• What are we doing?
• What impact are we having or value are we providing?
• What resources are we using?

These three questions map to three categories of program information. Many engagement programs are based on program design frames such as logic models or theories of change that name specific program outputs, outcomes, and inputs.  Whether or not these are named explicitly, these three categories of information are candidates to include in any measurement and evaluation system.  How essential each type is depends on the way you want to use the information.  Here, I summarize each type and map it to the purposes listed in the “why measure engagement” discussions here and here.

Information categories

What are we doing?

What are our outputs?  Examples include the number of activities, events, reports, or services provided; number of participants; number of op-ed pieces; etc.

What impact are we having or value are we providing?

What are our outcomes?  These are changes that occurred as a result of the program.  Examples could include the number of K-12 students achieving targeted skills, acres of pollinator habitat, amount of energy saved, number of communities adopting a policy or plan, etc.  Note that while this site uses outcomes to refer to any changes or effects resulting from a program, some evaluation contexts differentiate outcomes, defined as relatively immediate effects of a program, from impacts, referring to longer-term changes that occur.

What resources are we using?

What are our inputs?  Examples include number of staff, number of hours, dollars invested, units involved, etc.

Mapping your purposes to the type of data you need

This chart outlines recommendations on the categories of data needed for specific purposes, thinking beyond outcome or impact data to ways information on activities and resources can be used as well.

• An essential characteristic is one that is needed to achieve the purpose—it can’t be done without it.
• An important characteristic is one that is needed, but the purpose is achievable even if the data don’t fully meet the characteristic.
• Useful characteristics are ones that are helpful to have, but the purpose is achievable even if they aren’t met.