Data analysis and interpretation are two separate processes. Interpretation gives meaning to the numbers or to the data. Numbers or numerical data are limited:
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.
|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:
Based on these multiple ways of presenting the data, we might be lead to ask the following questions:
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.