QUALITATIVE DATA ANALYSIS
Qualitative data consist of words, not numbers. As with quantitative data, systematic analysis and interpretation are required to bring order and understanding to the data that has been collected.
Qualitative data come in many forms. Examples include responses to open-ended questions on a survey administered at the end of a food safety training, the transcript from an interview or focus group session, notes from a log or diary, or field notes. Data might come from many people, a few individuals, or a single case.
Analyzing Qualitative Data
Know the data. Good analysis depends on a thorough understanding of the data set. This means one must read and re-read the text. If tape recordings are available, listen to them several times. Write down thoughts while going through the data. Also, just because one has data does not mean it is quality data. Before beginning any analysis, consider the quality of the data and proceed accordingly. Investing time and effort in analysis may give the impression of greater value than is merited. Always explain the limitations and level of analysis that is appropriate given the data set.
Focus the analysis. Review the purpose of the evaluation. Write down a few key questions that the analysis should answer. These questions might change as one works with the data, but it will help begin the process. Here are two common approaches to focus an analysis.
Categorize information. Some define categorizing information as coding the data. Categorizing does not involve assigning numerical codes as is done in quantitative analysis where one labels data with preset values. Categorizing helps one to bring meaning to the data by identifying themes or patterns — ideas, concepts, behaviors, interactions, incidents, terminology or phrases used – in order to bring meaning to the text.
This can be fairly labor-intensive depending on the amount of data one has. But this is the basis of qualitative analysis. Read and re-read the text to identify coherent categories. Also, assign abbreviated codes of a few letters, words, or symbols and place them next to the themes and ideas that are found. This helps one to organize the data into categories. Provide a descriptive label (name) for each category that is created. Be clear about what to include in the category and what to exclude.
Here are two ways to categorize narrative data that is collected — using preset or emergent categories. With preset categories, start with a list of categories and then search the data for them. Whereas with emergent categories, read through the text and find the themes that recur in the data. These become the categories. This approach allows the categories to emerge from the data. Categories are defined after one has worked with the data or as a result of working with the data. Sometimes, one combines these two approaches — starting with some preset categories and adding others as they become apparent.
The initial list of categories might change as one works with the data. The definition of categories might change or new categories might be identified to accommodate data that do not fit the existing labels. Main categories might be further broken into subcategories. This allows for greater discrimination and differentiation. Continue to build categories until no new themes or subcategories are identified. Add as many categories as needed to reflect the nuances in the data and to interpret data clearly. While one usually wants to develop mutually exclusive and exhaustive categories, sometimes sections of data fit into two or more categories. So some data might need to be cross-indexed.
Identify patterns and connections within and between categories.
As one organizes data into categories — either by question or by case — one will see patterns and connections within and between categories. Assessing the relative importance of different themes might be important to the analysis. Here are some ways to do this.
Develop a table or matrix to illustrate relationships across two or more categories. Look for examples of responses or events that run counter to the prevailing themes. What do these countervailing responses suggest? Are they important to the interpretation and understanding? Often, one learns a great deal from looking at and trying to understand items that do not fit into the categorization scheme.
Use themes and connections to explain findings. It is often easy to get side tracked by the details and the rich descriptions in the data. But what does it all mean? What is really important? This is called interpreting the data — attaching meaning and significance to the analysis.
Develop a list of key points or important findings discovered as a result of categorizing and sorting the data. What are the major lessons? What has application to other settings, programs, and studies? What will those who use the results of the evaluation want to know?
Develop an outline for presenting results. The length and format will depend on the audience. It is often helpful to include quotes or descriptive examples to illustrate results. A visual display might help communicate the findings. Sometimes a diagram with boxes and arrows can help show how all the pieces fit together. Creating such a model may reveal gaps in the investigation and connections that remain unclear. These might be areas that suggest further study.
Moving from a mass of words to a final report requires using a systematic method to organize and keep track of the text. This is largely a process of cutting and sorting. Work by hand, either with a hard copy or directly on a computer. How data is managed depends on personal preference and the amount and type of qualitative data. In summary, here are some data management tips:
Check the data. Make sure everything is together. Decide whether the data are of sufficient quality to analyze, and what level of investment is warranted.
Add identification numbers. Add an identification number to each instrument, respondent, group, or site.
Prepare data for analysis. Transcribe taped interviews. How complete to make the transcription depends on the purpose and resources. Sometimes, simply summarizing what people say is adequately for an analysis. Or, certain parts of an interview might be particularly useful and important and just those sections are transcribed. Other times, one needs to have every word of the entire interview transcribed. Transcription is time-consuming so be sure both data quality and use of the data are worth the investment. With small amounts of narrative data, work directly from the original hard copy. Text is usually typed into a computer program.
Decide how to format cases. Willresponses be entered question by question, or whether you want to keep all text concerning one case, individual, group or site together. If data is typed into a word processing program, leave a wide margin on the left so enough space is available to write labels for text and notes. Number each line to help with cutting and sorting later.
Use a special qualitative data analysis program. Using such a program will depend on the size of the data set, resources available, preferences, and level of analysis needed or warranted.
Other considerations when analyzing qualitative data:
Bias can influence the results. The credibility of the findings can be increased by:
Pitfalls to Avoid
Working with qualitative data is a both a science and an art. It involves critical, analytical thinking and creative, innovative perspectives.
Communicate findings to the respondents who participated in the evaluation. Not only is this courteous, but it helps to ensure their cooperation in future work. Communication methods depend on the audience. Examples include: a written report, short summary statements, videotapes, pictures, photo essays, displays, slide presentations, graphs and visuals, and media releases.
Test Your Knowledge
1. How is qualitative data different from quantitative data?
2. What are two ways to categorize narrative data?
3. Can qualitative data be generalized?
4. Why should the process used to analyze data be documented?
5. What are four ways one assess the relative importance of different themes when analyzing data?