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Dissertation and Applied Doctoral Project Components

Qualitative Data Analysis Software, Tools, and Resources

Data Collection and Analysis Software Tools

The links below are to additional free/open-source or proprietary software that students may find useful.

For additional resources on QUALITATIVE data analysis:

Analyzing Your Dissertation’s Qualitative Data

National University GSSC. (2024, February 26). Analyzing your dissertation's qualitative data [Video file].

For additional assistance with QUALITATIVE data analysis, consider attending the Academic Success Center's group sessions:

Analysis and Coding Example: Qualitative Data

The following is an example of how to engage in a three-step analytic process of coding, categorizing, and identifying themes within the data presented. Note that different researchers would come up with different results based on their specific research questions, literature review findings, and theoretical perspective.

There are many ways cited in the literature to analyze qualitative data. The specific analytic plan in this exercise involved a constant comparative (Glaser & Strauss, 1967) approach that included a three-step process of open coding, categorizing, and synthesizing themes. The constant comparative process involved thinking about how these comments were interrelated. Intertwined within this three-step process, this example engages in content analysis techniques as described by Patton (1987) through which coherent and salient themes and patterns are identified throughout the data. This is reflected in the congruencies and incongruencies reflected in the memos and relational matrix.

Step 1: Open Coding

Codes for the qualitative data are created through a line-by-line analysis of the comments. Codes would be based on the research questions, literature review, and theoretical perspective articulated. Numbering the lines is helpful so that the researcher can make notes regarding which comments they might like to quote in their report.

It is also useful to include memos to remind yourself of what you were thinking and allow you to reflect on the initial interpretations as you engage in the next two analytic steps. In addition, memos will be a reminder of issues that need to be addressed if there is an opportunity for follow-up data collection. This technique allows the researcher time to reflect on how his/her biases might affect the analysis. Using different colored text for memos makes it easy to differentiate thoughts from the data.

Many novice researchers forgo this step.  Rather, they move right into arranging the entire statements into the various categories that have been pre-identified. There are two problems with the process. First, since the categories have been listed through open coding, it is unclear where the categories have been derived. Rather, when a researcher uses the open coding process, he/she look at each line of text individually and without consideration for the others. This process of breaking the pieces down and then putting them back together through analysis ensures that the researcher considers all the data equally and limits the bias that might be introduced. In addition, if a researcher is coding interviews or other significant amounts of qualitative data it will likely become overwhelming as the researcher tries to organize and remember from which context each piece of data came.

 

Data

Code
  1. There is not enough space in the building.

Building
  1. Our desks and equipment are dated and need replacing.

Resources, Modernization, Resources
  1. We need a better cleaning service for the building.

Services, Building
  1. We need more focused recruitment and hiring standards for teachers.

Instructional Quality
  1. We need objective observations and feedback from the principal.

Leadership Interaction, Support, Evaluation
  1. We need consistent decisions about school rules.

Uncertainty, Decision Making, Responsibilities
  1. There are leadership problems. Memo: I’m unclear about what this is. This could be about communications, requirements or interaction. What is meant by problems? How does this fit in with the other leadership concerns?

 
  1. Nonproductive staff members should not be retained.

Responsibilities, Equity
  1. Each team of teachers has stereotypes about the other teams.

Conflict, Lack of Data
  1. Decisions are made on inaccurate information.

Decision Making, Responsibilities
  1. We need more opportunities to shift teaching assignments.

Lack of Data, Responsibilities
  1. Our students are confused by different teacher styles and expectations.

Focus on Students, Quality Instruction
  1. There is too much gossiping and criticizing.

Conflict
  1. We are not sure who is teaching which topics.

Uncertainty, Instructional Clarification.
  1. We need a suggestion box. Memo: I think that this falls under the category of decision-making. What the statement seems to be saying is that this participant wants more opportunities to share her thoughts. In essence, then, she will become part of the decision-making process.

Decision Making
  1. We need more computer terminals.

Technology Resources
  1. There is a division between the old and new teachers. Memo: This not only indicates conflict but might be based on perspective. Is there a duality between the new and veteran teachers? Is this where some of the conflict lies? Are there underlying issues like pay or respect?

Conflict, New versus Veteran
  1. There is a lack of support for individual teachers.

Support
  1. There is favoritism and preferential treatment for some staff. Memo: Back to line 23. Could this idea have support with this statement????

Conflict
  1. We all need more content training, especially about reading instruction.

Quality Instruction
  1. We a need better assessment of teachers so assignments are more objectively based. Memo: There seems to be an emerging pattern here that there is an issue. Perhaps this belief is trying to get at the issue of some teachers commitment, pay, perks, respect, etc.

Support, Evaluation, New versus Veteran
  1. Training is needed for new teachers.

Quality Instruction, New versus Veteran
  1. Some teachers are carrying the weight for other teachers.

Inequities
  1. This school is very “turf” oriented.

Conlfict
  1. There are a few “favorites” at each grade level.

Respect
  1. Communication needs improving. Memo: Another general statement that could be about leadership, peer interaction, or other issues.

 
  1. Certain grade levels are put on a pedestal.

Equality
  1. There are too many tests and assessments required.

Quality Instruction, Requirements
  1. Too much red tape is needed to get our supplies. Memo: I coded this as respect because I felt that the comment had to do with trust and autonomy.

Respect, Resources
  1. Teachers are often teaching the same things to different grades.

Requirements, Quality Instruction
  1. Grade level teams work against each other rather than together.

Inequities, Conflict

 

Step 2: Categorizing

To categorize the codes developed in Step 1, list the codes and group them by similarity. Then, identify an appropriate label for each group. The following table reflects the result of this activity.

Category

Codes

Physical Surroundings

Building

Resources Modernization Services

Technology Resources

Instruction

Instructional Quality

Requirements

Communication

Uncertainty

Decision Making

Conflict

Lack of Data

Instructional Clarification

Peer Interaction

Responsibilities

Equity

Conflict

New versus Veteran

Respect

Leadership

Interaction

Support

Evaluation
Step 3: Identification of Themes

In this step, review the categories as well as the memos to determine the themes that emerge. In the discussion below, three themes emerged from the synthesis of the categories. Relevant quotes from the data are included that exemplify the essence of the themes. These can be used in the discussion of findings. The relational matrix demonstrates the pattern of thinking of the researcher as they engaged in this step in the analysis. This is similar to an axial coding strategy.

Note that this set of data is limited and leaves some questions in mind. In a well-developed study, this would just be a part of the data collected and there would be other data sets and/or opportunities to clarify/verify some of the interpretations made below.  In addition, since there is no literature review or theoretical statement, there are no reference points from which to draw inferences in the data. Some assumptions were made for the purposes of this demonstration in these areas.

 Theme 1:  Professional Standing 

Individual participants have articulated issues related to their own professional position. They are concerned about what and when they will teach, their performance, and the respect/prestige that they have within the school. For example, they are concerned about both their physical environment and the steps that they have to take to ensure that they have the up-to-date tools that they need. They are also concerned that their efforts are being acknowledged, sometimes in relation to their peers and their beliefs that they are more effective.

Selected quotes:

  • Some teachers are carrying the weight for other teachers (demonstrating that they think that some of their peers are not qualified).
  • We need objective observations and feedback from the principal (demonstrating that they are looking for acknowledgement for their efforts  or this could be interpreted as a belief that their peers who are less qualified should be acknowledged).
  • There is a lack of support for individual teachers.

Theme 2:  Group Dynamics and Collegiality

Rationale: There are groups or clicks that have formed. This seems to be the basis for some of the conflict. This conflict is closely related to the status and professional standing themes. This theme, however, has more to do with the group issues, while the first theme is an individual perspective. Some teachers and/or subjects are seen as more prestigious than others. Some of this is related to longevity. This creates jealousy and inhibits collegiality. This affects peer interaction, instruction, and communication.

Selected quotes:

  • Grade-level teams work against each other rather than together.
  • Each team of teachers has stereotypes about the other teams.
  • There is a division between the old and new teachers.

Theme 3:  Leadership Issues

Rationale: There seems to be a lack of leadership and shared understanding of the general direction in which the school will go. This is also reflected in a lack of two-way communication. There doesn’t seem to be information being offered by the leadership of the school, nor does there seem to be an opportunity for individuals to share their thoughts, let alone decision-making. There seems to be a lack of intervention in the conflict from leadership.

Selected quotes:

  • Decisions are made on inaccurate information.
  • We need consistent decisions about school rules.

Coding Example - Category - Relationships - Themes

References

Glaser, B.G., & Strauss, A. (1967). The discovery of grounded theory: Strategies for qualitative research. Chicago, IL: Aldine.

Patton, M. Q. (1987). How to use qualitative methods in evaluation. Newbury Park, CA: Sage Publications.

Trustworthiness of Qualitative Data

As noted in the DSE and ADE templates for qualitative studies, the section directly following the Chapter 4 or Section 3 introduction is to be labeled Trustworthiness of the Data, and in this section, qualitative researchers are required to articulate evidence of four primary criteria to ensure trustworthiness of the final study data set:

Credibility (e.g., triangulation, member checks)

Credibility of qualitative data can be assured through multiple perspectives throughout data collection to ensure data are appropriate. This may be done through data, investigator, or theoretical triangulation; participant validation or member checks; or the rigorous techniques used to gather the data.

Transferability (e.g., the extent to which the findings are generalizable to other situations)

Generalizability is not expected in qualitative research, so transferability of qualitative data assures the study findings are applicable to similar settings or individuals. Transferability can be demonstrated by clear assumptions and contextual inferences of the research setting and participants.

Dependability (e.g., an in-depth description of the methodology and design to allow the study to be repeated)

Dependability of the qualitative data is demonstrated through assurances that the findings were established despite any changes within the research setting or participants during data collection. Again, rigorous data collection techniques and procedures can assure the dependability of the final data set.

Confirmability (e.g., the steps to ensure that the data and findings are not due to participant and/or researcher bias)

Confirmability of qualitative data is assured when data are checked and rechecked throughout data collection and analysis to ensure results would likely be repeatable by others. This can be documented by a clear coding schema that identifies the codes and patterns in the analyses. Finally, a data audit prior to analysis can also ensure dependability.

For more information on these criteria, visit the Sage Research Methods database in the NU Library: https://resources.nu.edu/sagerm

NVivo

Students conducting qualitative studies can use NVivo software to analyze data. The Chair will guide students who may need alternative software for their data analysis software. NU provides students NVivo software for free in the Student Technology Resource Center. The Student Technology Resource Center is located in University Services. Find University Services in your course drop-down list in NCUOne.

Note: Doctoral students taking courses in NU Brightspace can find directions on how to download NVivo in the NU Library Connection. Find the NU Library Connection in your course list and then navigate to the Data Analysis Software for Doctoral Students sub-module within the Research Help module.

You can also visit the ASC's resources on NVivo for more information on how to use the software to create projects, import data, create coes, and manage and edit codes.

NVivo Basics: A Strong Start for Qualitative Data Analysis

National University GSSC. (2023, June 1). NVivo basics: A strong start for qualitative data analysis [Video file].