Enhancing Your Park and Recreation Programs Evaluation Skills: Lesson 1

September 2, 2019

Providers of youth recreation programs know when their recreational programs and services benefit their communities, but measuring and evaluating these outcomes are difficult. The difficulty is especially evident when practitioners want to demonstrate improved health benefits, but health is a subjective term that is interpreted differently from person to person, similarly to recreation. The subjectivity of these terms may make it difficult to craft questions on an evaluation survey that are understood the same way by all survey participants. To complicate matters further, the longevity of programs and services are dependent on producing measurable results. This reality can leave practitioners frustrated and confused about how to measure their program’s outcomes.

Similar frustrations were shared by NRF grantees at the 2019’s Nonprofit Executive Leadership Program (NELP) in Atlanta, Georgia. Therefore, the purpose of this post is to give providers of youth recreation programs insight into how to make program evaluations quick and effective. One lesson will be covered in this post, but I will release more blogs in the future on this topic because of the need expressed at NELP.

Lesson 1: Create a Logic Model

One of my supervisors, Gwendolyn Sams, Information and Policy Analyst for UITS (University Information Technology Services) at Indiana University, introduced me to logic models, and I am so thankful that she did. A thoughtfully developed logic model will make your program evaluations less stressful, more efficient, and effective. The reason logic models are useful for evaluations is because they force you and your team to think through your program’s intended: (1) outputs, (2) outcomes, (3) and how you will measure them (i.e., through a survey).

Mapping Out Your Program. Source: Pixabay
Mapping Out Your Program. Source: Pixabay

For example, let us say that your program extended an existing trail by ten miles. Outputs you would likely track could include: the amount of newly constructed trail miles and/or the number of individuals that use the extended trail. Outputs are typically the deliverables provided by your program.

Conversely, outcomes focus on the changes made in your community as a result of your program. You want to ask, how has participation in this program changed participant behavior? One example of an outcome could be the percentage of adults that no longer lead a sedentary life, because they now use the trail for physical activity. Outcomes are arguably more important to measure than outputs. The reason is because funders like to see the change your program makes, and outcomes are the component that can capture these sorts of data.

Example of outputs and outcomes. Outputs would be trail miles and number of people using the trail. Outcomes would be the changes seen in your community, such as increasing trail users' physical activty.
Example of outputs and outcomes. Outputs would be trail miles and number of people using the trail. Outcomes would be the changes seen in your community, such as increasing trail users' physical activty. Source: Pixabay

Many organizations cannot easily distinguish the difference from outputs and outcomes. If you ever need a refresher on the definitions of these two terms and how to create a logic model from scratch for that matter, then you could visit this page on the Centers for Disease Control and Prevention’s website. This is a great resource for organizations in terms of implementing program evaluations.

Another benefit of creating a logic model is that it forces you to identify how you will measure your outputs and outcomes. Many ways of collecting data on programs exist. Surveys, interviews, observing program participants, formal research studies, and the list goes on. Survey evaluations are popular because they are inexpensive, quick, and do not demand a lot of administrative resources.

In the end, logic models can help your team agree on a scope of work, what kind of data you will collect, and how you will collect them. These are important aspects to evaluations that are often overlooked. Thinking through these aspects of your program before the program launches will save you and your organization a lot of heartache once you need to show funders or board members the results from your program. For example, follow this link to see how NRF encourages organizations to write their own logic models. 

I know that this post has likely raised more questions. That is why I will continue to create posts about evaluations. The next post that covers evaluations will dive into survey methodology. In that post, I will describe some best practices when it comes to writing survey questions for the purposes of evaluating programs. Until next time!