program evaluation, logic model

Outcome measures: Is anyone better off?

March 02, 2020 | Pieta Blakely

 

three youth exchanging high-fives after playing basketball

 

In a previous post, I discussed process measures: data you measure while the program is in progress to assess what you’re doing and how well you’re doing it. This time, I’m going to discuss outcome measures, which measure the impact of your program.

Outcome measures evaluate the effect the program has/had on its target population, and can be assessed during or after the program is complete. Outcome measures assess the impact your program had on participants in three primary ways:

  • What they will have: Will they gain knowledge, improve skills, or develop a change in attitude? What will your participants have at the end of the program that they didn’t have at the beginning?
  • What they will do: How will they use the knowledge, skills, or changes in attitude they gained through the program?
  • How they will be better off: In the long term, how does this program make their lives better?

These outcome measures can be short-term or long-term. For example, consider a college-entrance-focused program that exposes students to careers in stem. Through participation in this program, these students will learn about stem careers as a possibility for themselves. They will take science classes. They will be more qualified to attend college and potentially have higher earnings. Some potential outcome measures for this program could include:

  • Short-term: Students will gain knowledge about careers in STEM fields. (What they will have)
  • Medium-term: Students will take science classes in high school. (What they will do)
  • Long-term: Students will go to college and pursue STEM majors. (How they will be better off)

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Of course, you could choose to measure outcomes long after the college stage, including how many students gain employment in STEM fields or how many have higher earnings. However, there are two potential obstacles: (1) longitudinal studies take time and are often not cost effective and (2) it may or may not be appropriate to measure these outcomes, as it could be invasive to the participant to gather the relevant data. Once your program is complete, participants have a right to their privacy and are no longer obligated to engage with you — and in some sensitive cases, it would be a violation of their privacy to follow up with them. However, if you have consent from your participants and they are reporting voluntarily, it may be appropriate to continue gathering data. Just ensure you are prepared for the additional time and expenses for this type of follow-up.

 

 

 

Tags: program evaluation, logic model