Part 4: Using qualitative methods

19. A survey of approaches to qualitative data analysis

Just a brief disclaimer, this chapter is not intended to be a comprehensive resource on qualitative data analysis. It does offer an overview of some of the diverse approaches that can be used for qualitative data analysis, but as you will read, even within each one of these approaches there is variation in how they might be implemented in a given project. If you are passionate (or at least curious 😊) about conducting qualitative research, use this as a starting point to help you dive deeper into some of these strategies. Before we begin reviewing some of these strategies, here a few considerations regarding ethics, cultural responsibility, power and control that should influence your thinking and planning as you map out your data analysis plan.

19.1 Ethical Responsibility and Cultural Respectfulness

Learning Objectives

Learner will be able to:

  • Identify how researchers can conduct ethically responsible qualitative data analysis.
  • Explain the role of culture and cultural context in qualitative data analysis (for both researcher and participant)

The ethics of deconstructing stories. Throughout this chapter, I will consistently suggest that you will be ‘deconstructing’ data.  That is to say, you will be taking the information that participants share with you through their words, performances, videos, documents, photos, and artwork, then breaking it up into smaller points of data, which you will then reassemble into your findings.  We have an ethical responsibility to treat what is shared with a sense of respect during this process of deconstruction and reconstruction. This means that we make conscientious efforts not to twist, change, or subvert the meaning of bits of data as we break them down or string them back together.  The act of bringing together people’s stories through qualitative research is not an easy one and shouldn’t be taken lightly.  Through the informed consent process, participants should learn about the ways in which their information will be used in your research, including giving them a general idea what will happen in your analysis and what the end results of that process are.

A deep understanding of cultural context as we make sense of meaning. Related to the preceding discussion about deconstructing stories, as we conduct our qualitative analysis, we need to work diligently to understand the cultural context in which these stories are shared. This requires that we approach the task of analysis with a sense of cultural humility, meaning that we don’t assume that our perspective or worldview as the researcher is the same as our participants. Their life experiences may be quite different from our own, and because of this, the meaning in their stories may be very different than what we might initially expect.

Example. I was conducting an interview a few years ago and the interviewee kept mentioning a healthcare clinic and how much they liked receiving services there. I had assumed this was because they received good care or that the providers were friendly. However, I was glad I followed up during the interview. At one point I asked, “I noticed you mentioned this clinic a number of times as we’ve talked. It seems pretty special. What is it about this clinic?” The interviewee paused and thought, and then responded back, “When I go there, I feel loved”. It blew me away. I would have never really associated the emotion of love with a healthcare clinic until I asked.  My own assumptions and experiences of having received care, being trained as a healthcare professional, or being a white man could have interfered with me understanding what this person was sharing with me. 

As such, we need to ask questions to better understand words, phrases, ideas, gestures, etc. that seem to have particular significance to participants.  We also can use activities like member checking, another tool to support qualitative rigor, to ensure that our findings are accurately interpreted by vetting them with participants prior to the study conclusion. Finally, we can spend a good amount of time getting to know the groups and communities that we work with, paying attention to their values, priorities, practices, norms, strengths, and challenges.

Accounting for our influence in the analysis process. Along with our ethical responsibility to our research participants, we also have an accountability to research consumers, the scientific community at large, and other stakeholders in our qualitative research. As qualitative researchers (or quantitative researchers, for that matter), people should expect that we have attempted, to the best of our abilities, to account for our role in the research process. This is especially true in analysis.  Our finding should not emerge from some ‘black box’, where raw data goes in and findings pop out the other side, with no indication of how we arrive at them. Thus, an important part of rigor is transparency and the use of tools such as writing in reflective journals, memoing, and creating an audit trail to assist us in documenting both our thought process and activities in reaching our findings. There will be more about this in the chapter dedicated to qualitative rigor.

19.2 Critical Considerations

Learning Objectives

Learners will be able to:

  • Explain how data analysis may be used as tool for power and control
  • Develop steps that reflect increased opportunities for empowerment of your study population, especially during the data analysis phase

How are participants present in the analysis process? What power or influence do they have? We need to consider that our findings represent ideas that are shared with us by living and breathing human beings.  They have been gracious enough to share their time and their stories with us, yet they often have a limited role once we gather data from them.  They are essentially putting their trust in us that we won’t be misrepresenting or culturally appropriating their stories in ways that will be harmful, damaging, or demeaning.

Now, some research approaches, particular participatory approaches, suggest that participants should be trained and actively engaged throughout the research process. This, however, is the exception, not the rule. As such, whenever possible, it is good practice to find ways to allow participants or other community representatives to help lend validation to our findings.  You may do this through activities like consulting with community representatives early and often during the analysis process and using (referenced above and in our chapter on qualitative rigor) to help review and refine results. These are distinct and important roles for the community and do not mean that community members becomes researchers; but that they lend their perspectives in helping the researcher to interpret their findings.

The bringing together of many voices: What does this represent and to whom? Another consideration from a critical perspective is that we are usually not just using data from one person, but we are combining their data with the data of many other participants. In our attempt to tell a broader story that incorporates the experiences of many, it’s helpful to consider how our results might be perceived by participants.  While they might be (and often are) excited to be part of a larger discussion on our research topic, they also might be concerned what other people might say and how this could impact their community.  To address this, it is especially important to educate our participants on what the research will involve and that we thoughtfully consider what risks may be involved, not just to the individual.  This should be done early on in the research process, during recruitment efforts and in the informed consent process.

19.3 Preparations: Creating a Plan for Qualitative Data Analysis

Learning Objectives

Learners will be able to:

  • Identify how your research question, research aim, sample selection, and type of data may influence your choice of analytic methods
  • Outline the steps you will take in preparation for conducting qualitative data analysis in your proposal

What is your goal with this data? Now we can turn our attention to planning our analysis. The question posed at the beginning of this paragraph is a good place to begin. Think about the reasons for selecting a qualitative approach, as well as your ultimate purpose for conducting your research. Some qualitative research is looking to find commonalities, some seeks to describe a range or diversity of perspectives, while other inquiries try to understand how something works or how decisions are made.  This is only a small sampling of what you might be trying to accomplish with your qualitative study.  Whatever your aim, you need to have a plan for what you will do when the data starts coming in.

Developing your Qualitative Research Protocol

Decision Point: What are you trying to accomplish with your data?

  • Thinking about your research question, can you state the aim of your study? What do you need to do with the qualitative data you are gathering to answer the question?

To help answer this question, consider:

    • Think about action verb(s) associated with your project and the qualitative data you are collecting. Does your research aim to summarize, compare, describe, examine, outline, identify, review, compose, develop, illustrate, etc.?
    • Then, think about the noun(s) you need to pair with your verb(s) – perceptions, experiences, thoughts, reactions, descriptions, understanding, processes, feelings, actions responses, etc.

 

Iterative or linear? We touched on this briefly in our chapter about qualitative sampling, but this is an important distinction to consider.  Some qualitative research is , meaning it follows more of a traditionally quantitative process: create a plan, gather data, and analyze data; each step is completed before we proceed to the next.

Example. I’ve recently been working on a project where my research team conducted a number of focus groups. We used a generally linear approach.  First, we planned our study and got it approved. Then we arranged and conducted our focus groups. We then proceeded to transcribe all our data and went on to analyze it.  We didn’t start our analysis until all we had all our data in front of us.   

However, many times qualitative research is , or evolving in cycles.  An iterative approach means that after planning and once we begin collecting data, we begin analyzing data as it is coming in.  This early and ongoing analysis of our (incomplete) data then impacts our planning, continued data gathering and future analyses.

Example. To demonstrate an iterative approach, let’s say we are conducting interviews. After we have completed our first three interviews, we sit down and do some preliminary analyses and identify some early themes that seem important. In our next interviews, we add a couple questions based on these themes to explore these ideas with new participants. These new participants agree that these topics are important, but they have a different take on them, and share their unique experiences with us.  This informs a new understanding of the data we are gathering. This cycle continues on until we reach a point where our interviews aren’t producing new ideas (the point of saturation) and we arrive at a more complete understanding of what our ultimate findings are. If we are utilizing an iterative approach like this, it also means that as new ideas emerge later on in the process, we need to go back to data that was collected earlier and see if there may have been previous evidence of those ideas that may have been missed, now that our understanding has evolved.

As you may have guessed, there are benefits and challenges to both linear and iterative approaches.  A linear approach is much more straightforward, each step being fairly defined.  However, linear research, with is more defined nature and rigid approach, also presents certain challenges. A linear approach assumes that we know what we need to ask or look for at the very beginning of data collection, which often is not the case.

With iterative research we have more flexibility to adapt our approach as we learn new things. We still need to keep our approach systematic and organized, however, so that our work doesn’t become a free-for-all. As we adapt, we do not want to stray too far from the original premise of our study. It’s also important to remember with an iterative approach that we may risk ethical concerns if our work extend beyond the original boundaries of our informed consent and IRB agreement.

Developing your Qualitative Research Protocol

Decision Point: Will your analysis reflect more of a linear or an iterative approach?

    • What justifies or supports this decision?
      • Fit with your research question
      • Available time and resources
      • Your knowledge and understanding of the research process

 

Reflexive Journal Entry Prompt

  • Are you more of a linear thinker or an iterative thinker?
    • What evidence are you basing this on?
  • How might this help or hinder your qualitative research process?

Acquainting yourself with your data. As you begin your analysis, you need to get to know your data. This usually means reading through your data prior to any attempt at breaking it apart and labeling it. You might read through a couple times, in fact.  This helps give you a more comprehensive feel for each piece of data and the data as a whole, again, before you start to break it down into smaller units or deconstruct it. This is especially important if others assisted us in the data collection process. We often gather data as part of team and everyone involved in the analysis needs to be very familiar with all of the data. 

Capturing your reaction to the data. During the review and memoing process , our understanding of the data often evolves as we observe patterns and trends. It is a good practice to document your reaction and evolving understanding. Your reaction can include noting phrases or ideas that surprise you, similarities or distinct differences in responses, additional questions that the data provokes in you, among other things. We often record these reactions directly in the text or artifact if we have the ability to do so, such as making a comment in a word document associated with a highlighted  phrase. If this isn’t possible, you will want to have a way to track what specific spot(s) in your data your reactions are referring to.  In qualitative research we refer to this process as .  Memoing is a strategy that helps us to link our findings to our raw data, demonstrating transparency. If you are using a CAQDAS software package, memoing functions are generally built into the technology.

Capturing your emerging understanding of the data. During your reviewing and memoing you will start to develop and evolve your understanding of the data.  This understanding should be dynamic and flexible, but you want to have a way to capture this understanding as it evolves.  You may include this as part of your memoing or as part of your codebook where you are tracking the main ideas that are emerging and what they mean. Below is an example of how your thinking might change about a code and how you can go about capturing it.

Developing your Qualitative Research Protocol

Decision Point: How to capture your thoughts?

  • How will you capture your thinking about the data and your emerging understanding about what it means?
    • What will this look like?
    • How often will you do it?
    • How will you keep it organized and consistent over time?

In addition, you will want to be actively using your reflexive journal during this time. Document your thoughts and feelings throughout the research process. This will promote transparency and help help account for your role in the analysis.

Reflexive Journal Entry Prompt

For entries during your analysis, respond to questions such as these in your journal:

  • What surprises you about what participants are sharing?
  • How has this information challenged you to look at this topic differently?
  • As you reflect on these findings, what personal biases or preconceived notions have been exposed for you?
    • Where might these have come from?
    • How might these be influencing your study?
  • How will you proceed differently based on what you are learning?

Determining when you are finished. If you are looking for a hard and fast rule as to when you have completed your data analysis, I’m going to let you down – sorry!  Because each of the approaches we will be discussing (i.e. thematic analysis, content analysis, grounded theory analysis, and photovoice analysis) are trying to accomplish slightly different things, e.g. converging on themes, developing a theory, merging narrative and visual explanations, they provide different guidance as to when your work is complete. Generally speaking, here are a few broad considerations you may want to take into consideration:

Have I utilized all my data? Unless you have intentionally made the decision that certain portions of your data are not relevant for your study, make sure that you don’t have sources or segments of data that aren’t incorporated into your analysis. Just because some data doesn’t “fit” the general trends you are uncovering, find a way to acknowledge this in your findings as well so that these voices don’t get lost in your data.
Have I fulfilled my obligation to my participants? As a qualitative researcher, you are a craftsperson. You are taking raw materials (e.g. people’s words, observations, photos) and bringing them together to form a new creation, your findings. These findings need to both honor the original integrity of the data that is shared with you, but also help tell a broader story that answers your research question(s).
Have I fulfilled my obligation to my audience? Not only do your findings need to help answer your research question, but they need to do so in a way that is consumable for your audience. From an analysis standpoint, this means that we need to make sufficient efforts to condense our data. For example, if you are conducting a thematic analysis, you don’t want to wind up with 20 themes. Having this many themes suggests that you aren’t finished looking at how these ideas relate to each other and might be combined into broader themes. Having these sufficiently reduced to a handful of themes will help tell a more complete story, one that is also much more approachable and meaningful for your reader.

In the following subsections, there is information regarding a variety of different approaches to qualitative analysis.  In designing your qualitative study, you would identify an analytical approach as you plan out your project.  The one you select would depend on the type of data you have and what you want to accomplish with it.

Developing your Qualitative Research Protocol

Decision Point: When will you stop?

  • How will you know when you are finished? What will determine your end point?
  • How will you monitor this?

19.4 Thematic Analysis

Learning Objectives

Learners will be able to:

  • Explain defining features of thematic analysis as a strategy for qualitative data analysis and identify when it is most effectively used
  • Formulate an initial thematic analysis plan (if appropriate for your research proposal)

What are you trying to accomplish with Thematic Analysis? As its name might suggest, with this approach you are attempting to identify themes or common ideas across your data. Let’s say that you are studying empowerment of older adults in assisted living facilities by interviewing residents in a number of these facilities. As you review your transcripts, you note that a number of participants are talking about the importance of maintaining connection to previous aspects of their life (e.g. their mosque, their VFW Post, their Queer book club) and having input into how the facility is run (e.g. representative on the board, community town hall meetings). You might note that these are two emerging themes in your data.  After you have deconstructed you data, you will likely end up with a handful (likely three or four) central ideas or take-aways that become the themes or major findings of your research.

Variations in approaches to thematic analysis. There are a variety of ways to approach qualitative data analysis, but even within the broad approach of thematic analysis, there is variation.  Some thematic analysis takes on an inductive approach.  In this case, we will first deconstruct our data into small segments representing distinct ideas (this is explained further in the section below on coding data).  We then go on to see which of these pieces seem to group together around common ideas.

In direct contrast, you might take a deductive approach (like we discussed in Chapter 6), in which you start with some idea about what grouping might look like and we see how well our data fits into those pre-identified groupings.  These initial deductive groupings (we call these a priori categories) often come from an existing theory related to the topic we are studying. You may also elect to use a combination of deductive and inductive strategies, especially if you find that much of your data is not fitting into deductive categories and you decide to let new categories inductively emerge.

A couple things to note here.  If you are using a deductive approach, be clear in specifying where your came from. For instance, perhaps you are interested in studying the conceptualization of social work in other cultures. You begin your analysis with prior research conducted by Tracie Mafile’o (2004) that identified the concepts of fekau’aki (connecting) and fakatokilalo (humility) as being central to Tongan social work practice.[1] You decide to use these two concepts as part of your initial deductive framework, because you are interested in studying a population that shares much in common with the Tongan people. When using an inductive approach, you need to plan to use memoing and journaling to document where the new categories or themes are coming from.

Coding Data. Coding is the process of breaking down your data into smaller meaningful units. Just like any story is made up by the bringing together of many smaller ideas, you need to uncover and label these smaller ideas within each piece of your data. After you have reviewed each piece of data you will go back and assign labels to words, phrases, or pieces of data that represent separate ideas that can stand on their own. When attempting to locate units of data to code, look for pieces of data that seem to represent an idea in-and-of-itself; a unique thought that stands alone. The following brief video from Duke’s Social Science Research Institute is a nice concise overview of coding and also ties into our previous discussion of memoing to help encourage rigor in your analysis process.

You might do the work of coding in the margins if you are working with hard copies, or you might do this through the use of comments or through copying and pasting if you are working with digital materials. If you are using a CAQDAS, there will be a function(s) built into the software to accomplish this.

Regardless of which strategy you use, you need to have a way to label discrete segments of your data with a short phrase that reflects what it stands for.  As you come across segments that seem to mean the same thing, you will want to use the same code. Make sure to select the words to represent your codes wisely, so that they are clear and memorable.  When you are finished, you will likely have hundreds (if not thousands!) of different codes – again, a story is made up of many different ideas and you are bringing together many different stories! A cautionary note, if you are physically manipulating your data in some way, for example copying and pasting, which I frequently do, you need to have a way to trace each code or little segment back to its original home (the artifact that it came from).

Example. When I’m working with interview data, I will assign each interview transcript a code and use continuous line numbering. That way I can label each segment of data or code with a corresponding transcript code and line number so I can find where it came from in case I need to refer back to the original. The following is an excerpt from a PBS interview transcript with Dr. Susan Hanks.  Continuous numbers have been added to the transcript to identify line numbers.  A few preliminary codes have been identified from this data and entered into a (below) with information to trace back to the raw data (transcript).

Activity: Practice with Coding Data

Below is another excerpt from the same interview with Dr. Hanks. What segments of this interview can you pull out and what initial code would you place on them?

Q: Do you see a profile of men who batter women?

There is no one profile of men who batter the woman with whom they live, or their significant female partner. There are many differential characteristics. As I said previously, some men batter because of an overwhelming life circumstance, but do not, or in response to that such as a major illness, a financial setback, the death of a child, death of a parent, and their violence is not an ongoing pattern within their relationships. However, many men who batter and we know of in ten percent of all relationships in the United States there is severe frequent, ongoing battery, psychological, physical, verbal battery of women. In those situations men batter often because they have psychological struggles internally which they bring home and hope to have resolved within the context of the relationship.

Many men batter because they feel, because they are tremendously dependent on the woman, because they depend on her to keep a stable sense of self-esteem for themselves, because they feel that they can’t survive without her, because they are threatened by her moves towards any kind of individual life of her own or individual thinking of her own. Some men batter because that’s the only way they know how to be close. We know from working with families in which there is spouse abuse, that there is a cycle that, repetitive cycle that happens in many families. And over the course of time, the family cycles through episodes of abuse and families emotional life then revolves around either anticipating an episode of violence actually coping with an episode of violence or recovering from that and oftentimes in the recovery phase of an episode of violence there’s tremendous closeness in the family, or at least a diminution of the anxiety that previously existed. So that sometimes men’s violence is reinforcing because the closeness in the relationship that existed in the beginning of the relationship or the wish for the closeness is actually reestablished after an episode of violence. So it’s paradoxical.

Identifying, reviewing, and refining themes. Now we have our codes, we need to find a sensible way of putting them together. Remember, we want to narrow this vast field of hundreds of codes down to a small handful of themes. If we don’t review and refine all these codes, the story we are trying to tell with our data becomes distracting and diffuse. An example is provided below to demonstrate this process. 

As we refine our thematic analysis, our first step will be to identify groups of codes that hang together or seem to be related. Let’s say you are studying the experience of people who are in a vocational preparation program and you have codes labeled “worrying about paying the bills” and “loss of benefits”.  You might group these codes into a category you label “income & expenses”. 

Code Category Reasoning 
Worrying about paying the bills Income & expenses Seem to be talking about financial stressors  and potential impact on resources
Loss of benefits
As you continue to review your codes, however, you find an additional code that seems to be related to these: “not confident managing money”. You determine that this code seems clearly related to the others, but you no longer feel the label “income & expenses” fits the group, and you relabel this category “financial insecurities”. 
Code Category Reasoning Category Reasoning
Worrying about Paying the bills Income & expenses Seem to be talking about financial stressors and potential impact on resources Financial insecurities Expanded category to also encompass personal  factor- confidence related to issue
Loss of benefits
Not confident managing money

You may review and refine the groups of your codes many times during the course of your analysis, including shifting codes around from one grouping to another as you get a clearer picture of what each of the groups represent. While you are shifting codes and relabeling categories, track this!  A research journal is a good place to do this.  So, as in the example above, you would have a journal entry that explains that you changed the label on the category from “income & expenses” to “financial insecurities” and you would briefly explain why.

Journal Entry                                                                                    Date: 10/04/19                                                                                Changed category [Income & expenses] to [Financial insecurities] to include new code “Not confident managing money” that appears to reflect a personal factor related to the participant’s confidence or personal capability related to the topic.       

Now, eventually you may decide that some of these categories can also be grouped together, but still stand alone as separate ideas.  Continuing with our example above, you have another category labeled “financial potential” that contains codes like “money to do things” and “saving for my future”. You determine that “financial insecurities” and “financial potential” are related, but distinctly different aspects of a broader grouping, which you go on to label “financial considerations”. This broader grouping reflects both the more worrisome or stressful aspects of people’s experiences that you have interviewed, but also the optimism and hope that was reflected related to finances and future work.

Code Category Reasoning Category Reasoning Theme
Worrying about Paying the bills Income & expenses Seem to be talking about financial stressors and potential impact on resources Financial insecurities Expanded category to also encompass personal  factor- confidence related to issue Financial considerations
Loss of benefits
Not confident managing money
Money to do things Financial potential Reflects positive aspects related to earnings
Saving for my future

This broadest grouping then becomes your theme and utilizing the categories and the codes contained therein, you create a description of what each of your themes means based on the data you have collected.

Journal Entry                                                                                    Date: 10/10/19                                                                                  Identified an emerging theme [Financial considerations] that reflects both the concerns reflected under [Financial insecurities] but also the hopes or more positive sentiments related to finances and work [Financial potential] expressed by participants.  As participants prepare to return to work, they appear to experience complex and maybe even conflicting feelings towards how it will impact their finances and what this will mean for their lives.   

Building a thematic representation. However, providing a list of themes may not really tell the whole story of your study. It may fail to explain to your audience how these individual themes relate to each other. A thematic map or thematic array can do just that: provides a visual representation of how each individual category fits with the others. As you build your thematic representation, be thoughtful of how you position each of your themes, as this spatially tells part of the story. You should also make sure that the relationships between the themes represented in your thematic map or array are narratively explained in your text as well.

Below is an illustration of a thematic map shared in an article by Maguire and Delahunt (2017) that walks learners through conducting a thematic analysis, step-by-step[2]. Each of these themes are explained in greater detail in the article, as is their relationship to one another. Additionally, sample quotes from the data that reflected those themes are also included.

 

References for learning more about Thematic Analysis:

Clarke, V. (2017, December 9). What is thematic analysis? [Video file]. Retreived from https://www.youtube.com/watch?v=4voVhTiVydc

Maguire, M., & Delahunt, B. (2017). Doing a thematic analysis: A practical, step-by-step guide for learning and teaching scholars. AISHE-J: The All Ireland Journal of Teaching and Learning in Higher Education9(3).

Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods16(1), 1609406917733847.

The University of Auckland. (n.d.). Thematic analysis: A reflexive approach [Webpage]. Retrieved from https://www.psych.auckland.ac.nz/en/about/our-research/research-groups/thematic-analysis.html

A few exemplars of studies employing Thematic Analysis:

Bastiaensens, S., Van Cleemput, K., Vandebosch, H., Poels, K., DeSmet, A., & De Bourdeaudhuij, I. (2019). “Were you cyberbullied? Let me help you.” Studying adolescents’ online peer support of cyberbullying victims using thematic analysis of online support group Fora. In H. Vandebosch H. and L. Green (Eds.) Narratives in Research and Interventions on Cyberbullying among Young People (pp. 95-112). Cham: Springer.

Borgström, Å., Daneback, K., & Molin, M. (2019). Young people with intellectual disabilities and social media: A literature review and thematic analysis. Scandinavian Journal of Disability Research21(1), 129-140.

Kapoulitsas, M., & Corcoran, T. (2015). Compassion fatigue and resilience: A qualitative analysis of social work practice. Qualitative Social Work14(1), 86-101.

19.5 Content Analysis

Learning Objectives

Learners will be able to:

  • Explain defining features of content analysis as a strategy for analyzing qualitative data
  • Determine when content analysis can be most effectively used
  • Formulate an initial content analysis plan (if appropriate for your research proposal)

What are you trying to accomplish with content analysis?  Much like with thematic analysis, if you elect to use content analysis to analyze your qualitative data, you will be deconstructing the artifacts that you have sampled and looking for similarities across these deconstructed parts. Also consistent with thematic analysis, you will be seeking to bring together these similarities in your discussion of your findings to tell a collective story of what you learned across your data.  While the distinction between thematic analysis and content analysis is somewhat murky, if you are looking to distinguish between the two, content analysis:

  1. Places a greater emphasis on determining unit of analysis. When conducting a content analysis you will want to first determine what your unit of analysis may be. A unit of analysis is the ‘chunk’ or segment of data you will be looking at to reflect a particular idea. This may be a line, a paragraph, a section, an image or section of an image, a scene, etc., depending on the type of artifact you are dealing with and the level at which you want to subdivide this artifact.
  2. Is perhaps more adept at bringing together a variety of forms of artifacts in the same study. While other approaches can certainly accomplish this, content analysis readily allows the researcher to deconstruct, label and compare different kinds of ‘content’. For example, perhaps you have developed a new advocacy training for community members. To evaluate your training you want to analyze a variety of products they create after the workshop, including written products (e.g. letters to their representatives, community newsletters), audio/visual products (e.g. interviews with leaders, photos hosted in a local art exhibit on the topic) and performance products (e.g. hosting town hall meetings, facilitating rallies). Content analysis can allow you the capacity to examine evidence across these different formats.

For some more in-depth discussion comparing these two approaches, including more philosophical differences between the two, check out this article by Vaismoradi, Turunen, and Bondas (2013).[3]

Variations in the approach. There are also significant variations among different content analysis approaches.  Some of these approaches are more concerned with quantifying (counting) how many times a code representing a specific concept or idea appears. These are more quantitative and deductive in nature. Other approaches look for codes to emerge from the data to help describe some idea or event. These are more qualitative and inductive. Hsieh and Shannon (2005) describe three approaches to help understand some of these differences:

  • Conventional Content Analysis: Starting with a general idea or phenomenon you want to explore (for which there is limited data), coding categories then emerge from the raw data. These coding categories help us understand the different dimensions, patterns, and trends that may exist within the raw data collected in our research.
  • Directed Content Analysis: Starts with a theory or existing research for which you develop your initial codes (there is some existing research, but incomplete in some aspects) and uses these to guide your initial analysis of the raw data to flesh out a more detailed understanding of the codes and ultimately, the focus of your study.
  • Summative Content Analysis: Starts by examining how many times and where codes are showing up in your data, but then looks to develop an understanding or an “interpretation of the underlying context” (p.1277) for how they are being used. As you might have guessed, this approach is more likely to be used if you’re studying a topic which already has some existing research that forms a basic place to begin the analysis.[4]

The authors also provide a table (see below) that highlights key features differentiating these approaches:

Determining your codes. We are back to coding!  As in thematic analysis, you will be coding your data (labeling smaller chunks of information within each data artifact of your sample).  In content analysis, you may be using pre-determined codes, such as those suggested by an existing theory (deductive) or you may seek out emergent codes that you uncover as you begin reviewing your data (inductive). Regardless of which approach you take, you will want to develop a well-documented codebook.

A is a document that outlines the list of codes you are using as you analyze your data, a descriptive definition of each of these codes, and any decision rules that apply to your codes.  A decision-rule provides information on how the researcher determines what code should be placed on an item, especially when codes may be similar in nature. If you are using a deductive approach, your codebook will largely be formed prior to analysis, whereas if you use an inductive approach, your codebook will be built over time. To help illustrate what this might look like, below is a brief excerpt of a codebook from one of the projects I’m currently working on.

Coding, comparing, counting. Once you have (or are developing) your codes, your next step will be to actually code your data. In most cases, you are looking for your coding structure (your list of codes) to have good coverage.  This means that most of the content in your sample should have a code applied to it.  If there are large segments of your data that are uncoded, you are potentially missing things.  Now, do note that I said most of the time.  There are instances when we are using artifacts that may contain a lot of information, only some of which will apply to what we are studying. In these instances, we obviously wouldn’t be expecting the same level of coverage with our codes. As you go about coding you may change, refine and adapt your codebook as you go through your data and compare the information that reflects each code.  As you do this, keep your research journal handy and make sure to capture and record these changes so that you have a trail documenting the evolution of your analysis. Also, as suggested earlier, content analysis may also involve some degree of counting as well.  You may be keeping a tally of how many times a particular code is represented in your data, thereby offering your reader both a quantification of how many times (and across how many sources) a code was reflected and a narrative description of what that code came to mean.

Representing the findings from your coding scheme. Finally, you need to consider how you will represent the findings from your coding work. This may involve listing out narrative descriptions of codes, visual representations of what each code came to mean or how they related to each other, or a table that includes examples of how your data reflected different elements of your coding structure. However you choose to represent the findings of your content analysis, make sure the resulting product answers your research question and is readily understandable and easy-to-interpret for your audience.

Below are two illustrations from the same study that used content analysis to analyze forensic social work syllabi[5]. The capture of image labeled Table 2 identifies in how many of the syllabi each of the categories (or what they call sub-themes) were present.  Then in the image labeled Table 3, the authors provide examples of data or quotes that reflected each of these categories.

 

Resources for learning more about Content Analysis:

Bengtsson, M. (2016). How to plan and perform a qualitative study using content analysis. Nursing Plus Open, 2, 8-14. Retrieved from: https://www.sciencedirect.com/science/article/pii/S2352900816000029?via%3Dihub

Colorado State University (n.d.) Writing@CSU Guide: Content analysis [Webpage]. Retrieved from:  https://writing.colostate.edu/guides/guide.cfm

Columbia University Mailman School of Public Health, Population Health. (n.d.) Methods: Content analysis [Webpage] Retrieved from: https://www.mailman.columbia.edu/research/population-health-methods/content-analysis

Mayring, P. (2000, June). Qualitative content analysis. Forum: Qualitative Social Research, 1(2), Art. 20. Retrieved from: http://www.qualitative-research.net/index.php/fqs/article/view/1089/2385

A few exemplars of studies employing Content Analysis:

Collins, S. E., Taylor, E., Jones, C., Haelsig, L., Grazioli, V. S., Mackelprang, J. L., … & Clifasefi, S. L. (2018). Content analysis of advantages and disadvantages of drinking among individuals with the lived experience of homelessness and alcohol use disorders. Substance Use & Misuse53(1), 16-25.

Corley, N. A., & Young, S. M. (2018). Is social work still racist? A content analysis of recent literature. Social Work63(4), 317-326.

Deepak, A. C., Wisner, B. L., & Benton, A. D. (2016). Intersections between technology, engaged learning, and social capital in social work education. Social Work Education35(3), 310-322.

19.6 Grounded Theory Analysis

Learning Objectives

Learners will be able to:

  • Explain defining features of grounded theory analysis as a strategy for qualitative data analysis and identify when it is most effectively used
  • Formulate an initial grounded theory analysis plan (if appropriate for your research proposal)

What are you trying to accomplish with Grounded Theory Analysis? As outlined by Creswell (2013), “grounded theory is a qualitative research design in which the inquirer generates a general explanation (a theory) of a process, an action, or an interaction shaped by the views of participants” (p.83). With a grounded theory analysis, we are attempting to come up with a common understanding of how some event or series of events occurs based on a study of participant knowledge and experience of that event.

Variations in the approach. Differences in approaches to grounded theory analysis largely lie in the amount (and types) of structure that are applied to the analysis process.  Strauss and Corbin (2014)  suggest a highly structured approach to grounded theory analysis, one that moves back and forth between the data and the evolving theory that is being developed, making sure to anchor the theory very explicitly in concrete data points. With this approach, the researcher role is more detective-like; the facts are there, and you are uncovering and assembling them. While Charmaz (2014) suggests a more interpretivist approach to grounded theory analysis, where findings emerge as an exchange between the unique and subjective (yet still accountable) position of the researcher(s) and their understanding of the data, acknowledging that another researcher might emerge with a different theory or understanding. So in this case, the researcher functions more as a liaison, where he or she bridges understanding between the participant group and the scientific community, using their own unique perspective to help facilitate this process.

Coding in grounded theory.  Coding in grounded theory is generally a sequential activity.  First the researcher engages in open coding of the data.  This involves reviewing the data to determine the preliminary categories or phases that make up the particular event.  Within this open coding process, the researcher will also likely develop subcategories that help to expand and provide a richer understanding of what each of the categories can mean. Next, axial coding will revisit the open codes and identify connection between codes, thereby beginning to group codes that share a relationship. Finally, selective or theoretical coding explores how the relationships between these concepts come together, providing a theory that describes how this event or series of events takes place, often ending in an overarching or unifying idea tying these concepts together. Dr. Tiffany Gallicano has a helpful blog post that walks the reader through examples of each stage of coding.  Below is the final chart that she provides that illustrates the progression of sample codes throughout this process.

Constant comparison. While ground theory is not the only approach to qualitative analysis that utilizes constant comparison, it is certainly widely associated with this approach.  Constant comparison reflects the motion that takes place throughout the analytic process (across the levels of coding described above), whereby the researcher moves back and forth between the data and the emerging categories and evolving theoretical understanding.  Ground theory often relies on a relatively large number of interviews and usually will begin analysis while the interviews are ongoing.  As a result, the researcher(s) work to continuously compare their understanding of findings against new and existing data that they have collected.

[7]

Developing your Theory. Remember, the aim of using a grounded theory approach to your analysis is to develop a theory, or an explanation of how a certain event/phenomenon/process occurs. As you bring your coding process to a close, you will emerge not just with a list of ideas or themes, but an explanation of how these ideas are interrelated and work together to produce the event you are studying. Thus, you are building a theory that explains the event you are studying that is grounded in the data you have gathered.

Resources for learning more about Grounded Theory:

Chun Tie, Y., Birks, M., & Francis, K. (2019). Grounded theory research: A design framework for novice researchers. SAGE Open Medicine, 7. Retrieved from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6318722/pdf/10.1177_2050312118822927.pdf

Gibbs, G.R. (2015, February 4). A discussion with Kathy Charmaz on Grounded Theory. [Video]. Retrieved from: https://www.youtube.com/watch?v=D5AHmHQS6WQ

Glaser, B.G., & Holton, J. (2004, May). Remodeling grounded theory. Forum: Qualitative Social Research, 5(2), Art.4. Retrieved from: http://www.qualitative-research.net/index.php/fqs/article/view/607/1315

Mills, J., Bonner, A., & Francis, K. (2006). The development of Constructivist Grounded Theory. International Journal of Qualitative Methods, 5(1). Retrieved from: https://journals.sagepub.com/doi/full/10.1177/160940690600500103 

A few exemplars of studies employing Grounded Theory:

Burkhart, L., & Hogan, N. (2015). Being a female veteran: A grounded theory of coping with transitions. Social Work in Mental Health13(2), 108-127.

Donaldson, W. V., & Vacha-Haase, T. (2016). Exploring staff clinical knowledge and practice with LGBT residents in long-term care: A grounded theory of cultural competency and training needs. Clinical Gerontologist39(5), 389-409.

Vanidestine, T., & Aparicio, E. M. (2019). How Social Welfare and Health Professionals Understand “Race,” Racism, and Whiteness: A Social Justice Approach to Grounded Theory. Social Work in Public Health, 1-14.

19.7 Photovoice

Learning Objectives

Learners will be able to:

  • Explain defining features of photovoice as a strategy for qualitative data analysis and identify when it is most effectively used
  • Formulate an initial analysis plan using photovoice (if appropriate for your research proposal)

What are you trying to accomplish with photovoice analysis? Photovoice is an approach to qualitative research that combines the steps of data gathering and analysis with visual and narrative data.  The ultimate aim of the analysis is to produce some kind of desired change with and for the community of participants.  While other analysis approaches discussed here may involve including participants more actively in the research process, it is certainly not the norm.  However, with photovoice, it is. Using an approach that involves photovoice will generally assume that the participants in your study will be taking on a very active role throughout the research process, to the point of acting as co-researchers.

As an example of this work, Mitchell (2018) combines photovoice and an environmental justice approach to engage an American Indian community around the significance and the implications of water for their tribe. This research is designed to help raise awareness and support advocacy efforts for improved access to and quality of natural resources for this group. Photovoice has grown out of participatory and community-based research traditions that assume that community members have their own expertise they bring to the research process, and that they should be involved, empowered, and should mutually benefit from research that is being conducted. This mutual benefit means that this type of research involves some kind of desired and very tangible changes for participants; the research will support something that community members want to see happen.

Training your team. Because this approach involves participants not just sharing information, but actually utilizing research skills to help collect and interpret data, as a researcher you need to take on an educator role and share your research expertise in preparing them to do so.  After recruiting and gathering informed consent, part of the on-boarding process will be to determine the focus of your study.  Some photovoice projects are more prescribed, where the researcher comes with an idea and seeks to partner with a specific group or community to explore this topic. At other times, the researcher joins with the community first, and collectively they determine the focus of the study and craft the research question . Once this focus has been determined and shared, the team will be charged with gathering photos that represent responses to the research question for each individual participant. Depending on the technology used to capture these photos (e.g. cameras, ipads, video recorders, cell phones), training may need to be provided.

Once photos have been captured, team members will be asked to provide a caption or description that helps to interpret what their picture(s) mean in relation to the focus of the study.  After this, the team will collectively need to seek out themes and patterns across the visual and narrative representations.  This means you may employ different elements of thematic or content analysis to help you interpret the collective meaning across the data and you will need to train your team to utilize these approaches.

Converging on a Shared Story. Once you have found common themes, together you will work to assemble these into a cohesive broader story or message regarding the focus of your topic.  Now remember, the participatory roots of photovoice suggest that the aim of this message is to seek out, support, encourage or demand some form of change or transformation, so part of what you will want to keep in mind is that this is intended to be a persuasive story. Your research team will need to consider how to put your findings together in a way that supports this intended change. The packaging and format of your findings will have important implications for developing and disseminating the final products of qualitative research. Chapter 21 focuses more specifically on decisions connected with this phase of the research process.

Resources for learning more about photovice:

Liebenberg, L. (2018). Thinking critically about photovoice: Achieving empowerment and social change. International Journal of Qualitative Methods, 17. Retrieved from: https://journals.sagepub.com/doi/pdf/10.1177/1609406918757631

Mangosing, D. (2015, June 18). Photovoice training and orientation. [Video]. Retrieved from: https://www.youtube.com/watch?v=UuPcnI3X_3c   

University of Kansas, Community Toolbox. (n.d.). Section 20. Implementing Photovoice in Your Community. [Webpage]. Retrieved from: https://ctb.ku.edu/en/table-of-contents/assessment/assessing-community-needs-and-resources/photovoice/main

Woodgate, R.L., Zubra, M., & Tennet, P. (2017, January). Worth a thousand words? Advantages, challenges and opportunities in working with photovoice as a qualitative research method with youth and their families. Forum: Qualitative Social Research, 18(1), Art. 2. Retrieved from: http://www.qualitative-research.net/index.php/fqs/article/view/2659/4045#g3

A few exemplars of studies employing Photovoice:

Fisher-Borne, M., & Brown, A. (2018). A Case Study Using Photovoice to Explore Racial and Social Identity Among Young Black Men: Implications for Social Work Research and Practice. Journal of Ethnic & Cultural Diversity in Social Work27(2), 157-175.

Houle, J., Coulombe, S., Radziszewski, S., Boileau, G., Morin, P., Leloup, X., … & Robert, S. (2018). Public housing tenants’ perspective on residential environment and positive well-being: An empowerment-based Photovoice study and its implications for social work. Journal of Social Work, 18(6), 703-731.

Mitchell, F. M. (2018). “Water Is Life”: Using photovoice to document American Indian perspectives on water and health. Social Work Research42(4), 277-289.

Reflexive Journal Entry Prompt

After learning about these different types of qualitative analysis:

  • Which of these approaches make the most sense to you and how you view the world?
  • Which of them are most appealing and why?
  • Which do you want to learn more about?
Developing your Qualitative Research Protocol

Decision Point: How will you conduct your analysis?

  • Thinking about what you need to accomplish with the data you have collected, which of these analytic approaches will you use?
    • What makes this the most effective choice?
  • Outline the steps you plan to take to conduct your analysis
  • What peer-reviewed resources have you gathered to help you learn more about this method of analysis? (keep these handy for when you write-up your study!)

Key Takeaways

  • In its most general sense, qualitative analysis is about breaking apart data and reassembling it in a systematic way that helps us answer our research question.
  • There are many strategies in our qualitative toolbox that we can utilize for analysis, but we should be guided in our selection of a strategy by the framing of our research question and what we are attempting to accomplish.
  • Even within the analytic approaches discussed here (which is far from exhaustive), there is much variation within each approach.  Make sure to explore each of these in more depth if you are considering them as contenders for your qualitative research design.
  • Qualitative analysis begins with you taking time getting to know your data.
  • Researcher transparency in qualitative data analysis is very important, and we demonstrate transparency by documenting our role throughout the research process. This includes capturing our thought process, the decisions we are making, and the justification for these decisions (among other things).
  • To engage in effective qualitative analysis, we need to have a deep understanding of the cultural context in which data is shared with us.
  • From an empowerment perspective, it is important to find ways for community members to inform and validate our analytic process whenever possible.

 

Media Attributions

  • no black box
  • iterative v linear
  • Greetings
  • evolvinig code
  • interview transcript
  • Data matrix
  • thematic map
  • types of CA
  • codebook and decision rules
  • CA_Table 2
  • CA_Table 1
  • gt coding
  • constant compare
  • community meeting
  • photo exhibit

  1. Mafile'o, T. (2004). Exploring Tongan Social Work: Fekau'aki (Connecting) and Fakatokilalo (Humility). Qualitative Social Work, 3(3), 239-257.
  2. Maguire, M., & Delahunt, B. (2017). Doing a thematic analysis: A practical, step-by-step guide for learning and teaching scholars. AISHE-J: The All Ireland Journal of Teaching and Learning in Higher Education, 9(3).
  3. Vaismoradi, M., Turunen, H., & Bondas, T. (2013). Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nursing & Health Sciences, 15(3), 398-405.
  4. Hsieh, H. F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277-1288.
  5. Maschi, T., Rees, J., Leibowitz, G., & Bryan, M. (2019). Educating for rights and justice: a content analysis of forensic social work syllabi. Social Work Education, 38(2), 177-197.
  6. Gallicano, T. (2013, July 22). An example of how to perform open coding, axial coding and selective coding. [Blog post]. Retrieved from https://prpost.wordpress.com/2013/07/22/an-example-of-how-to-perform-open-coding-axial-coding-and-selective-coding/
  7. https://www.goodfreephotos.com/vector-images/stick-figure-with-pencil-vector-clipart.png.php

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Graduate research methods in social work by Matthew DeCarlo, Cory Cummings, Kate Agnelli, Nicole Lee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted.

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