action research qualitative data analysis

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6 Qualitative Data Analysis

• Published: January 2022
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Qualitative data analysis differs substantially from quantitative data analysis. Qualitative data analysis is often iterative, which means procedures are repeated until there is a sufficient amount of data to provide information about a phenomenon or case. Qualitative data analysis is recursive, which means the analysis is repeated so researchers are immersed in data. Qualitative data analysis is holographic, which means that researchers are responsible and represented in our writing. Qualitative data analysis is critical, which means researchers are constantly reflecting on the process and questioning the outcomes. This chapter explores the process of analyzing and interpreting qualitative data, connecting data analysis to stakeholder inclusion, positionality, and ethics.

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Action Research Tutorials-CCAR

Action research tutorials, tutorial 8:  analyzing data - evidence, tutorial 8: resources, a. understanding your data.

Action Research is not a single research project; rather it is an ongoing iterative approach that takes place across cycles of innovation and reflection. It is a way of learning from and through systematic inquiry into one's practice. Central to this process is the collection and analysis of data. The image below (Rie1, 2014) uses color to represent the growing knowledge of the action researcher. After a number of cycles, there is a reporting out or sharing function. In the last tutorial, we discussed what data artifacts you could collect. Now we are going to talk about the analysis of these data artifacts. This is often the step that practitioners find most difficult because they have not been trained to analyze data.  

B.  Organizing your data -- What is your storyline?

You will need to report your "findings" that you discover from exploring your data. To develop findings, you have to engage in some serious "looking" to find meaning from what you collected. Often new action researchers will simply describe a survey and then paste the questions with the group responses in their reports. But this is not helping the reader to make sense of what you learned. You haven't processed the data to learn from it. It is helpful to begin with your research questions or your near and medium outcomes on your logic model. What does the data you collected tell you that will help answer your research question? How does the data help you to understand if you see evidence of the outcomes that you predicted in your logic model? To answer these questions, take the responses to your survey and group them around what you are trying to learn. You need to find the storyline that you will be sharing with your audience. AN EXAMPLE-- Katherine Korte Flips her Government Course For example, consider an educator teaching a government class who flipped her classroom as her "action" and, for data collection, asked her students to provide some feedback on their learning in a survey. RESEARCH QUESTION: If I flipped my classroom using video to send lessons home and ibook technology, how will this affect the quality of knowledge building dialogues in the classroom? She was interested in what students learned, how they learned, and changes in their attitudes and engagement in learning. Therefore, she might take her ten survey questions and organize them under these three topics with one question not fitting this process.

(1) knowledge ....(three survey questions) (2) process ....(four survey questions) (3) attitudes, ....(two survey questions)

Now she would compute responses to these groups of questions and think about other data sources besides the survey -- what tests or quizzes did students take that would help answer the first question about knowledge? How might field notes shed light on what students were learning? What artifacts were produced that could be examined? For the second topic, there were student responses that addressed their work in discussion circles, and what they learned from each other and some of the problems from group work. How did students' comments match her notes? Were there more or fewer problems that students needed help with? Then she might organize the analysis into three sections:

Changes in Student Knowledge ....analysis of three survey questions ....classroom quizzes, ....end of term assessment, ....ibook notetaking, ....performances Changes in the Way Students worked ....analysis of four survey questions ....field notes on how well the groups worked ....what problems they had, ....teacher assessment of how prepared they were for the classroom discussions, ....speed at which they accomplished their tasks Student Reactions to the flipped Classroom ....analysis of two survey questions ....students blogs ....observations about the comfort of students contributing ideas or making presentations

This organization helps her to find a storyline that she will tell about the data.  

Validity of Measures

You can see that now that multiple sources of data are being examined to see what they say about the outcomes that the educator cares about. The analysis should be an honest hunt to figure out what can be learned from what happened. You are looking for valid measures that will answer your question. For example, if a teacher listed grades as evidence of student engagement, one might challenge this measure as not a valid measure of engagement because a student could have high grades and not be engaged and another student could be engaged and yet have a low grade. This is why researchers consider the validity of measures.  

Participatory Data Collection

Empowering the group to participate in data collection and analysis helps everyone to have a sense of ownership over the outcomes and the deep understanding of change. These guides and articles might help you think about who should be involved in the data collection/analysis work in your action research. Even if you collect and analyze data within a group, you still have your own reflections on what this process means to you as an action researcher.   

Guijt, I.,(2014).  Participatory Approaches:  This guide, written by Irene Guijt for  UNICEF , looks at the use of participatory approaches in impact evaluation. While not exactly the same as action research it is very close and this guide will be useful if you are involving stakeholders in your process of data collection and analysis

Increasing Participation in Evaluating – Bruner Foundation Guide  - This guide discusses how Organization Staff, Evaluators and Funders are involved in Participatory Evaluation.

Campilan, D. (2000). Participatory Evaluation of Participatory Research. Forum on Evaluation of International Cooperation Projects: Centering on Development of Human Resources in the Field of Agriculture. Nagoya, Japan, International Potato Center.  http://ir.nul.nagoya-u.ac.jp/jspui/bitstream/2237/8890/1/39-56.pdf

Chambers, R. (2009) Making the Poor Count: Using Participatory Options for Impact Evaluation in Chambers, R., Karlan, D., Ravallion, M. and Rogers, P. (Eds) Designing impact evaluations: different perspectives . New Delhi, India, International Initiative for Impact Evaluation. http://www.3ieimpact.org/admin/pdfs_papers/50.pdf

Guijt, I. and J. Gaventa (1998).  Participatory Monitoring and Evaluation: Learning from Change.  IDS Policy Briefing. Brighton, UK, University of Sussex- In participatory monitoring and evaluation, stakeholders work together to decide how progress should be measured and how to understand the outcomes of change.

Zukoski, A. and M. Luluquisen (2002). "Participatory Evaluation: What is it? Why do it? What are the challenges?" Policy & Practice(5). http://depts.washington.edu/ccph/pdf_files/Evaluation.pdf

C.  Exploring your data -- What is your story?

Coding your Data

The Center for Evaluation Research at the University of Calif, Davis has a good and brief  guide on coding as well as  other tools for data analysis  that will be helpful to read before you begin your coding process. If you are doing coding of data-- for example coding what students write in their blogs, it will be helpful to develop a codebook where you list the codes and examples to help you make decisions. This serves to create consistency or reliability in your coding. Good research practice involves creating a codebook for any qualitative analysis you do. This can be very simple or more complex depending on the nature of your data and you scale of analysis you plan to do. This is similar to create a rubric to assess student work. If you are writing an action research dissertation, the use of a codebook is highly recommended. If you want to see how researchers use  codebooks and coding schemes , you will find a number of them have been collected by Kimberly Neuendorf as an accompaniment to her Content Analysis Guidebook. I enjoyed exploring the codebook for the  study of female roles in James Bond 007 movies . In this video, Kevin Meethan also describes the process of going from an interview to codes-- if you go to the youtube version, I think you can find a link to the texts that are given in the examples, but you can just listen to the examples and a get sense for the coding process.  

Reliability  In any coding, an important question to ask is: how reliable is the coding process? In other words, if this teacher told us what theme or content to look for and we coded the data would it be the same as her coding? To achieve reliability, researchers often have two people code data and compare. If they agree on 85% of the cases, then we say the coding is reliable. If you cannot find another person to compare your coding, you can check your coding with yourself at different times. Code a few cases and then wait a day and recode. Do they match? The closer the match the more reliable your coding. Teachers often engage in a very similar process to grade papers in a consistent or reliable way. They create a rubric and then use that rubric to assess the student work. The rubric is their codebook and the access is the coding process. Content Analysis Let's stay with the teacher who flipped her classroom and had students keep notes in their iBooks as they watched the videos at home. This teacher wants to analyze this textual data. How should she approach this? Depending on which category the action researcher chooses, there are lots of ways to do a qualitative analysis of data. Some are more time consuming than others and a large amount of data might be more than a teacher can examine. Our teacher might want to create a sample to analyze. She could take a random set of three entries for each student, or all students' last blog before the test, or she might decide to examine all of the blogs of six students. These six could be randomly selected from all of the students or from groups of students. For example, the teacher might select two students at random from those that received an A, B or C grade in the course, or in a previous course, or one student from each discussion group. A content analysis helps us to know what was contained in these blogs. Suppose there are some central ideas or concepts and our teacher wants to see how often, if at all, they appear in student writing. To access this information, she might use a single blog, or response to a question as the "unit of analysis" and then mark yes or no for a number of ideas that she has decided on. She would take each entry and code it, say 1 or 0, for each of the concepts. This would help her see how frequently individuals and the class as a whole explored these concepts. A different approach to content analysis would involve coding all of the topics that were covered in each entry. Then she could examine the topics that students selected as important. This approach would help her to understand what topics students judged to be important. Theme Analysis Theme analysis is similar to content analysis but a bit more difficult. Here, you are not looking for concepts but for themes. Some process themes might be positive or negative attitudes about the different forms of technology used in the classroom. As in content analysis, these could be set up by the teacher or they could emerge from the data as when the action researcher discovers themes through repeated reading, grouping them, and constructing a final set of themes to use.  Developing a Critical Eye Action research is not about proving how successful you were at some new attempt. It is about learning from your efforts. It is not so much about whether it worked or not, but a more thoughtful examination of how it worked, or for whom it worked, and why things turned out as they did. So be ready to hear that your plans did not work as you expected. Try not to be defensive. Instead, see this as a time to learn from your inquiry. What worked and why? What did not work and why? it is important to examine evidence that contradicts or opposes your research question.  You might find the " What Else Test " developed by Jess Dart will be helpful as you develop your critical eye. 

Advice for Data Analysis in Social Services

The Institute for Research and Innovation in Social Services (IRISS) is a charitable company with a mission to:…promote positive outcomes for the people who use Scotland’s social services by enhancing the capacity and capability of the social services workforce to access and make use of knowledge and research for service innovation and improvement. They have a number of resources can help think about action research starting with this animated video:

Outcomes ToolboxA toolbox of resources relevant to an outcomes-focused approach in social services Developed by  IRISS  in partnership with Coalition of Care and Support Providers in Scotland (CCPS), the Outcomes Toolbox brings together a range of resources and knowledge relevant to an outcomes-focused approach in the social services. Understanding and Measuring Outcomes The role of Qualitative Data Emma Miller and Ellen Daly

D.  Displaying your Data -- How will you Tell a Compelling Research Story?

Think about what your data has told you and tell others what you learned. Use chart, graphs or tables to help people quickly see what you have found. With all of the choices you will find yourself thinking which type of chart should I use. While there are suggested guidelines about these choices, you might find it works just as well to try displaying the data in a number of different ways and look at it and see what you learn. Then test it with someone who does not know the data. What do they see from your chart? Consider the signal to noise ratio. The signal is your data and the noise is graphical elements around the data you might not need. You want to encourage the eye to quickly zero in on what is relevant. This video shows some examples might help you think about how to represent your data without unnecessary distortion. While design can be subjective (and while I don't agree with all of the comments) the suggestion to minimize "chart junk" is important. Try to find ways to clearly communicate your data in an honest and pleasing image.

Presentation and Visualization of Data A site that might be helpful for understanding some basic concepts in statistics and how to display data can be found at  http://www.shodor.org/interactivate/lessons/HistogramsBarGraph/  This site is arranged to support for high school math teachers but there some interactives that might help you think about data displays. (And it is a fun way to play with math and statistic concepts). Many eyes ( http://www.many-eyes.com ) is a free tool for creating a visualization of data sets. for examples see... http://www-958.ibm.com/software/data/cognos/manyeyes/visualizations?sort=rating

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Data Analysis

Module 5: Data Analysis & Reciprocity

At this stage, you’re probably carrying out your planned intervention or action and gathering data to address your research question. Many newcomers to action research believe that analysis should only start after all the data has been collected.

An interim analysis is part of the continuous, ongoing data analysis. It is part of the ongoing reflective planning process of action research (Hendricks, 2013).

Your action research projects will typically involve both quantitative and qualitative data. The methods for simplifying quantitative data, such as  reporting, comparing , and  displaying data,  differ significantly from those used for qualitative data, which include  analyzing the data to identify patterns and themes.

New researchers often feel disappointed when their interventions don’t lead to the anticipated results. However, even in these situations, exploring the data to understand why things didn’t work as expected can provide valuable insights. This process can guide you in refining your intervention to achieve better results in the future.

Remember! Action research is an iterative process so what you learn from this cycle of your research project will inform your next iteration of action research.

Analysis of Quantitative Data: Reporting & Comparing

Quantitative data is usually gathered via:

• Test scores
• Rubric-scored work
• Tally sheets
• Behavioural scales
• Attitude scales
• Closed-ended survey items

For example:  Counting or averaging the number of responses for each item.

• Closed-ended responses (strong, average, weak) can reflect counts for the number of respondents who chose each response.
• For the behavioural scale item, which includes numerical responses, the actual number chosen for each item could be tallied and the numbers could be averaged to describe results (Hendricks, 2013).

Quick Tips to Analyze Quantitative Data

“According to Shank (ibid) “themes do not emerge from data. What emerges after much hard work and creative thought, is an awareness in the mind of the researcher that there are patterns of order that seem to cut across various aspects of the data. When these patterns become organized, and when they characterize different segments of data, then we can call them ‘themes’.”

(Hendricks 2013)

Analysis of Qualitative Data: Looking for Themes & Patterns

Analysis of qualitative data is a process of making meaning from data sources that can be interpreted in several ways and helps answer the  why questions .

These data sources can be explained and used to answer your research question only after they have been interpreted. This process requires a deeper analysis of data than those processes used to explain quantitative data sources (Hendricks 2013).

Verification

Verification is knowing when you “got it right.” Reaching valid conclusions in your study is a critical step in the action research cycle. Conclusions must be reasonable in light of the results obtained.

Quick Tips to Analyze Qualitative Data

Action Research Handbook Copyright © by Dr. Zabedia Nazim and Dr. Sowmya Venkat-Kishore is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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5 Collecting Data in Your Classroom

ESSENTIAL QUESTIONS

• What sort of methodological considerations are necessary to collect data in your educational context?
• What methods of data collection will be most effective for your study?
• What are the affordances and limitations associated with your data collection methods?
• What does it mean to triangulate data, and why is it necessary?

As you develop an action plan for your action research project, you will be thinking about the primary task of conducting research, and probably contemplating the data you will collect. It is likely you have asked yourself questions related to the methods you will be using, how you will organize the data collection, and how each piece of data is related within the larger project. This chapter will help you think through these questions.

Data Collection

The data collection methods used in educational research have originated from a variety of disciplines (anthropology, history, psychology, sociology), which has resulted in a variety of research frameworks to draw upon. As discussed in the previous chapter, the challenge for educator-researchers is to develop a research plan and related activities that are focused and manageable to study. While human beings like structure and definitions, especially when we encounter new experiences, educators-as-researchers frequently disregard the accepted frameworks related to research and rely on their own subjective knowledge from their own pedagogical experiences when taking on the role of educator-researcher in educational settings. Relying on subjective knowledge enables teachers to engage more effectively as researchers in their educational context. Educator-researchers especially rely on this subjective knowledge in educational contexts to modify their data collection methodologies. Subjective knowledge negotiates the traditional research frameworks with the data collection possibilities of their practice, while also considering their unique educational context. This empowers educators as researchers, utilizing action research, to be powerful agents for change in educational contexts.

Thinking about Types of Data

Whether the research design is qualitative, quantitative or mixed-methods, it will determine the methods or ways you use to collect data. Qualitative research designs focus on collecting data that is relational, interpretive, subjective, and inductive; whereas a typical quantitative study, collects data that are deductive, statistical, and objective.

In contrast, qualitative data is often in the form of language, while quantitative data typically involves numbers. Quantitative researchers require large numbers of participants for validity, while qualitative researchers use a smaller number of participants, and can even use one (Hatch, 2002). In the past, quantitative and qualitative educational researchers rarely interacted, sometimes holding contempt for each other’s work; and even published articles in separate journals based on having distinct theoretical orientations in terms of data collection. Overall, there is a greater appreciation for both quantitative and qualitative approaches, with scholars finding distinct value in each approach, yet in many circles the debate continues over which approach is more beneficial for educational research and in educational contexts.

The goal of qualitative data collection is to build a complex and nuanced description of social or human problems from multiple perspectives. The flexibility and ability to use a variety of data collection techniques encompasses a distinct stance on research. Qualitative researchers are able to capture conversations and everyday language, as well as situational attitudes and beliefs. Qualitative data collection is able to be fitted to the study, with the goal of collecting the most authentic data, not necessarily the most objective. To researchers who strictly use quantitative methods, qualitative methods may seem wholly unstructured, eclectic, and idiosyncratic; however, for qualitative researchers these characteristics are advantageous to their purpose. Quantitative research depends upon structure and is bounded to find relationship among variables and units of measurement. Quantitative research helps make sense of large amounts of data. Both quantitative and qualitative research help us address education challenges by better identifying what is happening, with the goal of identifying why it is happening, and how we can address it.

Most educator-researchers who engage in research projects in schools and classrooms utilize qualitative methodologies for their data collection. Educator-researchers also use mixed methods that focus on qualitative methods, but also use quantitative methods, such as surveys, to provide a multidimensional approach to inquiring about their topic. While qualitative methods may feel more comfortable, there is a methodological rationale for using quantitative research.

Research methodologists use two distinct forms of logic to describe research: induction and deduction. Inductive approaches are focused on developing new or emerging theories, by explaining the accumulation of evidence that provides meaning to similar circumstances. Deductive approaches move in the opposite direction, and create meaning about a particular situation by reasoning from a general idea or theory about the particular circumstances. While qualitative approaches are inductive – observe and then generate theories, for example – qualitative researchers will typically initiate studies with some preconceived notions of potential theories to support their work.

Flexible Research Design

A researcher’s decisions about data collection and activities involve a personal choice, yet the choice of data sources must be responsive to the proposed project and topic. Logically, researchers will use whatever validated methods help them to address the issue they are researching and will develop a research plan around activities to implement those methods. While a research plan is important to conducting valid research in schools and classrooms, a research plan should also be flexible in design to allow data to emerge and find the best data to address research questions. In this way, a research plan is recommended, but data collection methods are not always known in advance. As you, the educator-researcher, interacts with participants, you may find it necessary to continue the research with additional data sources to better address the question at the center of your research. When educators are researchers and a participant in their study, it is especially important to keep an open mind to the wide range of research methodologies. All-in-all educator-researchers should understand that there are varied and multiple paths to move from research questions to addressing those questions.

Mixed Methods

As mentioned above, mixed methods is the use of both qualitative and quantitative methods. Researchers generally use mixed methods to clarify findings from the initial method of data collection. In mixed-methods research, the educator-researcher has increased flexibility in data collection. Mixed methods studies often result in a combination of precise measurements (e.g., grades, test scores, survey, etc.) along with in-depth qualitative data that provide meaningful detail to those measurements. The key advantage of using mixed methods is that quantitative details enhance qualitative data sources that involve conclusions and use terms such as usually, some, or most which can be substituted with a number or quantity, such as percentages or averages, or the mean, the median, and/or the mode. One challenge to educator-researchers is that mixed methods require more time and resources to complete the study, and more familiarity about both qualitative and quantitative data collection methods.

Mixed methods in educator research, even if quantitative methods are only used minimally, provide an opportunity to clarify findings, fill gaps in understanding, and cross-check data. For example, if you are looking at the use of math journals to better engage students and improve their math scores, it would be helpful to understand their abilities in math and reading before analyzing the math journals. Therefore, looking at their test scores might give you some nuanced understanding of why some students improved more than others after using the math journals. Pre- and post-surveys would also provide valuable information in terms of students’ attitudes and beliefs about math and writing. In line with thinking about pre- and post-surveys, some researchers suggest using either qualitative or quantitative approaches in different phases of the research process. In the previous example, pre- and post test scores may quantitatively demonstrate growth or improvement after implementing the math journal; however, the qualitative data would provide detailed evidence as to why the math journals contributed to growth or improvement in math. Quantitative methods can establish relationships among variables, while qualitative methods can explain factors underlying those same relationships.

I caution the reader at this point to not simply think of qualitative methodologies as anecdotal details to quantitative reports. I only highlight mixed methods to introduce the strength of such studies, and to aid in moving educational research methodology away from the binary thinking of quantitative vs. qualitative. In thinking about data collection, possible data sources include questionnaires or surveys, observations (video or written notes), collaboration (meetings, peer coaching), interviews, tests and records, pictures, diaries, transcripts of video and audio recordings, personal journals, student work samples, e-mail and online communication, and any other pertinent documents and reports. As you begin to think about data collection you will consider the available materials and think about aspects discussed in the previous chapter: who, what, where, when, and how. Specifically:

• Who are the subjects or participants for the study?
• What data is vital evidence for this study?
• Where will the data be collected?
• When will the data be collected?
• How will the data be collected?

If you find you are having trouble identifying data sources that support your initial question, you may need to revise your research question – and make sure what you are asking is researchable or measurable. The research question can always change throughout the study, but it should only be in relation the data being collected.

Participant Data

As an educator, your possible participants selection pool is narrower than most researchers encounter – however, it is important to be clear about their role in the data design and collection. A study can involve one participant or multiple participants, and participants often serve as the primary source of data in the research process. Most studies by educator-researchers utilize purposeful sampling, or in other words, they select participants who will be able to provide the most relevant information to the study. Therefore, the study design relies upon the participants and the information they can provide. The following is a description of some data collection methods, which include: surveys or questionnaires, individual or group interviews, observations, field notes or diaries, narratives, documents, and elicitation.

Surveys, or questionnaires, are a research instrument frequently used to receive data about participants’ feelings, beliefs, and attitudes in regard to the research topic or activities. Surveys are often used for large sample sizes with the intent of generalizing from a sample population to a larger population. Surveys are used with any number of participants and can be administered at different times during the study, such as pre-activity and post-activity, with the same participants to determine if changes have occurred over the course of the activity time, or simply change over time. Researchers like surveys and questionnaires as an instrument because they can be distributed and collected easily – especially with all of the recent online application possibilities (e.g., Google, Facebook, etc.). Surveys come in several forms, closed-ended, open-ended, or a mix of the two. Closed-ended surveys are typically multiple-choice questions or scales (e.g. 1-5, most likely–least likely) that allow participants to rate or select a response for each question. These responses can easily be tabulated into meaningful number representations, like percentages. For example, Likert scales are often used with a five-point range, with options such as strongly agree, agree, neutral, disagree, and strongly disagree. Open-ended surveys consist of prompts for participants to add their own perspectives in short answer or limited word responses. Open-ended surveys are not always as easy to tabulate, but can provide more detail and description.

Interviews and Focus Groups

Interviews are frequently used by researchers because they often produce some of the most worthwhile data. Interviews allow researchers to obtain candid verbal perspectives through structured or semi-structured questioning. Interview questions, either structured or semi-structured, are related to the research question or research activities to gauge the participants’ thoughts, feelings, motivations, and reflections. Some research relies on interviewing as the primary data source, but most often interviews are used to strengthen and support other data sources. Interviews can be time consuming, but interviews are worthwhile in that you can gather richer and more revealing information than other methods that could be utilized (Koshy, 2010). Lincoln and Guba (1985) identified five outcomes of interviewing:

Outcomes of Interviewing

• Here and now explanations;
• Reconstructions of past events and experiences;
• Projections of anticipated experiences;
• Verification of information from other sources;
• Verification of information (p. 268).

As mentioned above, interviews typically take two forms: structured and semi-structured. In terms of interviews, structured means that the researcher identifies a certain number of questions, in a prescribed sequence, and the researcher asks each participant these questions in the same order. Structured interviews qualitatively resemble surveys and questionnaires because they are consistent, easy to administer, provide direct responses, and make tabulation and analysis more consistent. Structured interviews use an interview protocol to organize questions, and maintain consistency.

Semi-structured interviews have a prescribed set of questions and protocol, just like structured interviews, but the researcher does not have to follow those questions or order explicitly. The researcher should ask the same questions to each participant for comparison reasons, but semi-structured interviews allow the researcher to ask follow-up questions that stray from the protocol. The semi-structured interview is intended to allow for new, emerging topics to be obtained from participants. Semi-structured questions can be included in more structured protocols, which allows for the participant to add additional information beyond the formal questions and for the researcher to return to preplanned formal questions after the participant responds. Participants can be interviewed individually or collectively, and while individual interviews are time-consuming, they can provide more in-depth information.

When considering more than two participants for an interview, researchers will often use a focus group interview format. Focus group interviews typically involve three to ten participants and seek to gain socially dependent perspectives or organizational viewpoints. When using focus group interviews with students, researchers often find them beneficial because they allow student reflection and ideas to build off of each other. This is important because often times students feel shy or hesitant to share their ideas with adults, but once another student sparks or confirms their idea, belief, or opinion they are more willing to share. Focus group interviews are very effective as pre- and post-activity data sources. Researchers can use either a structured or semi-structured interview protocol for focus group interviews; however, with multiple participants it may be difficult to maintain the integrity of a structured protocol.

Observations

One of the simplest, and most natural, forms of data collection is to engage in formal observation. Observing humans in a setting provides us contextual understanding of the complexity of human behavior and interrelationships among groups in that setting. If a researcher wants to examine the ways teachers approach a particular area of pedagogical practice, then observation would be a viable data collection tool. Formal observations are truly unique and allow the researcher to collect data that cannot be obtained through other data sources. Ethnography is a qualitative research design that provides a descriptive account based on researchers’ observations and explorations to examine the social dynamics present in cultures and social systems – which includes classrooms and schools. Taken from anthropology, the ethnographer uses observations and detailed note taking, along with other forms of mapping or making sense of the context and relationships within. For Creswell (2007), several guidelines provide structure to an observation:

Structuring Observations

• Identify what to observe
• Determine the role you will assume — observer or participant
• Design observational protocol for recording notes
• Record information such as physical situation, particular events and activities
• Thank participants and inform them of the use of and their accessibility to the data (pp. 132– 134)

As an educator-researcher, you may take on a role that exceeds that of an observer and participate as a member of the research setting. In this case, the data sources would be called participant observation to clearly identify the degree of involvement you have in the study. In participant observation, the researcher embeds themselves in the actions of the participants. It is important to understand that participant observation will provide completely different data, in comparison to simply observing someone else. Ethnographies, or studies focused completely on observation as a data source, often extend longer than other data sources, ranging from several months to even years. Extended time provides the researcher the ability to obtain more detailed and accurate information, because it takes time to observe patterns and other details that are significant to the study. Self-study is another consideration for educators, if they want to use observation and be a participant observer. They can use video and audio recordings of their activities to use as data sources and use those as the source of observation.

Field Diaries and Notes

Utilizing a field dairy, or keeping field notes, can be a very effective and practical data collection method. In purpose, a field diary or notes keep a record of what happens during the research activities. It can be useful in tracking how and why your ideas and the research process evolved. Many educators keep daily notes about their classes, and in many ways, this is a more focused and narrower version of documenting the daily happenings of a class. A field diary or notes can also serve as an account of your reflections and commentary on your study, and can be a starting place for your data analysis and interpretations. A field diary or notes are typically valuable when researchers begin to write about their project because it allows them to draw upon their authentic voice. The reflective process that represents a diary can also serve as an additional layer of professional learning for researchers. The format and length of a field diary or notes will vary depending on the researching and the topic; however, the ultimate goal should be to facilitate data collection and analysis.

Data narratives and stories are a fairly new form of formalized data. While researchers have collected bits and pieces of narratives in other forms of data, asking participants to compose a narrative (either written, spoken, or performed) as a whole allows researchers to examine how participants embrace the complexities of the context and social interactions. Humans are programmed to engage with and share narratives to develop meaningful and experiential knowledge. Educator autobiographies bring to life personal stories shaped by knowledge, values, and feelings that developed from their classroom experiences. Narrative data includes three primary areas: temporality, sociality, and place (Clandinin & Conolley, 2000). In terms of temporality, narratives have a past, present, and future because stories are time-based and transitional. Sociality highlights the social relationships in narratives as well as the personal and moral dispositions. Place includes the spaces where the narratives happen. Furthermore, bell hooks (1991) notes that narratives, or storytelling, as inquiry can be a powerful way to study how contexts are influenced by power structures, often linking and intersecting the structural dynamics of social class, race, and gender to highlight the struggle.

Documents provide a way to collect data that is unobtrusive to the participant. Documents are unobtrusive data because it is collected without modifying or distracting the research context when gathered. Educational settings maintain records on all sorts of activities in schools: content standards, state mandates, student discipline records, student attendance, student assessments, performance records, parental engagement, records of how teachers spend PTO money, etc. Documents often provide background and contextual material providing a snapshot of school policies, demographic information, ongoing records over a period of time, and contextual details from the site of the research study. Documents can be characterized similarly to historical research, as primary and secondary. Examples of primary materials are first-hand sources from someone in the educational context, such as minutes from a school board or faculty meeting, photographs, video recordings, and letters. Examples of secondary sources typically include analysis or interpretations of a primary source by others, such as texts, critiques, and reviews. Both types of sources are especially valuable in action research.

Elicitation Methods

We have talked about several methods of data collection that each have useful ways of documenting, inquiring, and thinking about the research question. However, how does a researcher engage participants in ways that allow them to demonstrate what they know, feel, think, or believe? Asking participants directly about their thinking, feeling, or beliefs will only take you so far depending on the comfort and rapport the participant has with the researcher. There are always a variety of hurdles in extracting participants’ knowledge. Even the manner in which questions are framed and the way researchers use materials in the research process are equally important in getting participants to provide reliable, comparable, and valid responses. Furthermore, all individuals who participate in research studies vary in their ability to recall and report what they know, and this affects the value of traditional data collection, especially structured and semi-structured interviewing. In particular, participants’ knowledge or other thinking of interest may be implicit and difficult for them to explicate in simple discussion.

Elicitation methods help researchers uncover unarticulated participant knowledge through a potential variety of activities. Researchers will employ elicitation methods and document the participants’ actions and typically the description of why they took those particular actions. Educators may be able to relate the process of elicitation methods to a “think aloud” activity in which the researcher wants to record or document the activity. Elicitation methods can take many forms. What follows are some basic ideas and formats for elicitation methods.

Brainstorming/Concept Map

Most educators are probably familiar with the process of brainstorming or creating a concept map. These can be very effective elicitation methods when the researcher asks the participant to create a concept map or representation of brainstorming, and then asks the participant to explain the connections between concepts or ideas on the brainstorming or concept map.

Sorting provides an engaging way to gather data from your participants. Sorting, as you can imagine, involves participants sorting, grouping, or categorizing objects or photographs in meaningful ways. Once participants have sorted the objects or photographs, the researcher records or documents the participant explaining why they sorted or grouped the objects or photographs in the way that they did. As a former history teacher, I would often use sorting to assess my students’ understanding of related concepts and events in a world history class. I would use pictures too as the means for students to sort and demonstrate what they understood from the unit. For broader discussion of elicitation techniques in history education see Barton (2015).

Listing/ Ranking

Listing can be an effective way to examine participants’ thinking about a topic. Researchers can have participants construct a list in many different ways to fit the focus of the study and then have the participants explain their list. For example, if an educator was studying middle school student perceptions of careers, they could ask them to complete three lists: Careers in Most Demand; Careers with Most Education/Training; Careers of most Interest.

Then, once participants have filled out the lists, the most important part is documenting them explaining their thinking, and why they filled out the lists the way they did. As you may imagine, in this example, every participant would have a list that is different based on their personal interests.

Researchers can also elicit responses by simply giving participants a prompt, and then asking them to recall whatever they know about that prompt. Researchers will have the participants do this in some sort of demonstrative activity. For example, at the end of a world history course, I might ask students to explain what “culture” means to them and to explain their thinking.

Re-articulation (writing or drawing)

A unique way to engage participants in elicitation methods is to have them write about, rewrite, or draw visual representations of either life experiences or literature that they have read. For example, you could ask them to rewrite a part of the literature they did not like, add a part they thought should be there, or simply extend the ending. Participants can either write or draw these re-articulations. I find that drawing works just as well because, again, the goal is to have participant describe their thinking based on the activity.  

Scenario Decision-Making

Elicitation methods can also examine skills. Researchers can provide participants scenarios and ask them to make decisions. The researchers can document those decisions and analyze the extent to which the participant understands the skill.

  Document, Photograph, or Video Analysis

This is the most basic elicitation in which the researcher provides a document, photograph, or video for the participant to examine. Then, the researcher asks questions about the participants interpretations of the document, photograph, or video. One method that would support this sort of elicitation is to ask the participants to provide images from their everyday words. For example, asking students to document the literacy examples in their homes (i.e., pictures of calendars, bookshelves etc.).  With the availability of one-to-one tech, and iPads, participant documentation is easier.

There are many more methods of data collection also, as well as many variations of the methods described above. The goal for you is to find the data collection methods that are going to give you the best data to answer your research question. If you are unsure, there is nothing wrong with collecting more data than you need to make sure you use effective methods – the only thing you have to lose is time!

Use of Case Studies

Case studies are a popular way for studying phenomena in settings using qualitative methodology. Case studies typically encompass qualitative studies which look closely at what happens when researchers collect data, analyze the data, and present the results. Case studies can focus on a single case or examine a phenomenon across multiple cases. Case studies frame research in a way that allows for rich description of data and depth of analysis.

An advantage of using case study design is that the reader often identifies with the case or phenomena, as well as the participants in the study. Yin (2003) describes case study methodology as inquiry that investigates a contemporary phenomenon within its authentic context. Case studies are particularly appropriate when the boundaries and relationship between the phenomenon and the context are not clear. Case studies relate well with the processes involved in action research. Critics of action research case studies sometimes criticize the inevitable subjectivity, just like general criticisms of action research. Case studies provide researchers opportunities to explore both the how and the why of phenomena in context, while being both exploratory and descriptive.

We want to clarify the differences between methodologies and methods of research. There are methodologies of research, like case study and action research, and methods of data collection. Methodologies like ethnography, narrative inquiry, and case study draw from some similar methods of data collecting that include interviews, collection of artifacts (writings, drawings, images), and observations. The differences between the methodologies include the time-frame for research; the boundaries of the research; and the epistemology.

Triangulation of Data

Triangulation is a method used by qualitative researchers to check and establish trustworthiness in their studies by using and analyzing multiple (three or more) data collection methods to address a research question and develop a consistency of evidence from data sources or approaches. Thus, triangulation facilitates trustworthiness of data through cross verification of evidence, to support claims, from more than two data collection sources. Triangulation also tests the consistency of findings obtained through different data sources and instruments, while minimizing bias in the researcher’s interpretations of the data.

If we think about the example of studying the use of math journals in an elementary classroom, the researcher would want to collect at least three sources of data – the journal prompts, assessment scores, and interviews. When the researcher is analyzing the data, they will want to find themes or evidence across all three data sources to address their research question. In a very basic analysis, if the students demonstrated a deeper level of reflection about math in the journals, their assessment scores improved, and their interviews demonstrated they had more confidence in their number sense and math abilities – then, the researcher could conclude, on a very general level, that math journals improved their students’ math skills, confidence, or abilities. Ideally, the study would examine specific aspects of math to enable deeper analysis of math journals, but this example demonstrates the basic idea of triangulation. In this example, all of the data provided evidence that the intervention of a math journal improved students’ understanding of math, and the three data sources provided trustworthiness for this claim.

Data Collection Checklist

• Based on your research question, what data might you need ?
• What are the multiple ways you could collect that data ?
• How might you document this data , or organize it so that it can be analyzed?
• What methods are most appropriate for your context and timeframe ?
• How much time will your data collection require? How much time can you allow for?
• Will you need to create any data sources (e.g., interview protocol, elicitation materials)?
• Do your data sources all logically support the research question, and each other?
• Does your data collection provide for multiple perspectives ?
• How will your data achieve triangulation in addressing the research question?
• Will you need more than three data sources to ensure triangulation of data?

Action Research Copyright © by J. Spencer Clark; Suzanne Porath; Julie Thiele; and Morgan Jobe is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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• What Is Action Research? | Definition & Examples

What Is Action Research? | Definition & Examples

Published on January 27, 2023 by Tegan George . Revised on January 12, 2024.

Table of contents

Types of action research, action research models, examples of action research, action research vs. traditional research, advantages and disadvantages of action research, other interesting articles, frequently asked questions about action research.

There are 2 common types of action research: participatory action research and practical action research.

• Participatory action research emphasizes that participants should be members of the community being studied, empowering those directly affected by outcomes of said research. In this method, participants are effectively co-researchers, with their lived experiences considered formative to the research process.
• Practical action research focuses more on how research is conducted and is designed to address and solve specific issues.

Both types of action research are more focused on increasing the capacity and ability of future practitioners than contributing to a theoretical body of knowledge.

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Action research is often reflected in 3 action research models: operational (sometimes called technical), collaboration, and critical reflection.

• Operational (or technical) action research is usually visualized like a spiral following a series of steps, such as “planning → acting → observing → reflecting.”
• Collaboration action research is more community-based, focused on building a network of similar individuals (e.g., college professors in a given geographic area) and compiling learnings from iterated feedback cycles.
• Critical reflection action research serves to contextualize systemic processes that are already ongoing (e.g., working retroactively to analyze existing school systems by questioning why certain practices were put into place and developed the way they did).

Action research is often used in fields like education because of its iterative and flexible style.

After the information was collected, the students were asked where they thought ramps or other accessibility measures would be best utilized, and the suggestions were sent to school administrators. Example: Practical action research Science teachers at your city’s high school have been witnessing a year-over-year decline in standardized test scores in chemistry. In seeking the source of this issue, they studied how concepts are taught in depth, focusing on the methods, tools, and approaches used by each teacher.

Action research differs sharply from other types of research in that it seeks to produce actionable processes over the course of the research rather than contributing to existing knowledge or drawing conclusions from datasets. In this way, action research is formative , not summative , and is conducted in an ongoing, iterative way.

As such, action research is different in purpose, context, and significance and is a good fit for those seeking to implement systemic change.

Action research comes with advantages and disadvantages.

• Action research is highly adaptable , allowing researchers to mold their analysis to their individual needs and implement practical individual-level changes.
• Action research provides an immediate and actionable path forward for solving entrenched issues, rather than suggesting complicated, longer-term solutions rooted in complex data.
• Done correctly, action research can be very empowering , informing social change and allowing participants to effect that change in ways meaningful to their communities.

Disadvantages

• Due to their flexibility, action research studies are plagued by very limited generalizability  and are very difficult to replicate . They are often not considered theoretically rigorous due to the power the researcher holds in drawing conclusions.
• Action research can be complicated to structure in an ethical manner . Participants may feel pressured to participate or to participate in a certain way.
• Action research is at high risk for research biases such as selection bias , social desirability bias , or other types of cognitive biases .

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

Sources in this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

George, T. (2024, January 12). What Is Action Research? | Definition & Examples. Scribbr. Retrieved April 8, 2024, from https://www.scribbr.com/methodology/action-research/

Cohen, L., Manion, L., & Morrison, K. (2017). Research methods in education (8th edition). Routledge.

Naughton, G. M. (2001).  Action research (1st edition). Routledge.

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Action Research: Steps, Benefits, and Tips

Introduction

History of action research, what is the definition of action research, types of action research, conducting action research.

Action research stands as a unique approach in the realm of qualitative inquiry in social science research. Rooted in real-world problems, it seeks not just to understand but also to act, bringing about positive change in specific contexts. Often distinguished by its collaborative nature, the action research process goes beyond traditional research paradigms by emphasizing the involvement of those being studied in resolving social conflicts and effecting positive change.

The value of action research lies not just in its outcomes, but also in the process itself, where stakeholders become active participants rather than mere subjects. In this article, we'll examine action research in depth, shedding light on its history, principles, and types of action research.

Tracing its roots back to the mid-20th century, Kurt Lewin developed classical action research as a response to traditional research methods in the social sciences that often sidelined the very communities they studied. Proponents of action research championed the idea that research should not just be an observational exercise but an actionable one that involves devising practical solutions. Advocates believed in the idea of research leading to immediate social action, emphasizing the importance of involving the community in the process.

Applications for action research

Over the years, action research has evolved and diversified. From its early applications in social psychology and organizational development, it has branched out into various fields such as education, healthcare, and community development, informing questions around improving schools, minority problems, and more. This growth wasn't just in application, but also in its methodologies.

How is action research different?

Like all research methodologies, effective action research generates knowledge. However, action research stands apart in its commitment to instigate tangible change. Traditional research often places emphasis on passive observation , employing data collection methods primarily to contribute to broader theoretical frameworks . In contrast, action research is inherently proactive, intertwining the acts of observing and acting.

The primary goal isn't just to understand a problem but to solve or alleviate it. Action researchers partner closely with communities, ensuring that the research process directly benefits those involved. This collaboration often leads to immediate interventions, tweaks, or solutions applied in real-time, marking a departure from other forms of research that might wait until the end of a study to make recommendations.

This proactive, change-driven nature makes action research particularly impactful in settings where immediate change is not just beneficial but essential.

Action research is best understood as a systematic approach to cooperative inquiry. Unlike traditional research methodologies that might primarily focus on generating knowledge, action research emphasizes producing actionable solutions for pressing real-world challenges.

This form of research undertakes a cyclic and reflective journey, typically cycling through stages of planning , acting, observing, and reflecting. A defining characteristic of action research is the collaborative spirit it embodies, often dissolving the rigid distinction between the researcher and the researched, leading to mutual learning and shared outcomes.

Advantages of action research

One of the foremost benefits of action research is the immediacy of its application. Since the research is embedded within real-world issues, any findings or solutions derived can often be integrated straightaway, catalyzing prompt improvements within the concerned community or organization. This immediacy is coupled with the empowering nature of the methodology. Participants aren't mere subjects; they actively shape the research process, giving them a tangible sense of ownership over both the research journey and its eventual outcomes.

Moreover, the inherent adaptability of action research allows researchers to tweak their approaches responsively based on live feedback. This ensures the research remains rooted in the evolving context, capturing the nuances of the situation and making any necessary adjustments. Lastly, this form of research tends to offer a comprehensive understanding of the issue at hand, harmonizing socially constructed theoretical knowledge with hands-on insights, leading to a richer, more textured understanding.

Disadvantages of action research

Like any methodology, action research isn't devoid of challenges. Its iterative nature, while beneficial, can extend timelines. Researchers might find themselves engaged in multiple cycles of observation, reflection, and action before arriving at a satisfactory conclusion. The intimate involvement of the researcher with the research participants, although crucial for collaboration, opens doors to potential conflicts. Through collaborative problem solving, disagreements can lead to richer and more nuanced solutions, but it can take considerable time and effort.

Another limitation stems from its focus on a specific context: results derived from a particular action research project might not always resonate or be applicable in a different context or with a different group. Lastly, the depth of collaboration this methodology demands means all stakeholders need to be deeply invested, and such a level of commitment might not always be feasible.

Examples of action research

To illustrate, let's consider a few scenarios. Imagine a classroom where a teacher observes dwindling student participation. Instead of sticking to conventional methods, the teacher experiments with introducing group-based activities. As the outcomes unfold, the teacher continually refines the approach based on student feedback, eventually leading to a teaching strategy that rejuvenates student engagement.

In a healthcare context, hospital staff who recognize growing patient anxiety related to certain procedures might innovate by introducing a new patient-informing protocol. As they study the effects of this change, they could, through iterations, sculpt a procedure that diminishes patient anxiety.

Similarly, in the realm of community development, a community grappling with the absence of child-friendly public spaces might collaborate with local authorities to conceptualize a park. As they monitor its utilization and societal impact, continual feedback could refine the park's infrastructure and design.

Contemporary action research, while grounded in the core principles of collaboration, reflection, and change, has seen various adaptations tailored to the specific needs of different contexts and fields. These adaptations have led to the emergence of distinct types of action research, each with its unique emphasis and approach.

Collaborative action research

Collaborative action research emphasizes the joint efforts of professionals, often from the same field, working together to address common concerns or challenges. In this approach, there's a strong emphasis on shared responsibility, mutual respect, and co-learning. For example, a group of classroom teachers might collaboratively investigate methods to improve student literacy, pooling their expertise and resources to devise, implement, and refine strategies for improving teaching.

Participatory action research

Participatory action research (PAR) goes a step further in dissolving the barriers between the researcher and the researched. It actively involves community members or stakeholders not just as participants, but as equal partners in the entire research process. PAR is deeply democratic and seeks to empower participants, fostering a sense of agency and ownership. For instance, a participatory research project might involve local residents in studying and addressing community health concerns, ensuring that the research process and outcomes are both informed by and beneficial to the community itself.

Educational action research

Educational action research is tailored specifically to practical educational contexts. Here, educators take on the dual role of teacher and researcher, seeking to improve teaching practices, curricula, classroom dynamics, or educational evaluation. This type of research is cyclical, with educators implementing changes, observing outcomes, and reflecting on results to continually enhance the educational experience. An example might be a teacher studying the impact of technology integration in her classroom, adjusting strategies based on student feedback and learning outcomes.

Community-based action research

Another noteworthy type is community-based action research, which focuses primarily on community development and well-being. Rooted in the principles of social justice, this approach emphasizes the collective power of community members to identify, study, and address their challenges. It's particularly powerful in grassroots movements and local development projects where community insights and collaboration drive meaningful, sustainable change.

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Engaging in action research is both an enlightening and transformative journey, rooted in practicality yet deeply connected to theory. For those embarking on this path, understanding the essentials of an action research study and the significance of a research cycle is paramount.

Understanding the action research cycle

At the heart of action research is its cycle, a structured yet adaptable framework guiding the research. This cycle embodies the iterative nature of action research, emphasizing that learning and change evolve through repetition and reflection.

The typical stages include:

• Identifying a problem : This is the starting point where the action researcher pinpoints a pressing issue or challenge that demands attention.
• Planning : Here, the researcher devises an action research strategy aimed at addressing the identified problem. In action research, network resources, participant consultation, and the literature review are core components in planning.
• Action : The planned strategies are then implemented in this stage. This 'action' phase is where theoretical knowledge meets practical application.
• Observation : Post-implementation, the researcher observes the outcomes and effects of the action. This stage ensures that the research remains grounded in the real-world context.
• Critical reflection : This part of the cycle involves analyzing the observed results to draw conclusions about their effectiveness and identify areas for improvement.
• Revision : Based on the insights from reflection, the initial plan is revised, marking the beginning of another cycle.

Rigorous research and iteration

It's essential to understand that while action research is deeply practical, it doesn't sacrifice rigor . The cyclical process ensures that the research remains thorough and robust. Each iteration of the cycle in an action research project refines the approach, drawing it closer to an effective solution.

The role of the action researcher

The action researcher stands at the nexus of theory and practice. Not just an observer, the researcher actively engages with the study's participants, collaboratively navigating through the research cycle by conducting interviews, participant observations, and member checking . This close involvement ensures that the study remains relevant, timely, and responsive.

Drawing conclusions and informing theory

As the research progresses through multiple iterations of data collection and data analysis , drawing conclusions becomes an integral aspect. These conclusions, while immediately beneficial in addressing the practical issue at hand, also serve a broader purpose. They inform theory, enriching the academic discourse and providing valuable insights for future research.

Identifying actionable insights

Keep in mind that action research should facilitate implications for professional practice as well as space for systematic inquiry. As you draw conclusions about the knowledge generated from action research, consider how this knowledge can create new forms of solutions to the pressing concern you set out to address.

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• Developing the Quantitative Research Design
• Qualitative Descriptive Design
• Design and Development Research (DDR) For Instructional Design
• Qualitative Narrative Inquiry Research
• Action Research Resource

What is Action Research?

Considerations, creating a plan of action.

• Case Study Design in an Applied Doctorate
• SAGE Research Methods
• Research Examples (SAGE) This link opens in a new window
• Dataset Examples (SAGE) This link opens in a new window
• IRB Resource Center This link opens in a new window

Action research is a qualitative method that focuses on solving problems in social systems, such as schools and other organizations. The emphasis is on solving the presenting problem by generating knowledge and taking action within the social system in which the problem is located. The goal is to generate shared knowledge of how to address the problem by bridging the theory-practice gap (Bourner & Brook, 2019). A general definition of action research is the following: “Action research brings together action and reflection, as well as theory and practice, in participation with others, in the pursuit of practical solutions to issues of pressing concern” (Bradbury, 2015, p. 1). Johnson (2019) defines action research in the field of education as “the process of studying a school, classroom, or teacher-learning situation with the purpose of understanding and improving the quality of actions or instruction” (p.255).

Origins of Action Research

Kurt Lewin is typically credited with being the primary developer of Action Research in the 1940s. Lewin stated that action research can “transform…unrelated individuals, frequently opposed in their outlook and their interests, into cooperative teams, not on the basis of sweetness but on the basis of readiness to face difficulties realistically, to apply honest fact-finding, and to work together to overcome them” (1946, p.211).

Sample Action Research Topics

Some sample action research topics might be the following:

• Examining how classroom teachers perceive and implement new strategies in the classroom--How is the strategy being used? How do students respond to the strategy? How does the strategy inform and change classroom practices? Does the new skill improve test scores? Do classroom teachers perceive the strategy as effective for student learning?
• Examining how students are learning a particular content or objectives--What seems to be effective in enhancing student learning? What skills need to be reinforced? How do students respond to the new content? What is the ability of students to understand the new content?
• Examining how education stakeholders (administrator, parents, teachers, students, etc.) make decisions as members of the school’s improvement team--How are different stakeholders encouraged to participate? How is power distributed? How is equity demonstrated? How is each voice valued? How are priorities and initiatives determined? How does the team evaluate its processes to determine effectiveness?
• Examining the actions that school staff take to create an inclusive and welcoming school climate--Who makes and implements the actions taken to create the school climate? Do members of the school community (teachers, staff, students) view the school climate as inclusive? Do members of the school community feel welcome in the school? How are members of the school community encouraged to become involved in school activities? What actions can school staff take to help others feel a part of the school community?
• Examining the perceptions of teachers with regard to the learning strategies that are more effective with special populations, such as special education students, English Language Learners, etc.—What strategies are perceived to be more effective? How do teachers plan instructionally for unique learners such as special education students or English Language Learners? How do teachers deal with the challenges presented by unique learners such as special education students or English Language Learners? What supports do teachers need (e.g., professional development, training, coaching) to more effectively deliver instruction to unique learners such as special education students or English Language Learners?

Remember—The goal of action research is to find out how individuals perceive and act in a situation so the researcher can develop a plan of action to improve the educational organization. While these topics listed here can be explored using other research designs, action research is the design to use if the outcome is to develop a plan of action for addressing and improving upon a situation in the educational organization.

Considerations for Determining Whether to Use Action Research in an Applied Dissertation

• When considering action research, first determine the problem and the change that needs to occur as a result of addressing the problem (i.e., research problem and research purpose). Remember, the goal of action research is to change how individuals address a particular problem or situation in a way that results in improved practices.
• If the study will be conducted at a school site or educational organization, you may need site permission. Determine whether site permission will be given to conduct the study.
• Consider the individuals who will be part of the data collection (e.g., teachers, administrators, parents, other school staff, etc.). Will there be a representative sample willing to participate in the research?
• If students will be part of the study, does parent consent and student assent need to be obtained?
• As you develop your data collection plan, also consider the timeline for data collection. Is it feasible? For example, if you will be collecting data in a school, consider winter and summer breaks, school events, testing schedules, etc.
• As you develop your data collection plan, consult with your dissertation chair, Subject Matter Expert, NU Academic Success Center, and the NU IRB for resources and guidance.
• Action research is not an experimental design, so you are not trying to accept or reject a hypothesis. There are no independent or dependent variables. It is not generalizable to a larger setting. The goal is to understand what is occurring in the educational setting so that a plan of action can be developed for improved practices.

Considerations for Action Research

Below are some things to consider when developing your applied dissertation proposal using Action Research (adapted from Johnson, 2019):

• Research Topic and Research Problem -- Decide the topic to be studied and then identify the problem by defining the issue in the learning environment. Use references from current peer-reviewed literature for support.
• Purpose of the Study —What need to be different or improved as a result of the study?
• Research Questions —The questions developed should focus on “how” or “what” and explore individuals’ experiences, beliefs, and perceptions.
• Theoretical Framework -- What are the existing theories (theoretical framework) or concepts (conceptual framework) that can be used to support the research. How does existing theory link to what is happening in the educational environment with regard to the topic? What theories have been used to support similar topics in previous research?
• Literature Review -- Examine the literature, focusing on peer-reviewed studies published in journal within the last five years, with the exception of seminal works. What about the topic has already been explored and examined? What were the findings, implications, and limitations of previous research? What is missing from the literature on the topic?  How will your proposed research address the gap in the literature?
• Data Collection —Who will be part of the sample for data collection? What data will be collected from the individuals in the study (e.g., semi-structured interviews, surveys, etc.)? What are the educational artifacts and documents that need to be collected (e.g., teacher less plans, student portfolios, student grades, etc.)? How will they be collected and during what timeframe? (Note--A list of sample data collection methods appears under the heading of “Sample Instrumentation.”)
• Data Analysis —Determine how the data will be analyzed. Some types of analyses that are frequently used for action research include thematic analysis and content analysis.
• Implications —What conclusions can be drawn based upon the findings? How do the findings relate to the existing literature and inform theory in the field of education?
• Recommendations for Practice--Create a Plan of Action— This is a critical step in action research. A plan of action is created based upon the data analysis, findings, and implications. In the Applied Dissertation, this Plan of Action is included with the Recommendations for Practice. The includes specific steps that individuals should take to change practices; recommendations for how those changes will occur (e.g., professional development, training, school improvement planning, committees to develop guidelines and policies, curriculum review committee, etc.); and methods to evaluate the plan’s effectiveness.
• Recommendations for Research —What should future research focus on? What type of studies need to be conducted to build upon or further explore your findings.
• Professional Presentation or Defense —This is where the findings will be presented in a professional presentation or defense as the culmination of your research.

Adapted from Johnson (2019).

Considerations for Sampling and Data Collection

Below are some tips for sampling, sample size, data collection, and instrumentation for Action Research:

Sampling and Sample Size

Action research uses non-probability sampling. This is most commonly means a purposive sampling method that includes specific inclusion and exclusion criteria. However, convenience sampling can also be used (e.g., a teacher’s classroom).

Critical Concepts in Data Collection

Triangulation- - Dosemagen and Schwalbach (2019) discussed the importance of triangulation in Action Research which enhances the trustworthiness by providing multiple sources of data to analyze and confirm evidence for findings.

Trustworthiness —Trustworthiness assures that research findings are fulfill four critical elements—credibility, dependability, transferability, and confirmability. Reflect on the following: Are there multiple sources of data? How have you ensured credibility, dependability, transferability, and confirmability? Have the assumptions, limitations, and delimitations of the study been identified and explained? Was the sample a representative sample for the study? Did any individuals leave the study before it ended? How have you controlled researcher biases and beliefs? Are you drawing conclusions that are not supported by data? Have all possible themes been considered? Have you identified other studies with similar results?

Sample Instrumentation

Below are some of the possible methods for collecting action research data:

• Pre- and Post-Surveys for students and/or staff
• Staff Perception Surveys and Questionnaires
• Semi-Structured Interviews
• Focus Groups
• Observations
• Document analysis
• Student work samples
• Classroom artifacts, such as teacher lesson plans, rubrics, checklists, etc.
• Attendance records
• Discipline data
• Journals from students and/or staff
• Portfolios from students and/or staff

A benefit of Action Research is its potential to influence educational practice. Many educators are, by nature of the profession, reflective, inquisitive, and action-oriented. The ultimate outcome of Action Research is to create a plan of action using the research findings to inform future educational practice. A Plan of Action is not meant to be a one-size fits all plan. Instead, it is mean to include specific data-driven and research-based recommendations that result from a detailed analysis of the data, the study findings, and implications of the Action Research study. An effective Plan of Action includes an evaluation component and opportunities for professional educator reflection that allows for authentic discussion aimed at continuous improvement.

When developing a Plan of Action, the following should be considered:

• How can this situation be approached differently in the future?
• What should change in terms of practice?
• What are the specific steps that individuals should take to change practices?
• What is needed to implement the changes being recommended (professional development, training, materials, resources, planning committees, school improvement planning, etc.)?
• How will the effectiveness of the implemented changes be evaluated?
• How will opportunities for professional educator reflection be built into the Action Plan?

Sample Action Research Studies

Anderson, A. J. (2020). A qualitative systematic review of youth participatory action research implementation in U.S. high schools. A merican Journal of Community Psychology, 65 (1/2), 242–257. https://onlinelibrary-wiley-com.proxy1.ncu.edu/doi/epdf/10.1002/ajcp.12389

Ayvaz, Ü., & Durmuş, S.(2021). Fostering mathematical creativity with problem posing activities: An action research with gifted students. Thinking Skills and Creativity, 40. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=edselp&AN=S1871187121000614&site=eds-live

Bellino, M. J. (2018). Closing information gaps in Kakuma Refugee Camp: A youth participatory action research study. American Journal of Community Psychology, 62 (3/4), 492–507. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ofs&AN=133626988&site=eds-live

Beneyto, M., Castillo, J., Collet-Sabé, J., & Tort, A. (2019). Can schools become an inclusive space shared by all families? Learnings and debates from an action research project in Catalonia. Educational Action Research, 27 (2), 210–226. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ehh&AN=135671904&site=eds-live

Bilican, K., Senler, B., & Karısan, D. (2021). Fostering teacher educators’ professional development through collaborative action research. International Journal of Progressive Education, 17 (2), 459–472. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ehh&AN=149828364&site=eds-live

Black, G. L. (2021). Implementing action research in a teacher preparation program: Opportunities and limitations. Canadian Journal of Action Research, 21 (2), 47–71. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ehh&AN=149682611&site=eds-live

Bozkuş, K., & Bayrak, C. (2019). The Application of the dynamic teacher professional development through experimental action research. International Electronic Journal of Elementary Education, 11 (4), 335–352. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ehh&AN=135580911&site=eds-live

Christ, T. W. (2018). Mixed methods action research in special education: An overview of a grant-funded model demonstration project. Research in the Schools, 25( 2), 77–88. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ehh&AN=135047248&site=eds-live

Jakhelln, R., & Pörn, M. (2019). Challenges in supporting and assessing bachelor’s theses based on action research in initial teacher education. Educational Action Research, 27 (5), 726–741. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ehh&AN=140234116&site=eds-live

Klima Ronen, I. (2020). Action research as a methodology for professional development in leading an educational process. Studies in Educational Evaluation, 64 . https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=edselp&AN=S0191491X19302159&site=eds-live

Messiou, K. (2019). Collaborative action research: facilitating inclusion in schools. Educational Action Research, 27 (2), 197–209. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ehh&AN=135671898&site=eds-live

Mitchell, D. E. (2018). Say it loud: An action research project examining the afrivisual and africology, Looking for alternative African American community college teaching strategies. Journal of Pan African Studies, 12 (4), 364–487. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ofs&AN=133155045&site=eds-live

Pentón Herrera, L. J. (2018). Action research as a tool for professional development in the K-12 ELT classroom. TESL Canada Journal, 35 (2), 128–139. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ofs&AN=135033158&site=eds-live

Rodriguez, R., Macias, R. L., Perez-Garcia, R., Landeros, G., & Martinez, A. (2018). Action research at the intersection of structural and family violence in an immigrant Latino community: a youth-led study. Journal of Family Violence, 33 (8), 587–596. https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132323375&site=eds-live

Vaughan, M., Boerum, C., & Whitehead, L. (2019). Action research in doctoral coursework: Perceptions of independent research experiences. International Journal for the Scholarship of Teaching and Learning, 13 . https://proxy1.ncu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=edsdoj&AN=edsdoj.17aa0c2976c44a0991e69b2a7b4f321&site=eds-live

Sample Journals for Action Research

Educational Action Research

Canadian Journal of Action Research

Sample Resource Videos

Call-Cummings, M. (2017). Researching racism in schools using participatory action research [Video]. Sage Research Methods  http://proxy1.ncu.edu/login?URL=https://methods.sagepub.com/video/researching-racism-in-schools-using-participatory-action-research

Fine, M. (2016). Michelle Fine discusses community based participatory action research [Video]. Sage Knowledge. http://proxy1.ncu.edu/login?URL=https://sk-sagepub-com.proxy1.ncu.edu/video/michelle-fine-discusses-community-based-participatory-action-research

Getz, C., Yamamura, E., & Tillapaugh. (2017). Action Research in Education. [Video]. You Tube. https://www.youtube.com/watch?v=X2tso4klYu8

Bradbury, H. (Ed.). (2015). The handbook of action research (3rd edition). Sage.

Bradbury, H., Lewis, R. & Embury, D.C. (2019). Education action research: With and for the next generation. In C.A. Mertler (Ed.), The Wiley handbook of action research in education (1st edition). John Wiley and Sons. https://ebookcentral.proquest.com/lib/nu/reader.action?docID=5683581&ppg=205

Bourner, T., & Brook, C. (2019). Comparing and contrasting action research and action learning. In C.A. Mertler (Ed.), The Wiley handbook of action research in education (1st edition). John Wiley and Sons. https://ebookcentral.proquest.com/lib/nu/reader.action?docID=5683581&ppg=205

Bradbury, H. (2015). The Sage handbook of action research . Sage. https://www-doi-org.proxy1.ncu.edu/10.4135/9781473921290

Dosemagen, D.M. & Schwalback, E.M. (2019). Legitimacy of and value in action research. In C.A. Mertler (Ed.), The Wiley handbook of action research in education (1st edition). John Wiley and Sons. https://ebookcentral.proquest.com/lib/nu/reader.action?docID=5683581&ppg=205

Johnson, A. (2019). Action research for teacher professional development. In C.A. Mertler (Ed.), The Wiley handbook of action research in education (1st edition). John Wiley and Sons. https://ebookcentral.proquest.com/lib/nu/reader.action?docID=5683581&ppg=205

Lewin, K. (1946). Action research and minority problems. In G.W. Lewin (Ed.), Resolving social conflicts: Selected papers on group dynamics (compiled in 1948). Harper and Row.

Mertler, C. A. (Ed.). (2019). The Wiley handbook of action research in education. John Wiley and Sons. https://ebookcentral.proquest.com/lib/nu/detail.action?docID=5683581

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Chapter 7: Action Research

Darshini Ayton

Learning outcomes

Upon completion of this chapter, you should be able to:

• Explain the purpose of an action research approach.
• Explain the action research cycle.
• Describe action research characteristics.

What is action research?

The key concept in action research is change or action .

Action research (also known as ‘participatory action research’) aligns well with the practice of health and social care because researchers and practitioners in this discipline work with people and communities in holistic and relational ways to understand the history, culture and context of the setting. Action research aims to understand the setting and improve it through change or action. 1 This method has its roots in activism and advocacy and is focused on solutions. It is practical and deals with real-world problems and issues. Action research often undergoes phases in seeking to understand the problem, plan a solution, implement the solution and then reflect on or evaluate the solution, cyclically and iteratively. Action research is used in the practice of health and social care because it has two fundamental aims: to improve and to involve. This chapter outlines how this is evident, using examples from the research literature (see Table 7.1.).

Action research as involvement

Action research is a collaborative process between researchers and community members. This process is a core component of action research and represents a significant shift from typical research methods. Through action research, those who are being researched become the researchers, with close consideration given to power dynamics. The research participants become partners in the research and are involved in identifying and prioritising the research area, designing and undertaking data collection, conducting data analysis, and interpreting and disseminating the results. 1 The research partners may be provided with support and training to enable them to undertake these activities and to promote empowerment and capacity building (see examples following). Patient and public involvement in research and healthcare improvement (known in Australia as ‘consumer and community involvement’), has led to action research gaining popularity as a research design that captures the ‘living knowledge’ with, for and by people and communities throughout the research journey.

As an example, in the project Relationships Matter for Youth ‘Aging Out’ of Care, 2 Doucet and colleagues aimed to examine relationships that matter to young people in care and how these relationships can be nurtured and supported over time. The project is a collaborative participatory action research study incorporating photovoice (see Chapter 17 for more information on photovoice). Eight young people, formerly in care and from diverse backgrounds, were recruited to the study. The lead researcher highlighted their own lived experience of the child welfare system and a consciousness of the power dynamics at play. The lead researcher created processes within the project to ensure the youth co-researchers were empowered to share their experiences and that the research team members were working with the youth co-researchers and not for them. These processes included three months of weekly facilitated group discussions, shared meals before project commencement and group outings and community engagement during the project to encourage connection, bonding and trust. The youth co-researchers were provided with photography training and digital cameras. Data collection included the youth co-researchers submitting 6–7 photographs with responses to the following questions for photo contextualisation:

• What does this photograph mean to you? Why is this photo, in particular, most significant to you?
• How do you see this photo as a reflection of the issue of supportive long-term relationships – and one that is relevant to you as a former youth in care in your community?
• What is the relationship between the content of the photo and how you perceive the community or the world around you? What recommendation for change in your community is associated with this photo? 2(para22)

The photographs were showcased at an exhibition that was open to the community; those in attendance included policymakers, advocates and community representatives. The change documented through this project was one of social transformation for the community and self-transformation and healing for the individuals.

Action research as improvement

Action research can be practitioner-led, whereby the study investigates problems identified by the practitioner with the goal of understanding and improving practice over time. Improvement can be both social improvement and healthcare improvement. Healthcare improvement, in particular quality (of healthcare) improvement, has been the focus of clinical practice, research, education and advocacy for more than 30 years. The two main frameworks guiding healthcare and quality improvement efforts are the Plan, Do, Study, Act (PDSA) cycle and Learning Health Systems. 3 Both of these frameworks lend themselves to action research. For example, the PDSA cycle is guided by three overarching questions:

• What are we trying to accomplish?
• How will we know that a change is an improvement?
• What change can we make that will result in improvement? 4(Figure1)

Learning Health Systems is another approach to quality improvement that has gained popularity over the past decade. Data collected by health services (e.g. patient data, health records, laboratory results) are used for knowledge creation in continuous and rapid cycles of study, feedback and practice change. 5 A Learning Health Systems framework incorporates systems science, data science, research methods for real-world contexts, implementation science, participatory research and quality improvement approaches.

Van Heerden and colleagues adopted an action research study to transform the practice and environment of neonatal care in the maternity section of a district hospital in South Africa. The study Strategies to sustain a quality improvement initiative in neonatal resuscitation 6 was conducted in three cycles. Cycle 1 was a situation analysis that explored and described the existing practices and factors influencing neonatal resuscitation and mortality in the hospital through administering questionnaires with nurses (n=69); a focus group with nine doctors; and an analysis of hospital records. A nominal group discussion (structured group discussion including prioritisation) was conducted with 10 managers and staff, followed by a reflective meeting with the project’s steering committee. Cycle 2 developed and implemented strategies to sustain a quality improvement initiative. The strategies addressed training, equipment and stock, staff attitudes, staff shortages, transport transfer for critically ill neonates, and protocols. Cycle 3 was an evaluation of change and sustainability after the implementation of strategies (Cycle 2) and involved the analysis of hospital record data, repeat questionnaire with nurses (n=40), focus group discussion with 10 doctors, steering committee and management members, followed by reflective meetings with the steering committee. Qualitative data was analysed through open coding, and quantitative data was analysed descriptively. The neonatal mortality rate declined (yet still needed to improve) and the implementation strategies facilitated change that led to improvement and practice transformation.

Action research as a methodology or an approach

There is debate as to whether action research is a methodology or an approach, since several different research methods and methodologies can be used. For example, multiple forms of data collection can be utilized, including quantitative data from surveys or medical records, to inform the identification and understanding of the problem and evaluation of the solution. Action research can also draw on descriptive qualitative research, quantitative cross-sectional studies, case studies (see Chapter 8 ), ethnography ( Chapter 9 ) and grounded theory ( Chapter 10 ). Action research can therefore take a purely qualitative approach, or can take a mixed-methods approach. See Table 7.1. for examples of action research studies.

Advantages and disadvantages of action research

Action research addresses practical problems, drawing on principles of empowerment, capacity-building and participation. The research problem to be addressed is typically identified by the community, and the solutions are for the community. The research participants are collaborators in the research process. The examples presented in this chapter demonstrate how the research collaborators and co-researchers received training and support to lead elements of the project. Another advantage of action research is that it is a continuous cycle of development. Hence, the approach is iterative and the full solution can take multiple cycles and iterations to develop and sustain. 7,8

Since action research is fundamentally about relationships and integrating research into the real world, studies can take years to result in a solution. It is important to be able to adapt and be flexible in response to community and stakeholder needs and contexts. The research can therefore be constrained by what is practical and also ethical within the setting. This may limit the scope and scale of the research and compromise its rigour. Action research can also create unanticipated work for community members and participants because they are not usually involved in research in this way, and thus training may be required, as well as remuneration for time and experience. 7,8

Action research is a research design in which researchers and community members work together to identify problems, design and implement solutions and evaluate the impact of these solutions. Change or action is a core component of this research design.

• Baum F, MacDougall C, Smith D. Participatory action research. J Epidemiol Community Health .  2006;60(10):854-857. doi:10.1136/jech.2004.028662
• Doucet M, Pratt H, Dzhenganin M, Read J. Nothing About Us Without Us: Using Participatory Action Research (PAR) and arts-based methods as empowerment and social justice tools in doing research with youth ‘aging out’ of care. Child Abuse Negl . 2022;130:105358. doi: 10.1016/j.chiabu.2021.105358
• Taylor J, Coates E, Wessels B, Mountain G, Hawley MS. Implementing solutions to improve and expand telehealth adoption: participatory action research in four community healthcare settings. BMC Health Serv Res . 2015;15:529. doi:10.1186/s12913-015-1195-3
• Taylor MJ, McNicholas C, Nicolay C, Darzi A, Bell D, Reed JE. Systematic review of the application of the plan-do-study-act method to improve quality in healthcare. BMJ Qual Saf .  2014;23(4):290-298. doi:10.1136/bmjqs-2013-001862
• Menear M, Blanchette MA, Demers-Payette O, Roy D. A framework for value-creating learning health systems. Health Res Policy Syst . 2019;17(1):79. doi:10.1186/s12961-019-0477-3
• Van Heerden C, Maree C, Janse Van Rensburg ES. Strategies to sustain a quality improvement initiative in neonatal resuscitation. Afr J Prim Health Care Fam Med . 2016;8(2):a958. doi:10.4102/phcfm.v8i2.958
• Liamputtong P. Qualitative Research Methods . 5th ed. Oxford University Press; 2020.
• Liamputtong P, Ezzy D. Qualitative Research Methods: A Health Focus . Oxford University Press; 1999.
• Middleton TRF, Schinke RJ, Lefebvre D, Habra B, Coholic D, Giffin C. Critically examining a community-based participatory action research project with forced migrant youth. Sport Soc . 2021;25(2):418-433. doi:10.1080/17430437.2022.2017619

Qualitative Research – a practical guide for health and social care researchers and practitioners Copyright © 2023 by Darshini Ayton is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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Qualitative study design: Action research

• Qualitative study design
• Phenomenology
• Grounded theory
• Ethnography
• Narrative inquiry
• Action research
• Case Studies
• Field research
• Focus groups
• Observation
• Surveys & questionnaires
• Study Designs Home

Action research / Participatory Action Research

These methods focus on the emancipation, collaboration and empowerment of the participants. This methodology is appropriate for collaborative research with groups, especially marginalised groups, where there is more flexibility in how the research is conducted and considers feedback from the participants. 

Has three primary characteristics:  

Action oriented, participants are actively involved in the research.

involvement by participants in the research, collaborative process between participant and researcher - empowerment of participants. The participants have more of a say in what is being researched and how they want the research to be conducted.

cycle is iterative so that it is flexible and responsive to a changing situation.  

• Questionnaires
• Oral recordings
• Focus groups,
• Photovoice (use of images or video to capture the local environment / community and to share with others)
• Informal conversations 

Produces knowledge from marginalised people's point of view and can lead to more personalised interventions.  

Provides a voice for people to speak about their issues and the ability to improve their own lives. People take an active role in implementing any actions arising from the research. 

Transforms social reality by linking theory and practice.  

Limitations

Open ended questions are mainly used, and these can be misinterpreted by researcher – data needs to be cross-checked with other sources.

Data ownership between researcher and research participants needs to be negotiated and clearly stated from the beginning of the project.

Ethical considerations with privacy and confidentiality.

This method is not considered scientific as it is more fluid in its gathering of information and is considered an unconventional research method – thus it may not attract much funding.

Example questions

• What is the cultural significance of yarning amongst Aboriginal people?  

Macro Question:

• “What would it take to improve the stability of young people’s living situations?”  

Micro Questions:  

• “What can we do to better engage with accommodation service providers?”  
• “What can we do to improve the service knowledge of young people?”  
• “What can we do to measure stability outcomes for our clients?”  

(Department of Social Services)  

Example studies

• Miller, A., Massey, P. D., Judd, J., Kelly, J., Durrheim, D. N., Clough, A. R., . . . Saggers, S. (2015). Using a participatory action research framework to listen to Aboriginal and Torres Strait Islander people in Australia about pandemic influenza.  Rural and Remote Health , 15(3), 2923-2923.  
• Spinney, A. (2013). Safe from the Start? An Action Research Project on Early Intervention Materials for Children Affected by Domestic and Family Violence. Children & Society, 27(5), 397-405. doi:10.1111/j.1099-0860.2012.00454.x 
• Department of Social Services. (2019).  On PAR  - Using participatory action research to improve early intervention. 
• Liamputtong, P. (2013). Qualitative research methods (4th ed.). South Melbourne: Oxford  University Press. 
• Mills, J., & Birks, M. (2014). Qualitative Methodology: A Practical Guide. Retrieved from https://methods.sagepub.com/book/qualitative-methodology-a-practical-guide doi:10.4135/9781473920163 
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• Research article
• Open access
• Published: 12 December 2018

Participatory action research, mixed methods, and research teams: learning from philosophically juxtaposed methodologies for optimal research outcomes

• Marguerite C. Sendall   ORCID: orcid.org/0000-0002-1239-9173 1 ,
• Laura K. McCosker   ORCID: orcid.org/0000-0001-9107-9401 1 ,
• Alison Brodie   ORCID: orcid.org/0000-0003-2786-8152 1 ,
• Melissa Hill   ORCID: orcid.org/0000-0001-8077-0889 1 &
• Phil Crane   ORCID: orcid.org/0000-0001-9105-1089 2  

BMC Medical Research Methodology volume  18 , Article number:  167 ( 2018 ) Cite this article

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Workplace health interventions incorporating qualitative and quantitative components (mixed methods) within a Participatory Action Research approach can increase understanding of contextual issues ensuring realistic interventions which influence health behaviour. Mixed methods research teams, however, face a variety of challenges at the methodological and expertise levels when designing actions and interventions. Addressing these challenges can improve the team’s functionality and lead to higher quality health outcomes. In this paper we reflect on the data collection, implementation and data analysis phases of a mixed methods workplace health promotion project and discuss the challenges which arose within our multidisciplinary team.

This project used mixed methods within a Participatory Action Research approach to address workers’ sun safety behaviours in 14 outdoor workplaces in Queensland, Australia, and elucidate why certain measures succeeded (or failed) at the worker and management level. The project integrated qualitative methods such as policy analysis and interviews, with a range of quantitative methods – including worker surveys, ultraviolet radiation (UVR) exposure measurement, and implementation cost analyses.

The research team found the integration of qualitative and quantitative analyses within the Participatory Action Research process to be challenging and a cause of tensions. This had a negative impact on the data analysis process and reporting of results, and the complexity of qualitative analysis was not truly understood by the quantitative team. Once all researchers recognised qualitative and quantitative data would be equally beneficial to the Participatory Action Research process, methodological bias was overcome to a degree to which the team could work cooperatively.

Conclusions

Mixed methods within a Participatory Action Research approach may allow a research team to discuss, reflect and learn from each other, resulting in broadened perspectives beyond the scope of any single research methodology. However, cohesive and supportive teams take constant work and adjustment under this approach, as knowledge and understanding is gained and shared. It is important researchers are cognisant of, and learn from, potential tensions within research teams due to juxtaposed philosophies, methodologies and experiences, if the team is to function efficiently and positive outcomes are to be achieved.

Peer Review reports

In health outcomes research, it is important to use appropriate methods to obtain the best possible data with minimal bias [ 1 ]. Previous research to improve sun safety in the workplace has predominately used quantitative methods to study improvement in sun safety behaviours [ 2 , 3 ], however, quantitative research alone does allow complex issues like sun safety behaviour to be fully understood. It offers little contextualised evidence explaining why certain sun safety interventions have or have not worked, and it cannot help identify essential elements to be included in multi-component sun safety health promotion interventions [ 2 ]. Mixed methods within a Participatory Action Research (PAR), as applied to workplace health promotion for sun safety, can provide a greater understanding which ensures realistic interventions to influence behavioural change in specific in settings [ 4 ]. However, the mixed methods approach can raise several challenges during planning, implementation and analysis stages [ 5 , 6 , 7 , 8 , 9 , 10 ].

In particular, teamwork has been identified as a key issue impacting on the potential effectiveness of mixed methods research [ 11 ]. Mixed methods teams can face a variety of challenges while designing actions and interventions at the methodological and expertise levels. Qualitative and quantitative researchers hold juxtaposed ontological or epistemological stances which make teamwork difficult or impossible [ 11 , 12 ]. To overcome potential challenges, a successful mixed methods research team requires a good understanding of the nature of the research questions and the expertise required to address them [ 13 ]. Team members must be willing to learn each other’s approach to share knowledge, build trust, and develop a common language [ 13 ].

In this paper we reflect on the data collection, implementation and data analysis phases of a mixed methods workplace health promotion project addressing sun safety behaviours in outdoor workers. We discuss the challenges which arose within our multidisciplinary team in designing and implementing intervention components, and explain how we learnt to effectively minimise or overcome these challenges.

Project description

The aim of this project was to implement a comprehensive health promotion intervention using mixed methods within a Participatory Action Research (PAR) approach to influence the sun-related attitudes and behaviours of outdoor workers in workplace settings in Queensland, Australia. Recruitment and baseline characteristics are described in detail previously [ 14 ]. Briefly, 14 workplaces were recruited. These included small and large organisations across the rural, building and construction, public and local government sectors in geographically dispersed regions of Queensland. All the organisations employed outdoor workers, defined as individuals who work outdoors for most of the day on at least 5 days per week.

Each workplace nominated a workplace representative as a champion. The champions, with other workers, were invited to participate in the development of a plan for the suite of sun-safe health promotion interventions for the workplace. These plans were referred to as Sun Protection Action Plans and were developed over time in partnership with the research team. They encompassed six domains, as appropriate to, and based on data from, each workplace: policy, structure and environment, Personal Protective Equipment (PPE), education and awareness, role modelling, and skin examinations.

Using the principles and processes of a PAR approach, mixed methods were applied concurrently throughout the development of the Action Plans and data collection stages [ 9 ]. Two preliminary tools were used to engage workers and workplaces, identify the priority given to sun safety within each organisation, and gather a policy baseline. The first was a telephone-based screening tool, comprised of quantitative and qualitative questions about workplace demographics, locations and structures and existing workplace policies and procedures related to sun safety and UVR exposure. A second, more comprehensive situational analysis tool, conducted in person with the representative in each workplace, involved the systematic collection of detailed information about existing workplace sun safety policies, procedures and strategies,

The combination of data from these tools enabled a comprehensive picture of existing sun safety strategies and culture to be developed, to inform the sun safety interventions for each workplace. As the researchers were on-site to conduct the more comprehensive situational analysis, it was prudent to integrate additional research strategies targeting workers. For example, a discussion group with outdoor workers from each site was undertaken. The discussion group was guided by PAR principles to allow the discovery of information grounded in the workers’ realities. The discussion groups involved generating ideas from workers about strategies to promote and increase sun safety practices in their workplace. The discussion was transparent, free-flowing and allowed a ‘heads together’ way of thinking. It valued the workers’ inputs, took advantage of their existing skills, knowledge and resources and stimulated ideas. In most cases, supervisors were excluded from the discussion to allow workers to speak openly. All discussions were documented and transcribed to identify key themes.

Quantitative measures employed as the project developed included 1) the distribution of ultraviolet radiation (UVR) dosimeters to workers to measure their UVR exposure across one working day, and 2) a telephone survey with a sample of the workers from each workplace, to collect information about workers’ demographics, behaviours and attitudes related to sun exposure and protection in the workplace, knowledge of workplace sun protection policy, and perception of their workplaces’ level of support for sun protection. Similar data was collected during the research evaluation stage to allow for pre and post-intervention comparisons. Additional questions about workers’ perception of changes to workplace sun safety protocols were asked.

Working with a diverse team

This project was conducted by a multidisciplinary team of quantitative and qualitative researchers with various expertise in epidemiology, public health, health promotion, health economics, and the social sciences.

The team consisted of eight researches and two professional staff. All researchers held a doctoral degree. Five researchers were quantitative experts, including the Project Lead. The three qualitative methodologists were assigned an equal number of geographically feasible and like workplaces to undertake fieldwork with a Research Assistant. The Project Office consisted of two contracted professional staff – a Project Manager and a Research Assistant. Casual professional support staff was provided when needed, for example, undertaking surveys. Six researchers were located within travelling distance of the project office. One researcher was located in another Australian city and one researcher was located in Canada. The team met once a fortnight in the planning phase, and once a month during fieldwork. Six team members attended in person and two team members attended by Skype. Most team members attended most meetings. Two team members attended meetings irregularly. Each meeting was guided by an agenda developed by the project lead.

Before the project commenced, the research team set the foundation for how they would use mixed methods within the PAR approach to address the research question. The research team needed to collectively define the meaning of mixed methods and PAR to create a common language. This was complicated by the diversity of methodological expertise and experience with PAR as a research approach. Through early discussions, the researchers learnt from each other to achieve an understanding and consensus about what data each method was collecting within the PAR process, and how this information would be useful to all relevant parties: the funding body, the health promotion community, the workers and the workplaces and the skin cancer research community. This was a healthy debate which required researchers from both methodologies to consider the scope and sequence of the research. Once all researchers recognised qualitative and quantitative data would be equally beneficial to the PAR process, methodological bias was overcome to a degree to which the team could work cooperatively.

A methods paper for use by the research team was then drafted by the project manager to outline the selected mixed methods, explain how these would be integrated, and indicate the logical sequence of PAR activities. This paper allowed the research team to conceptualise the mixed methods not as designs, but as a set of interactive parts [ 15 ]. This methods paper was crucial in guiding the research team in applying the principles and processes of PAR in the project.

Researchers were encouraged to contribute to conversations from their own methodological perspective, and make these perspectives available for discussion and debate. These shared learnings fostered respect amongst the team and enabled effective information sharing because researchers felt safe voicing concerns [ 16 ]. As respect from learning grew among the team, leadership became more collaborative and the hierarchy underpinning the team dissolved. A separate language was not created but the focus remained on understanding and learning the language used by other researchers to keep the underlying methodologies of qualitative and quantitative research clear. This process of learning was a fundamental step in facilitating the team’s effective use of the mixed methods within the PAR approach.

There was, however, tension within the research team about how best to integrate qualitative and quantitative data collection methods without exceeding the project timeline. Such tension regarding data methods integration is common in mixed methods research and is an often-cited barrier to conducting research of this type [ 5 , 9 ]. After listening, reflecting and learning it was agreed data collection had to be resilient and flexible [ 16 ], combining qualitative and quantitative methods to ensure each was not wholly dependent on the other. For example, as the team became aware of rising tensions, ‘methodology’ was tabled as an agenda item at each team meeting. This ensured there was explicit permission and opportunity for team members to ask questions and clarify concerns about underlying methodological reasoning, engage in discussion about integrating approaches and ensuring rigour. This helped avoid the added complexity of problems and potential setbacks normally associated with the interdependency of multiple methods [ 16 ] throughout the PAR process.

The team’s different approaches to mixed methods had a negative impact on the data analysis process and reporting of results. The complexity of qualitative analysis was not truly understood and recognised by the quantitative team. For example, analysis of quantitative results was prioritised to meet the final report deadline. The analysis of qualitative findings was left until the quantitative results were completed. The limited time for qualitative analysis impacted a rigorous analytical process and the opportunity to present quantitative and qualitative outcomes as a comprehensive integrated whole. Subsequently, the project’s qualitative and quantitative outcomes were reported separately in the final report [ 17 , 18 ].

A key philosophy of this project was the participatory and collaborative nature of planning between workers and each workplace and the research team. The qualitative methodologist worked directly with workplaces acting as a link to the project office. Over the 12-month intervention period, these team members developed rapport with the workplaces arriving at a deep understanding of the workplace context. This is consistent with the PAR approach. Time was taken to consider outcomes from the situational analysis tools and discussion groups, to negotiate meaning with stakeholders, to build a shared understanding of the pre-intervention data and to decide upon the most effective strategies for the workplace. This involved the research team sharing and learning from insights of existing practices and piecing together a plan which best suited individual workplaces. Throughout the intervention period, the research team met regularly to discuss fieldwork progress. This process was challenged however, because 1) some team members did not attend meetings, 2) team meetings were dominated by quantitative experts and 3) the same challenges were discussed from one meeting to the next.

The Sun Protection Action Plans were implemented and progress monitored by the research team, with all nuances, key outcomes, barriers and facilitating factors documented in a case study design. This created further discussion and learning amongst the research team. Team members were required to ‘step back’ from their own area of expertise and genuinely endeavour to understand another’s view and to situate that view within the projects. This was often difficult because not all team members had the experience of working in mixed methods teams. The team faced the challenges of being true to the PAR process by not confining the development of case studies to rigid boundaries, yet ensuring the case studies reflected consistent themes. For example, a proposed research design involved grouping workplaces to control and case. Case workplaces would be assigned a suite of interventions. This design is not viable due to the highly contextual nature of workplaces but remained on the team meetings’ agenda despite discussions. The case studies aimed to consider the voice and perspective of management (policy analysis, the relevant groups of workers (survey research) and the interaction between them. Case study development was a joint exercise between the workers and each workplace and the research team and no unresolved conflicts arose during their construction.

There is a lack of research about optimal ways for teams to function in mixed method studies [ 19 ]. A significant learning from this project which could benefit the functionality and cohesiveness of mixed methods research teams is an understanding of the importance of commencing from a platform of social inquiry and extending to a common analytical space, rather than relying on individual investigators’ philosophical assumptions. For example, there was no early discussion about the weighting of methodologies – was this a quantitative project, with some qualitative injury, or was this a qualitative project, with some quantitative inquiry? Team members may have assumed equal or unequal weighting. Either way, the integration of these analyses within the PAR process was challenging. For example, there was an unspoken but obvious positioning between the methodologies. Qualitative methodology was highly positioned within each workplace and fieldwork and quantitative methodology was highly positioned in the project office and the project our-outcomes. The analysis strategy originally involved use of qualitative data to interpret the quantitative results and then the integration and comparison of qualitative and quantitative phases at the beginning and the end of the project. As the project developed, the reporting of quantitative results was prioritised, particularly at the evaluation stage.

Lessons learnt and future challenges

Ineffective communication underpinned by a lack of a shared language or ‘methodological disrespect’ may impede successful teamwork [ 16 ]. This issue of compatibility between research philosophies arose while the project was being designed, and required the research team to listen, reflect and learn about each other’s perspectives. Although consensus was reached about the integration of quantitative and qualitative methodologies within a PAR approach, there was general learning amongst the researchers that a shift away from a preoccupation with explicit assumptive differences among paradigms, and toward other characteristics of social inquiry traditions was required. Paradigms are not universally accepted as necessary among mixed methods researchers as they can marginalise other beliefs or force researchers to adhere rigidly to a set of beliefs [ 20 ]. However, they are useful to guide inquiry, especially for novice mixed methods researchers or teams. Where paradigms are applied, they are regarded as not static, concrete entities which restrict a research process [ 21 ] but rather, a system of beliefs and practices which influence what questions are asked [ 21 ]. Moving away from qualitative or quantitative methodologies and towards those inherent in mixed methods within a PAR approach allows researchers to transition beyond the methodological binary and ‘paradigm wars’ that have characterised social science research for several decades [ 20 ].

This project subscribed most fully, though not in a contrived way, to the transformative-emancipatory paradigm as outlined by [ 22 ]. This perspective is characterised by the intentional collaboration with minority and marginalised groups to address a research problem [ 22 ]. While it was not the intention of the research team to subscribe to a paradigm, the use of action research to guide this project meant this happened incidentally. Purposeful use of a guiding paradigm may have allowed the research team to learn from one another and reach a consensus about the use of qualitative and quantitative methods earlier in the planning stages of the project.

The use of mixed methods within a PAR approach will continue to expand across disciplines and fields. It is important researchers are cognisant of, and learn from, potential tensions within research teams due to juxtaposed philosophies, methodologies and experiences. If positive outcomes are to be achieved, learning from each other for the efficient functioning of the research team is just as important as the effective integration of qualitative and quantitative research methods. Cohesive and supportive teams take constant work and adjustment as knowledge and understanding is gained and shared. Discussion of strategies to facilitate team cohesiveness in mixed methods projects is still widely unavailable in the literature.

Even though challenges were faced by our research team at every phase of the project, mixed methods within a PAR approach allowed the team to discuss, reflect and learn from each other, resulting in broadened perspectives beyond the scope of any single research methodology.

Abbreviations

Participatory action research

Personal protective equipment

Ultraviolet radiation

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Acknowledgements

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Sendall, M.C., McCosker, L.K., Brodie, A. et al. Participatory action research, mixed methods, and research teams: learning from philosophically juxtaposed methodologies for optimal research outcomes. BMC Med Res Methodol 18 , 167 (2018). https://doi.org/10.1186/s12874-018-0636-1

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Data Analysis in Qualitative Research: A Brief Guide to Using Nvivo

MSc, PhD, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

Qualitative data is often subjective, rich, and consists of in-depth information normally presented in the form of words. Analysing qualitative data entails reading a large amount of transcripts looking for similarities or differences, and subsequently finding themes and developing categories. Traditionally, researchers ‘cut and paste’ and use coloured pens to categorise data. Recently, the use of software specifically designed for qualitative data management greatly reduces technical sophistication and eases the laborious task, thus making the process relatively easier. A number of computer software packages has been developed to mechanise this ‘coding’ process as well as to search and retrieve data. This paper illustrates the ways in which NVivo can be used in the qualitative data analysis process. The basic features and primary tools of NVivo which assist qualitative researchers in managing and analysing their data are described.

QUALITATIVE RESEARCH IN MEDICINE

Qualitative research has seen an increased popularity in the last two decades and is becoming widely accepted across a wide range of medical and health disciplines, including health services research, health technology assessment, nursing, and allied health. 1 There has also been a corresponding rise in the reporting of qualitative research studies in medical and health related journals. 2

The increasing popularity of qualitative methods is a result of failure of quantitative methods to provide insight into in-depth information about the attitudes, beliefs, motives, or behaviours of people, for example in understanding the emotions, perceptions and actions of people who suffer from a medical condition. Qualitative methods explore the perspective and meaning of experiences, seek insight and identify the social structures or processes that explain people”s behavioural meaning. 1 , 3 Most importantly, qualitative research relies on extensive interaction with the people being studied, and often allows researchers to uncover unexpected or unanticipated information, which is not possible in the quantitative methods. In medical research, it is particularly useful, for example, in a health behaviour study whereby health or education policies can be effectively developed if reasons for behaviours are clearly understood when observed or investigated using qualitative methods. 4

ANALYSING QUALITATIVE DATA

Qualitative research yields mainly unstructured text-based data. These textual data could be interview transcripts, observation notes, diary entries, or medical and nursing records. In some cases, qualitative data can also include pictorial display, audio or video clips (e.g. audio and visual recordings of patients, radiology film, and surgery videos), or other multimedia materials. Data analysis is the part of qualitative research that most distinctively differentiates from quantitative research methods. It is not a technical exercise as in quantitative methods, but more of a dynamic, intuitive and creative process of inductive reasoning, thinking and theorising. 5 In contrast to quantitative research, which uses statistical methods, qualitative research focuses on the exploration of values, meanings, beliefs, thoughts, experiences, and feelings characteristic of the phenomenon under investigation. 6

Data analysis in qualitative research is defined as the process of systematically searching and arranging the interview transcripts, observation notes, or other non-textual materials that the researcher accumulates to increase the understanding of the phenomenon. 7 The process of analysing qualitative data predominantly involves coding or categorising the data. Basically it involves making sense of huge amounts of data by reducing the volume of raw information, followed by identifying significant patterns, and finally drawing meaning from data and subsequently building a logical chain of evidence. 8

Coding or categorising the data is the most important stage in the qualitative data analysis process. Coding and data analysis are not synonymous, though coding is a crucial aspect of the qualitative data analysis process. Coding merely involves subdividing the huge amount of raw information or data, and subsequently assigning them into categories. 9 In simple terms, codes are tags or labels for allocating identified themes or topics from the data compiled in the study. Traditionally, coding was done manually, with the use of coloured pens to categorise data, and subsequently cutting and sorting the data. Given the advancement of software technology, electronic methods of coding data are increasingly used by qualitative researchers.

Nevertheless, the computer does not do the analysis for the researchers. Users still have to create the categories, code, decide what to collate, identify the patterns and draw meaning from the data. The use of computer software in qualitative data analysis is limited due to the nature of qualitative research itself in terms of the complexity of its unstructured data, the richness of the data and the way in which findings and theories emerge from the data. 10 The programme merely takes over the marking, cutting, and sorting tasks that qualitative researchers used to do with a pair of scissors, paper and note cards. It helps to maximise efficiency and speed up the process of grouping data according to categories and retrieving coded themes. Ultimately, the researcher still has to synthesise the data and interpret the meanings that were extracted from the data. Therefore, the use of computers in qualitative analysis merely made organisation, reduction and storage of data more efficient and manageable. The qualitative data analysis process is illustrated in Figure 1 .

Qualitative data analysis flowchart

USING NVIVO IN QUALITATIVE DATA ANALYSIS

NVivo is one of the computer-assisted qualitative data analysis softwares (CAQDAS) developed by QSR International (Melbourne, Australia), the world’s largest qualitative research software developer. This software allows for qualitative inquiry beyond coding, sorting and retrieval of data. It was also designed to integrate coding with qualitative linking, shaping and modelling. The following sections discuss the fundamentals of the NVivo software (version 2.0) and illustrates the primary tools in NVivo which assist qualitative researchers in managing their data.

Key features of NVivo

To work with NVivo, first and foremost, the researcher has to create a Project to hold the data or study information. Once a project is created, the Project pad appears ( Figure 2 ). The project pad of NVivo has two main menus: Document browser and Node browser . In any project in NVivo, the researcher can create and explore documents and nodes, when the data is browsed, linked and coded. Both document and node browsers have an Attribute feature, which helps researchers to refer the characteristics of the data such as age, gender, marital status, ethnicity, etc.

Project pad with documents tab selected

The document browser is the main work space for coding documents ( Figure 3 ). Documents in NVivo can be created inside the NVivo project or imported from MS Word or WordPad in a rich text (.rtf) format into the project. It can also be imported as a plain text file (.txt) from any word processor. Transcripts of interview data and observation notes are examples of documents that can be saved as individual documents in NVivo. In the document browser all the documents can be viewed in a database with short descriptions of each document.

Document browser with coder and coding stripe activated

NVivo is also designed to allow the researcher to place a Hyperlink to other files (for example audio, video and image files, web pages, etc.) in the documents to capture conceptual links which are observed during the analysis. The readers can click on it and be taken to another part of the same document, or a separate file. A hyperlink is very much like a footnote.

The second menu is Node explorer ( Figure 4 ), which represents categories throughout the data. The codes are saved within the NVivo database as nodes. Nodes created in NVivo are equivalent to sticky notes that the researcher places on the document to indicate that a particular passage belongs to a certain theme or topic. Unlike sticky notes, the nodes in NVivo are retrievable, easily organised, and give flexibility to the researcher to either create, delete, alter or merge at any stage. There are two most common types of node: tree nodes (codes that are organised in a hierarchical structure) and free nodes (free standing and not associated with a structured framework of themes or concepts). Once the coding process is complete, the researcher can browse the nodes. To view all the quotes on a particular Node, select the particular node on the Node Explorer and click the Browse button ( Figure 5 ).

Node explorer with a tree node highlighted

Browsing a node

Coding in NVivo using Coder

Coding is done in the document browser. Coding involves the desegregation of textual data into segments, examining the data similarities and differences, and grouping together conceptually similar data in the respective nodes. 11 The organised list of nodes will appear with a click on the Coder button at the bottom of document browser window.

To code a segment of the text in a project document under a particular node, highlight the particular segment and drag the highlighted text to the desired node in the coder window ( Figure 3 ). The segments that have been coded to a particular node are highlighted in colours and nodes that have attached to a document turns bold. Multiple codes can be assigned to the same segment of text using the same process. Coding Stripes can be activated to view the quotes that are associated with the particular nodes. With the guide of highlighted text and coding stripes, the researcher can return to the data to do further coding or refine the coding.

Coding can be done with pre-constructed coding schemes where the nodes are first created using the Node explorer followed by coding using the coder. Alternatively, a bottom-up approach can be used where the researcher reads the documents and creates nodes when themes arise from the data as he or she codes.

Making and using memos

In analysing qualitative data, pieces of reflective thinking, ideas, theories, and concepts often emerge as the researcher reads through the data. NVivo allows the user the flexibility to record ideas about the research as they emerge in the Memos . Memos can be seen as add-on documents, treated as full status data and coded like any other documents. 12 Memos can be placed in a document or at a node. A memo itself can have memos (e.g. documents or nodes) linked to it, using DocLinks and NodeLinks .

Creating attributes

Attributes are characteristics (e.g. age, marital status, ethnicity, educational level, etc.) that the researcher associates with a document or node. Attributes have different values (for example, the values of the attribute for ethnicity are ‘Malay’, ‘Chinese’ and ‘Indian’). NVivo makes it possible to assign attributes to either document or node. Items in attributes can be added, removed or rearranged to help the researcher in making comparisons. Attributes are also integrated with the searching process; for example, linking the attributes to documents will enable the researcher to conduct searches pertaining to documents with specified characteristics ( Figure 6 ).

Document attribute explorer

Search operation

The three most useful types of searches in NVivo are Single item (text, node, or attribute value), Boolean and Proximity searches. Single item search is particularly important, for example, if researchers want to ensure that every mention of the word ‘cure’ has been coded under the ‘Curability of cervical cancer’ tree node. Every paragraph in which this word is used can be viewed. The results of the search can also be compiled into a single document in the node browser and by viewing the coding stripe. The researcher can check whether each of the resulting passages has been coded under a particular node. This is particularly useful for the researcher to further determine whether conducting further coding is necessary.

Boolean searches combine codes using the logical terms like ‘and’, ‘or’ and ‘not’. Common Boolean searches are ‘or’ (also referred to as ‘combination’ or ‘union’) and ‘and’ (also called ‘intersection’). For example, the researcher may wish to search for a node and an attributed value, such as ‘ever screened for cervical cancer’ and ‘primary educated’. Search results can be displayed in matrix form and it is possible for the researcher to perform quantitative interpretations or simple counts to provide useful summaries of some aspects of the analysis. 13 Proximity searches are used to find places where two items (e.g. text patterns, attribute values, nodes) appear near each other in the text.

Using models to show relationships

Models or visualisations are an essential way to describe and explore relationships in qualitative research. NVivo provides a Modeler designated for visual exploration and explanation of relationships between various nodes and documents. In Model Explorer, the researcher can create, label and connect ideas or concepts. NVivo allows the user to create a model over time and have any number of layers to track the progress of theory development to enable the researcher to examine the stages in the model-building over time ( Figure 7 ). Any documents, nodes or attributes can be placed in a model and clicking on the item will enable the researcher to inspect its properties.

Model explorer showing the perceived risk factors of cervical cancer

NVivo has clear advantages and can greatly enhance research quality as outlined above. It can ease the laborious task of data analysis which would otherwise be performed manually. The software certainly removes the tremendous amount of manual tasks and allows more time for the researcher to explore trends, identify themes, and make conclusions. Ultimately, analysis of qualitative data is now more systematic and much easier. In addition, NVivo is ideal for researchers working in a team as the software has a Merge tool that enables researchers that work in separate teams to bring their work together into one project.

The NVivo software has been revolutionised and enhanced recently. The newly released NVivo 7 (released March 2006) and NVivo 8 (released March 2008) are even more sophisticated, flexible, and enable more fluid analysis. These new softwares come with a more user-friendly interface that resembles the Microsoft Windows XP applications. Furthermore, they have new data handling capacities such as to enable tables or images embedded in rich text files to be imported and coded as well. In addition, the user can also import and work on rich text files in character based languages such as Chinese or Arabic.

To sum up, qualitative research undoubtedly has been advanced greatly by the development of CAQDAS. The use of qualitative methods in medical and health care research is postulated to grow exponentially in years to come with the further development of CAQDAS.

More information about the NVivo software

Detailed information about NVivo’s functionality is available at http://www.qsrinternational.com . The website also carries information about the latest versions of NVivo. Free demonstrations and tutorials are available for download.

ACKNOWLEDGEMENT

The examples in this paper were adapted from the data of the study funded by the Ministry of Science, Technology and Environment, Malaysia under the Intensification of Research in Priority Areas (IRPA) 06-02-1032 PR0024/09-06.

TERMINOLOGY

Attributes : An attribute is a property of a node, case or document. It is equivalent to a variable in quantitative analysis. An attribute (e.g. ethnicity) may have several values (e.g. Malay, Chinese, Indian, etc.). Any particular node, case or document may be assigned one value for each attribute. Similarities within or differences between groups can be identified using attributes. Attribute Explorer displays a table of all attributes assigned to a document, node or set.

CAQDAS : Computer Aided Qualitative Data Analysis. The CAQDAS programme assists data management and supports coding processes. The software does not really analyse data, but rather supports the qualitative analysis process. NVivo is one of the CAQDAS programmes; others include NUDIST, ATLAS-ti, AQUAD, ETHNOGRAPH and MAXQDA.

Code : A term that represents an idea, theme, theory, dimension, characteristic, etc., of the data.

Coder : A tool used to code a passage of text in a document under a particular node. The coder can be accessed from the Document or Node Browser .

Coding : The action of identifying a passage of text in a document that exemplifies ideas or concepts and connecting it to a node that represents that idea or concept. Multiple codes can be assigned to the same segment of text in a document.

Coding stripes : Coloured vertical lines displayed at the right-hand pane of a Document ; each is named with title of the node at which the text is coded.

DataLinks : A tool for linking the information in a document or node to the information outside the project, or between project documents. DocLinks , NodeLinks and DataBite Links are all forms of DataLink .

Document : A document in an NVivo project is an editable rich text or plain text file. It may be a transcription of project data or it may be a summary of such data or memos, notes or passages written by the researcher. The text in a document can be coded, may be given values of document attributes and may be linked (via DataLinks ) to other related documents, annotations, or external computer files. The Document Explorer shows the list of all project documents.

Memo : A document containing the researcher”s commentary flagged (linked) on any text in a Document or Node. Any files (text, audio or video, or picture data) can be linked via MemoLink .

Model : NVivo models are made up of symbols, usually representing items in the project, which are joined by lines or arrows, designed to represent the relationship between key elements in a field of study. Models are constructed in the Modeller .

Node : Relevant passages in the project”s documents are coded at nodes. A Node represents a code, theme, or idea about the data in a project. Nodes can be kept as Free Nodes (without organisation) or may be organised hierarchically in Trees (of categories and subcategories). Free nodes are free-standing and are not associated to themes or concepts. Early on in the project, tentative ideas may be stored in the Free Nodes area. Free nodes can be kept in a simple list and can be moved to a logical place in the Tree Node when higher levels of categories are discovered. Nodes can be given values of attributes according to the features of what they represent, and can be grouped in sets. Nodes can be organised (created, edited) in Node Explorer (a window listing all the project nodes and node sets). The Node Browser displays the node”s coding and allow the researcher to change the coding.

Project : Collection of all the files, documents, codes, nodes, attributes, etc. associated with a research project. The Project pad is a window in NVivo when a project is open which gives access to all the main functions of the programme.

Sets : Sets in NVivo hold shortcuts to any nodes or documents, as a way of holding those items together without actually combining them. Sets are used primarily as a way of indicating items that in some way are related conceptually or theoretically. It provides different ways of sorting and managing data.

Tree Node : Nodes organised hierarchically into trees to catalogue categories and subcategories.