Arriving at Conclusions
Now it is time to take the results of your different data analyses and pull them together into an overall picture of your program. What do the patterns in the data or the comparisons tell you? How can you use them to answer your evaluation questions?
Conclusions are driven by the reason that you are evaluating—the questions you wanted to answer. Depending on your reasons for evaluating, you may highlight conclusions that relate to things like the program’s strengths and best practices, the impact it is having, or areas where it is not performing well. Extracting meaningful conclusions from data can be straightforward if the data provide clear, direct answers to your evaluation questions. However, it can be challenging if the answers are less apparent or if different data sources yield results that lead to different conclusions. While it is not always possible to resolve all differences or contradictions in the data, inconclusive data should still prompt discussion among stakeholders and can point out areas where more or different types of data may be needed.
Keep in mind that conclusions are different from recommendations. Conclusions describe what you learned as well as answer the evaluation questions. Recommendations describe the actions you think should be taken based on what you learned. Recommendations will be discussed in the next section.