Analyzing Evaluation Data

From CDC Division of Adolescent and School Health Evaluation Briefs.

Qualitative data are information in nonnumeric form. They usually appear in textual or narrative format. For example, focus group notes, open-ended interview or questionnaire responses, and observation notes are all types of qualitative data. Qualitative data analysis is the process of interpreting and understanding the qualitative data that you have collected.

It is critical that you develop a systematic approach for analyzing your qualitative data. There are four major steps to this process:

Review each theme that arose during the coding process and identify similarities and differences in responses from participants with differing characteristics. Also, consider the relationships between themes to determine how they may be connected. Determine what new lessons you have learned about your program and how those lessons can be applied to different parts of your program.

Quantitative data are information in numeric form. They can either be counted (such as the number of people who
attend a training) or compared on a numerical scale (such as the number of training participants who said that a
training was “very helpful” or “somewhat helpful”).

There are two main types of quantitative data:

Conducting quantitative data analysis: There are three major steps to this process:

Mean, median, and mode are three measures of the most typical values in your dataset (also called measures of
central tendency). A mean, or average, is determined by summing all the values and dividing by the total number of
units in the sample. A median is the 50th percentile point, with half of the values above the median and half of the
values below the median. A mode is the category or value that occurs most frequently within a dataset.

Review and interpret your data. Following data analysis, review your findings to identify patterns in your data.
Consider similarities and differences between responses from participants with different characteristics. Determine
whether there are any extreme data that fall significantly above or below the mean, median, or mode. Those extreme
data points may alter some statistics, such as the mean.

Summarize your data. Develop tables, graphs and charts to summarize your data findings. Communicate your
findings. When your analysis is complete, share your data with stakeholders. There are several ways to disseminate
your findings, including print formats, oral presentations, and web based distribution.