Qualitative Data Analysis: Identifying Meaningful Patterns and Themes
In order for qualitative data to be analyzable it must first be grouped into the meaningful patterns and/or themes that you observed. This process is the core of qualitative data analysis.
This process is generally conducted in two primary ways:
1. Content analysis
2. Thematic analysis
The type of analysis is highly dependent on the nature of the research questions and the type(s) of data you collected. Sometimes a study will use one type of analysis and other times, a study may use both types
Content analysis is carried out by:
1. Coding the data for certain words or content
2. Identifying their patterns
3. Interpreting their meanings.
This type of coding is done by going through all of the text and labeling words, phrases, and sections of text (either using words or symbols) that relate to your research questions of interest. After the data is coded you can sort and examine the data by code to look for patterns.
Thematic analysis - grouping the data into themes that will help answer the research question(s). These themes may be (Taylor-Powell and Renner, 2003):
Directly evolved from the research questions and were pre-set before data collection even began, or
Naturally emerged from the data as the study was conducted.
Once your themes have been identified it is useful to group the data into thematic groups so that you can analyze the meaning of the themes and connect them back to the research question(s)
The analysis process - Once you have these data, what do you do? The steps below describe the basic elements of narrative data analysis and interpretation. This process is fluid, so moving back and forth between steps is likely.
Step 1 Get to know your data
Good analysis depends on understanding the data. For qualitative analysis, this means you read and re-read the text. If you have tape recordings, you listen to them several times. Write down any impressions you have as you go through the data. These impressions may be useful later.
Also, just because you have data does not mean those are quality data. Sometimes, information provided does not add meaning or value. Or it may have been collected in a biased way.
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. Explain the limitations and level of analysis you deem appropriate given your data.
Step 2 Focus the analysis
Review the purpose of the evaluation and what you want to find out. Identify a few key questions that you want your analysis to answer. Write these down. These will help you decide how to begin. These questions may change as you work with the data, but will help you get started.
How you focus your analysis depends on the purpose of the evaluation and how you will use the results. Here are two common approaches.
Focus by question or topic, time period or event
In this approach, you focus the analysis to look at how all individuals or groups responded to each question or topic, or for a given time period or event. This is often done with open-ended questions. You organize the data by question to look across all respondents and their answers in order to identify consistencies and differences. You put all the data from each question together.
You can apply the same approach to particular topics, or a time period or an event of interest. Later, you may explore the connections and relationships between questions (topics, time periods, events).
Focus by case, individual or group.
You may want an overall picture of:
One case such as one family or one agency. One individual such as a first-time or teen participant in the program. One group such as all first-time participants in the program, or all teens ages 13 to 18. Rather than grouping these respondents' answers by question or topic, you organize the data from or about the case, individual or group, and analyze it as a whole. Or you may want to combine these approaches and analyze the data both by question and by case, individual or group.
Step 3 Categorize information:
Some people refer to categorizing information as coding the data or indexing the data. However, categorizing does not involve assigning numerical codes as you do in quantitative analysis where you label exclusive variables with preset codes or values. To bring meaning to the words before you:
Identify themes or patterns - ideas, concepts, behaviors, interactions, incidents, terminology or phrases used.
Organize them into coherent categories that summarize and bring meaning to the text. This can be fairly labor-intensive depending on the amount of data you have. But this is the crux of qualitative analysis. It involves reading and re-reading the text and identifying coherent categories. You may want to assign abbreviated codes of a few letters, words or symbols and place them next to the themes and ideas you find. This will help organize the data into categories. Provide a descriptive label (name) for each category you create. Be clear about what you include in the category and what you exclude. As you categorize the data, you might identify other themes that serve as subcategories. Continue to categorize until you have identified and labeled all relevant themes. The following examples show categories that were identified to sort responses to the questions.
ANALYZING QUALITATIVE DATA:
Coding Responses to the question were sorted into similar and different content areas with identified themes
1. What makes a quality educational program?
Staff (Stf), relevance (Rel), participation (Part), timeliness (Time), content (Con)
2. What is the benefit of a youth mentoring program?
Benefits to youth (Y), benefits to mentor (M), benefits to family (Fam), benefits to community (Comm)
3. What do you need to continue your learning about evaluation?
Practice (P), additional training (Trg), time (T), resources (R), feedback (Fdbk), mentor (M), uncertain (U)
Step 4 Identify patterns and connections within and between categories
As you organize the data into categories - either by question or by case - you will begin to see patterns and connections both within and between the categories. Assessing the relative importance of different themes or highlighting subtle variations may be important to your analysis. Here are some ways to do this.
Within category description
You may be interested in summarizing the information pertaining to one theme, or capturing the similarities or differences in people's responses within a category. To do this, you need to assemble all the data pertaining to the particular theme (category).
What are the key ideas being expressed within the category? What are the similarities and differences in the way people responded, including the subtle variations? It is helpful to write a summary for each category that describes these points.
Larger categories
You may wish to create larger super categories that combine several categories. You can work up from more specific categories to larger ideas and concepts. Then you can see how the parts relate to the whole.
Relative importance
To show which categories appear more important, you may wish to count the number of times a particular theme comes up, or the number of unique respondents who refer to certain themes. These counts provide a very rough estimate of relative importance. They are not suited to statistical analysis, but they can reveal general patterns in the data.
Relationships
You also may discover that two or more themes occur together consistently in the data. Whenever you find one, you find the other. For example, youth with divorced parents consistently list friendship as the primary benefit of the mentoring program.
You may decide that some of these connections suggest a cause and effect relationship, or create a sequence through time. For example, respondents may link improved school performance to a good mentor relationship. From this, you might argue that good mentoring causes improved school performance.
Such connections are important to look for, because they can help explain why something occurs. But be careful about simple cause and effect interpretations. Seldom is human behavior or narrative data so simple.
Ask yourself: How do things relate? What data support this interpretation? What other factors may be contributing?
You may wish to 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, you learn a great deal from looking at and trying to understand items that do not fit into your categorization scheme.
Step 5 Interpretation - Bringing it all together
Use your themes and connections to explain your 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 what we call interpreting the data - attaching meaning and significance to the analysis. A good place to start is to develop a list of key points or important findings you discovered as a result of categorizing and sorting your data.
Stand back and think about what you have learned. What are the major lessons? What new things did you learn? What has application to other settings, programs, studies? What will those who use the results of the evaluation be most interested in knowing?
Too often, we list the findings without synthesizing them and tapping their meaning. Develop an outline for presenting your results to other people or for writing a final report. The length and format of your report will depend on your audience. It is often helpful to include quotes or descriptive examples to illustrate your points and bring the data to life. 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 your investigation and connections that remain unclear. These may be areas where you can suggest further study.
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