Each of your key guiding questions might require a different type of analysis. In this section, you will find several methods of data analysis—but the world of data is vast, and our examples may or may not reflect the type of analysis you need. For a more in-depth look at choosing the right analysis tactics for your data, take a look at this helpful resource.
Analyzing your data should be empowering. After all, even very small sets of data can improve your ability to make better decisions. However, it is all too easy to make mistakes interpreting your results—and make bad decisions because of it.
For example, let us assume that we ran five summer service programs for rising high school juniors and seniors, with 50 participants in each program. In all of the programs, the majority of participants indicated that participation in the program significantly changed their mind about the importance of service, except for Program 5. From this comparison, you might decide that Program 5 was ineffective and that you should not run a similar program next year.
In this example, you are making the assumption that your results were a product of inadequate programming, when in fact, Program 5 failed to alter opinions because that group of participants already possessed a keen understanding of the importance of service. Instead of canceling Program 5, you may, instead, need to refine your recruitment strategy to target participants who do not already feel as strongly.
Another consideration to keep in mind when analyzing your data is confirmation bias, which is the tendency to interpret new evidence through the lens of existing beliefs or theories, rather than approaching new evidence at face value and without preconceived notions. Whenever you embark on an analytical journey, keep an open mind and understand that the data may or may not confirm your hypothesis, and it may not always tell you what you want to hear.