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Analyze Qualitative Data

Qualitative data analysis involves the identification, examination, and interpretation of patterns and themes in textual data and determines how these patterns and themes help answer the research questions at hand.

Qualitative analysis is (NSF, 1997):

  • Not guided by universal rules
  • Is a very fluid process that is highly dependent on the evaluator and the context of the study
  • Likely to change and adapt as the study evolves and the data emerges.

Therefore, this section will provide a loosely structured guide for the steps you should take when analyzing qualitative data.

It is important to note that qualitative data analysis is an ongoing, fluid, and cyclical process that happens throughout the data collection stage of your evaluation project and carries over to the data entry and analysis stages. Although the steps listed below are somewhat sequential they do not always (and sometimes should not) happen in isolation of each other.

As you move between and within the steps of analysis it is important to always keep some guiding questions in mind that will help you reflect back on the study’s purpose, research questions, and potential. Figure 7-3 lists examples of types of questions you should ask yourself throughout the analysis process. Additionally, you may want to click on the following link for additional information on qualitative data analysis:

Questions to Ask Yourself throughout the Qualitative Analysis Process

While analyzing your qualitative data it is important that you continuously ask yourself the following types of questions:

  • What patterns/common themes emerge around specific items in the data?  
    • How do these patterns (or lack thereof) help to shed light on the broader study question(s)?
  • Are there any deviations from these patterns?
    • If, yes, what factors could explain these atypical responses?
  • What interesting stories emerge from the data?
    • How can these stories help to shed light on the broader study question?
  • Do any of the patterns/emergent themes suggest that additional data needs to be collected?
    • Do any of they study questions need to be revised?
  • Do the patterns that emerge support the findings of other corresponding qualitative analyses that have been conducted?
This figure was adapted from the National Science Foundations’s (1997) Analyzing Qualitative Data. Chapter 4 in User Friendly Handbook for Mixed Methods Evaluations.


Process and Record Data Immediately

As soon as data is collected it is critical that you immediately process the information and record detailed notes.

These notes could include:

  • Things that stuck out to you
  • Time/date details
  • Other observations
  • Highlights from the interaction

It is important to do this while the interaction is still fresh in your mind so that you can record your thoughts and reactions as accurately as possible.

  • It is helpful to make a reflection sheet template that you carry with you and complete after each interaction so that it is standardized across all data collection points.

Begin Analyzing as Data is Being Collected

Qualitative data analysis should begin as soon as you begin collecting the first piece of information.

  • The moment the first pieces of data are collected you should begin reviewing the data and mentally processing it for themes or patterns that were exhibited. It is important to do this early so that you will be focused on these patterns and themes as they appear in subsequent data you collect.

Data Reduction

Qualitative studies generally produce a wealth of data but not all of it is meaningful. After data has been collected, you will need to undergo a data reduction process in order to identify and focus in on what is meaningful. This is the process of reducing and transforming your raw data.

It is your job as the evaluator to comb through the raw data to determine what is significant and transform the data into a simplified format that can be understood in the context of the research questions (Krathwohl, 1998; Miles and Huberman, 1994; NSF, 1997). When trying to discern what is meaningful data you should always refer back to your research questions and use them as your framework. Additionally, you should rely on your own intuition as the evaluator and the expertise of other individuals with a thorough understanding of the program.

This step does not happen in isolation, it naturally occurs during the first two steps. You are already reducing data by not recording every single thing that occurs in your data collection interaction but only recording what you felt was most meaningful, usable, and relevant. You are also reducing data by looking for themes from the beginning. This process helps you hone in on specific patterns and themes of interest while not focusing on other aspects of the data.

The process of data reduction, however, must go beyond the data collection stage. Evaluators must take time to carefully review all of the data you have collected as a whole.

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:

  • Content analysis
  • 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).

Data Display

After identifying themes or content patterns, assemble, organize, and compress the data into a display that facilitates conclusion drawing. The display can be a graphic, table/matrix, or textual display.

  • Regardless of what format you chose, it should be able to help you arrange and think about the data in new ways and assist you in identifying systematic patterns and interrelationships across themes and/or content (Miles and Huberman, 1994; NSF, 1997).
  • Through this process you should be able to identify patterns and relationships observed within groups and across groups. For example, using our Summer Program study, you could examine patterns and themes both within a program city and across program cities.

Conclusion Drawing and Verification

Conclusion drawing and verification are the final step in qualitative data analysis.

To draw reasonable conclusions, you wil need to (Krathwohl, 1998; Miles and Huberman, 1994; NSF, 1997):

  • Step back and interpret what all of your findings mean
  • Determine how your findings help answer the research question(s)
  • Draw implications from your findings

To verify these conclusions, you must revisit the data (multiple times) to confirm the conclusions that you have drawn.