stable and consistent. Reliable data are collected by using tools such as questionnaires that can be implemented in the same way multiple times. In practice, this means that if you use the same questionnaire to ask the same person the same questions and nothing else has changed, you should get the same answer. Consider this factor when you are designing your discussion guides and questionnaires for focus groups and interviews.

Precision Data are precise when they have a level of detail that gives you an accurate picture of what is happening and enables you to make good decisions. For example, precise data allow you to compare results between men and women, if this is important for your project. When designing your data collection tools, make sure any subgroups you have identified are incorporated into the design. Accordingly, precise data are collected using appropriate sampling methods, which are described in detail below.

Integrity Data have integrity when they are accurate. Data should be free of the kinds of errors that occur, consciously or unconsciously, when people collect and manage data. Errors can enter your data when, for example, the questionnaire is implemented incorrectly or the data are not properly entered into the database. Following the guidance outlined below on the design and implementation of collection tools and the management of the data you collect will increase the integrity of your data.

Timeliness Timely data should be available when you need it for learning that informs decisions and for communication purposes. Data are not useful to you when they arrive too late to inform these processes. This factor plays a significant role in your planning for data collection, which is the reason for the column in the PMP on timing. Design your data collection efforts to coincide with when you need to make decisions, and report to stakeholders. Timeliness should also be factored into the design and implementation of your tools. You want to make sure that your design is as efficient as possible and only collects the data that you absolutely must collect.

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