Reviewing Data - Interpretation


Data analysis and interpretation are two separate processes. Interpretation gives meaning to the numbers or to the data. Numbers or numerical data are limited:

  • They must be interpreted in order to have meaning
  • They do not perfectly describe what the world is like
  • They can contain different degrees of error


As an example of how to interpret your results, the table below contains the results of our analysis of program satisfaction for the Arizona Youth Program. 

Program Satisfaction
Frequency of Participation High (Very satisfied) Medium (Satisfied) Low (Not very satisfied)
Frequent (>8 hrs/week)(N=50) 50% (N=25) 30% (N=15) 20% (N=10)
Minimal (<4 hrs/week)(N=50) 20% (N=10) 20% (N=10) 60% (N=30)
All participants (N=100) 35% (N=35) 25% (N=25) 40% (N=40)

Below is an example of how one might summarize the context of the data and provide definitions of key terms.

The data shown in the table above comes from a program satisfaction survey given to middle school youth in the Arizona Youth Program during their final week of participation. This 12-week program, which took place during the fall semester, provided after-school tutoring and civic engagement activities. Satisfaction was measured for all participants from their selections of one of three responses: 3=I am very satisfied with the program; 2=I am satisfied with the program; and 1=I am not very satisfied with the program.  

Having already provided the context for the data and defined the key terms, we could interpret this data as follows:

  • Of all participants in the program, 40% reported a low level of program satisfaction; however, 60% of those who reported a low level of program satisfaction also demonstrated minimal (four hours or less hours per week) program participation.
  • Of all participants, 35% reported a high level of program satisfaction. The majority of students reporting a high level of participation also showed frequent (eight hours or more per week) program participation.
  • Of all participants, 60% reported a high or medium level of satisfaction, and 40% reported a low level of satisfaction.
  • Of students who participated in the program on a frequent basis (eight or more hours per week), 50% reported a high level of satisfaction with the program.
  • Of students who participated in the program on a minimal basis (four or fewer hours per week), 60% reported a low level of satisfaction with the program.


Based on these multiple ways of presenting the data, we might be lead to ask the following questions:

  • What about those students who participated on an “average” basis, perhaps 6 or 7 hours a week?
  • Why would more frequent participation be related to program satisfaction? Do people come more because they enjoy the program?
  • Do all students’ perceptions improve over time (when comparing their perceptions of satisfaction at the beginning and at the end of the program)?
  • Do we know if these results were statistically significant?
  • Do we know if the level of participation caused participants satisfaction levels to change?

We may not be able to answer all of these questions based on the available data. When communicating the results of your evaluation to others, it may be useful to keep these questions in mind so that the limits of the study can be addressed.

 Explore Additional Resources