North Dakota Prevention Tool Kit

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 your data. Before beginning any analysis, it is important that you understand the data you have collected by reviewing them several times. For example, if your data consist of interview transcripts, read and reread the transcripts until you have a general understanding of the content. As you are reviewing, write notes of your first impressions of the data; these initial responses may be useful later as you interpret your data.
Organize your data. Qualitative data sets tend to be very lengthy and complex. Once you have reviewed your data and are familiar with what you have, organize your data so that they are more manageable and easy to navigate. This can save you time and energy later. Depending on the evaluation question(s) you want to answer, there are a variety of ways to group your data, including by date, by data collection type (such as focus group vs. interview), or by question asked.
Code your data. Coding is the process of identifying and labeling themes within your data that correspond with the evaluation questions you want to answer. Themes are common trends or ideas that appear repeatedly throughout the data. You may have to read through your data several times before you identify all of the themes within them.
Interpret your data. Interpretation involves attaching meaning and significance to your data. Start by making a list of key themes. Revisit your review notes to factor in your initial responses to the data.

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:

Categorical data have a limited number of possible values. For some categorical data, numbers assigned to categories have no inherent meaning and the order of the categories is arbitrary. For example, when asking about marital status, there are a limited set of possible responses and categories can be ordered in numerous ways. For other kinds of categorical data, numbers assigned to categories have inherent meaning and the order of the categories follows a logical progression in the values assigned to responses. A question where the responses range from 1 = “strongly agree” to 5 = “strongly disagree” is an example of this type of categorical data. There is no set interval between each response for categorical data.
Continuous data, in contrast, have many possible values. There is a logical progression in the numerical values assigned to responses and the interval between values is meaningful. Continuous data can have almost any numeric value along a continuum and can be broken down into smaller parts and still have meaning. Age, weight, height, and income are all examples of continuous data. Quantitative data analysis is the process of using statistical methods to describe, summarize, and compare data. Your analysis will vary based on the type of data you collect. Analyzing quantitative data allows your evaluation findings to be more understandable so you can use them to strengthen your program.

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

Frequencies, or counts, describe how many times something has occurred within a given interval, such as a particular category or period of time. For example, the number of training participants who are classroom teachers is a frequency. Frequencies can be used for categorical or continuous data.
A percentage is the given number of units divided by the total number of units and multiplied by 100. Percentages are a good way to compare two different groups or time periods. For example, if 50 of 100 training participants are classroom teachers, 50% of training participants are classroom teachers. Percentages can be used for categorical or continuous data.
A ratio shows the numerical relationship between two groups. For example, the ratio of the number of students in a particular school (300) to the number of teachers in that same school (25) would be 300/25, or 12:1. Ratios can only be used for continuous data.

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.