what is purpose of action research

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1 What is Action Research for Classroom Teachers?

ESSENTIAL QUESTIONS

• What is the nature of action research?
• How does action research develop in the classroom?
• What models of action research work best for your classroom?
• What are the epistemological, ontological, theoretical underpinnings of action research?

Educational research provides a vast landscape of knowledge on topics related to teaching and learning, curriculum and assessment, students’ cognitive and affective needs, cultural and socio-economic factors of schools, and many other factors considered viable to improving schools. Educational stakeholders rely on research to make informed decisions that ultimately affect the quality of schooling for their students. Accordingly, the purpose of educational research is to engage in disciplined inquiry to generate knowledge on topics significant to the students, teachers, administrators, schools, and other educational stakeholders. Just as the topics of educational research vary, so do the approaches to conducting educational research in the classroom. Your approach to research will be shaped by your context, your professional identity, and paradigm (set of beliefs and assumptions that guide your inquiry). These will all be key factors in how you generate knowledge related to your work as an educator.

Action research is an approach to educational research that is commonly used by educational practitioners and professionals to examine, and ultimately improve, their pedagogy and practice. In this way, action research represents an extension of the reflection and critical self-reflection that an educator employs on a daily basis in their classroom. When students are actively engaged in learning, the classroom can be dynamic and uncertain, demanding the constant attention of the educator. Considering these demands, educators are often only able to engage in reflection that is fleeting, and for the purpose of accommodation, modification, or formative assessment. Action research offers one path to more deliberate, substantial, and critical reflection that can be documented and analyzed to improve an educator’s practice.

Purpose of Action Research

As one of many approaches to educational research, it is important to distinguish the potential purposes of action research in the classroom. This book focuses on action research as a method to enable and support educators in pursuing effective pedagogical practices by transforming the quality of teaching decisions and actions, to subsequently enhance student engagement and learning. Being mindful of this purpose, the following aspects of action research are important to consider as you contemplate and engage with action research methodology in your classroom:

• Action research is a process for improving educational practice. Its methods involve action, evaluation, and reflection. It is a process to gather evidence to implement change in practices.
• Action research is participative and collaborative. It is undertaken by individuals with a common purpose.
• Action research is situation and context-based.
• Action research develops reflection practices based on the interpretations made by participants.
• Knowledge is created through action and application.
• Action research can be based in problem-solving, if the solution to the problem results in the improvement of practice.
• Action research is iterative; plans are created, implemented, revised, then implemented, lending itself to an ongoing process of reflection and revision.
• In action research, findings emerge as action develops and takes place; however, they are not conclusive or absolute, but ongoing (Koshy, 2010, pgs. 1-2).

In thinking about the purpose of action research, it is helpful to situate action research as a distinct paradigm of educational research. I like to think about action research as part of the larger concept of living knowledge. Living knowledge has been characterized as “a quest for life, to understand life and to create… knowledge which is valid for the people with whom I work and for myself” (Swantz, in Reason & Bradbury, 2001, pg. 1). Why should educators care about living knowledge as part of educational research? As mentioned above, action research is meant “to produce practical knowledge that is useful to people in the everyday conduct of their lives and to see that action research is about working towards practical outcomes” (Koshy, 2010, pg. 2). However, it is also about:

creating new forms of understanding, since action without reflection and understanding is blind, just as theory without action is meaningless. The participatory nature of action research makes it only possible with, for and by persons and communities, ideally involving all stakeholders both in the questioning and sense making that informs the research, and in the action, which is its focus. (Reason & Bradbury, 2001, pg. 2)

In an effort to further situate action research as living knowledge, Jean McNiff reminds us that “there is no such ‘thing’ as ‘action research’” (2013, pg. 24). In other words, action research is not static or finished, it defines itself as it proceeds. McNiff’s reminder characterizes action research as action-oriented, and a process that individuals go through to make their learning public to explain how it informs their practice. Action research does not derive its meaning from an abstract idea, or a self-contained discovery – action research’s meaning stems from the way educators negotiate the problems and successes of living and working in the classroom, school, and community.

While we can debate the idea of action research, there are people who are action researchers, and they use the idea of action research to develop principles and theories to guide their practice. Action research, then, refers to an organization of principles that guide action researchers as they act on shared beliefs, commitments, and expectations in their inquiry.

Reflection and the Process of Action Research

When an individual engages in reflection on their actions or experiences, it is typically for the purpose of better understanding those experiences, or the consequences of those actions to improve related action and experiences in the future. Reflection in this way develops knowledge around these actions and experiences to help us better regulate those actions in the future. The reflective process generates new knowledge regularly for classroom teachers and informs their classroom actions.

Unfortunately, the knowledge generated by educators through the reflective process is not always prioritized among the other sources of knowledge educators are expected to utilize in the classroom. Educators are expected to draw upon formal types of knowledge, such as textbooks, content standards, teaching standards, district curriculum and behavioral programs, etc., to gain new knowledge and make decisions in the classroom. While these forms of knowledge are important, the reflective knowledge that educators generate through their pedagogy is the amalgamation of these types of knowledge enacted in the classroom. Therefore, reflective knowledge is uniquely developed based on the action and implementation of an educator’s pedagogy in the classroom. Action research offers a way to formalize the knowledge generated by educators so that it can be utilized and disseminated throughout the teaching profession.

Research is concerned with the generation of knowledge, and typically creating knowledge related to a concept, idea, phenomenon, or topic. Action research generates knowledge around inquiry in practical educational contexts. Action research allows educators to learn through their actions with the purpose of developing personally or professionally. Due to its participatory nature, the process of action research is also distinct in educational research. There are many models for how the action research process takes shape. I will share a few of those here. Each model utilizes the following processes to some extent:

• Plan a change;
• Take action to enact the change;
• Observe the process and consequences of the change;
• Reflect on the process and consequences;
• Act, observe, & reflect again and so on.

Figure 1.1 Basic action research cycle

There are many other models that supplement the basic process of action research with other aspects of the research process to consider. For example, figure 1.2 illustrates a spiral model of action research proposed by Kemmis and McTaggart (2004). The spiral model emphasizes the cyclical process that moves beyond the initial plan for change. The spiral model also emphasizes revisiting the initial plan and revising based on the initial cycle of research:

Figure 1.2 Interpretation of action research spiral, Kemmis and McTaggart (2004, p. 595)

Other models of action research reorganize the process to emphasize the distinct ways knowledge takes shape in the reflection process. O’Leary’s (2004, p. 141) model, for example, recognizes that the research may take shape in the classroom as knowledge emerges from the teacher’s observations. O’Leary highlights the need for action research to be focused on situational understanding and implementation of action, initiated organically from real-time issues:

Figure 1.3 Interpretation of O’Leary’s cycles of research, O’Leary (2000, p. 141)

Lastly, Macintyre’s (2000, p. 1) model, offers a different characterization of the action research process. Macintyre emphasizes a messier process of research with the initial reflections and conclusions as the benchmarks for guiding the research process. Macintyre emphasizes the flexibility in planning, acting, and observing stages to allow the process to be naturalistic. Our interpretation of Macintyre process is below:

Figure 1.4 Interpretation of the action research cycle, Macintyre (2000, p. 1)

We believe it is important to prioritize the flexibility of the process, and encourage you to only use these models as basic guides for your process. Your process may look similar, or you may diverge from these models as you better understand your students, context, and data.

Definitions of Action Research and Examples

At this point, it may be helpful for readers to have a working definition of action research and some examples to illustrate the methodology in the classroom. Bassey (1998, p. 93) offers a very practical definition and describes “action research as an inquiry which is carried out in order to understand, to evaluate and then to change, in order to improve educational practice.” Cohen and Manion (1994, p. 192) situate action research differently, and describe action research as emergent, writing:

essentially an on-the-spot procedure designed to deal with a concrete problem located in an immediate situation. This means that ideally, the step-by-step process is constantly monitored over varying periods of time and by a variety of mechanisms (questionnaires, diaries, interviews and case studies, for example) so that the ensuing feedback may be translated into modifications, adjustment, directional changes, redefinitions, as necessary, so as to bring about lasting benefit to the ongoing process itself rather than to some future occasion.

Lastly, Koshy (2010, p. 9) describes action research as:

a constructive inquiry, during which the researcher constructs his or her knowledge of specific issues through planning, acting, evaluating, refining and learning from the experience. It is a continuous learning process in which the researcher learns and also shares the newly generated knowledge with those who may benefit from it.

These definitions highlight the distinct features of action research and emphasize the purposeful intent of action researchers to improve, refine, reform, and problem-solve issues in their educational context. To better understand the distinctness of action research, these are some examples of action research topics:

Examples of Action Research Topics

• Flexible seating in 4th grade classroom to increase effective collaborative learning.
• Structured homework protocols for increasing student achievement.
• Developing a system of formative feedback for 8th grade writing.
• Using music to stimulate creative writing.
• Weekly brown bag lunch sessions to improve responses to PD from staff.
• Using exercise balls as chairs for better classroom management.

Action Research in Theory

Action research-based inquiry in educational contexts and classrooms involves distinct participants – students, teachers, and other educational stakeholders within the system. All of these participants are engaged in activities to benefit the students, and subsequently society as a whole. Action research contributes to these activities and potentially enhances the participants’ roles in the education system. Participants’ roles are enhanced based on two underlying principles:

• communities, schools, and classrooms are sites of socially mediated actions, and action research provides a greater understanding of self and new knowledge of how to negotiate these socially mediated environments;
• communities, schools, and classrooms are part of social systems in which humans interact with many cultural tools, and action research provides a basis to construct and analyze these interactions.

In our quest for knowledge and understanding, we have consistently analyzed human experience over time and have distinguished between types of reality. Humans have constantly sought “facts” and “truth” about reality that can be empirically demonstrated or observed.

Social systems are based on beliefs, and generally, beliefs about what will benefit the greatest amount of people in that society. Beliefs, and more specifically the rationale or support for beliefs, are not always easy to demonstrate or observe as part of our reality. Take the example of an English Language Arts teacher who prioritizes argumentative writing in her class. She believes that argumentative writing demonstrates the mechanics of writing best among types of writing, while also providing students a skill they will need as citizens and professionals. While we can observe the students writing, and we can assess their ability to develop a written argument, it is difficult to observe the students’ understanding of argumentative writing and its purpose in their future. This relates to the teacher’s beliefs about argumentative writing; we cannot observe the real value of the teaching of argumentative writing. The teacher’s rationale and beliefs about teaching argumentative writing are bound to the social system and the skills their students will need to be active parts of that system. Therefore, our goal through action research is to demonstrate the best ways to teach argumentative writing to help all participants understand its value as part of a social system.

The knowledge that is conveyed in a classroom is bound to, and justified by, a social system. A postmodernist approach to understanding our world seeks knowledge within a social system, which is directly opposed to the empirical or positivist approach which demands evidence based on logic or science as rationale for beliefs. Action research does not rely on a positivist viewpoint to develop evidence and conclusions as part of the research process. Action research offers a postmodernist stance to epistemology (theory of knowledge) and supports developing questions and new inquiries during the research process. In this way action research is an emergent process that allows beliefs and decisions to be negotiated as reality and meaning are being constructed in the socially mediated space of the classroom.

Theorizing Action Research for the Classroom

All research, at its core, is for the purpose of generating new knowledge and contributing to the knowledge base of educational research. Action researchers in the classroom want to explore methods of improving their pedagogy and practice. The starting place of their inquiry stems from their pedagogy and practice, so by nature the knowledge created from their inquiry is often contextually specific to their classroom, school, or community. Therefore, we should examine the theoretical underpinnings of action research for the classroom. It is important to connect action research conceptually to experience; for example, Levin and Greenwood (2001, p. 105) make these connections:

• Action research is context bound and addresses real life problems.
• Action research is inquiry where participants and researchers cogenerate knowledge through collaborative communicative processes in which all participants’ contributions are taken seriously.
• The meanings constructed in the inquiry process lead to social action or these reflections and action lead to the construction of new meanings.
• The credibility/validity of action research knowledge is measured according to whether the actions that arise from it solve problems (workability) and increase participants’ control over their own situation.

Educators who engage in action research will generate new knowledge and beliefs based on their experiences in the classroom. Let us emphasize that these are all important to you and your work, as both an educator and researcher. It is these experiences, beliefs, and theories that are often discounted when more official forms of knowledge (e.g., textbooks, curriculum standards, districts standards) are prioritized. These beliefs and theories based on experiences should be valued and explored further, and this is one of the primary purposes of action research in the classroom. These beliefs and theories should be valued because they were meaningful aspects of knowledge constructed from teachers’ experiences. Developing meaning and knowledge in this way forms the basis of constructivist ideology, just as teachers often try to get their students to construct their own meanings and understandings when experiencing new ideas.  

Classroom Teachers Constructing their Own Knowledge

Most of you are probably at least minimally familiar with constructivism, or the process of constructing knowledge. However, what is constructivism precisely, for the purposes of action research? Many scholars have theorized constructivism and have identified two key attributes (Koshy, 2010; von Glasersfeld, 1987):

• Knowledge is not passively received, but actively developed through an individual’s cognition;
• Human cognition is adaptive and finds purpose in organizing the new experiences of the world, instead of settling for absolute or objective truth.

Considering these two attributes, constructivism is distinct from conventional knowledge formation because people can develop a theory of knowledge that orders and organizes the world based on their experiences, instead of an objective or neutral reality. When individuals construct knowledge, there are interactions between an individual and their environment where communication, negotiation and meaning-making are collectively developing knowledge. For most educators, constructivism may be a natural inclination of their pedagogy. Action researchers have a similar relationship to constructivism because they are actively engaged in a process of constructing knowledge. However, their constructions may be more formal and based on the data they collect in the research process. Action researchers also are engaged in the meaning making process, making interpretations from their data. These aspects of the action research process situate them in the constructivist ideology. Just like constructivist educators, action researchers’ constructions of knowledge will be affected by their individual and professional ideas and values, as well as the ecological context in which they work (Biesta & Tedder, 2006). The relations between constructivist inquiry and action research is important, as Lincoln (2001, p. 130) states:

much of the epistemological, ontological, and axiological belief systems are the same or similar, and methodologically, constructivists and action researchers work in similar ways, relying on qualitative methods in face-to-face work, while buttressing information, data and background with quantitative method work when necessary or useful.

While there are many links between action research and educators in the classroom, constructivism offers the most familiar and practical threads to bind the beliefs of educators and action researchers.  

Epistemology, Ontology, and Action Research

It is also important for educators to consider the philosophical stances related to action research to better situate it with their beliefs and reality. When researchers make decisions about the methodology they intend to use, they will consider their ontological and epistemological stances. It is vital that researchers clearly distinguish their philosophical stances and understand the implications of their stance in the research process, especially when collecting and analyzing their data. In what follows, we will discuss ontological and epistemological stances in relation to action research methodology.

Ontology, or the theory of being, is concerned with the claims or assumptions we make about ourselves within our social reality – what do we think exists, what does it look like, what entities are involved and how do these entities interact with each other (Blaikie, 2007). In relation to the discussion of constructivism, generally action researchers would consider their educational reality as socially constructed. Social construction of reality happens when individuals interact in a social system. Meaningful construction of concepts and representations of reality develop through an individual’s interpretations of others’ actions. These interpretations become agreed upon by members of a social system and become part of social fabric, reproduced as knowledge and beliefs to develop assumptions about reality. Researchers develop meaningful constructions based on their experiences and through communication. Educators as action researchers will be examining the socially constructed reality of schools. In the United States, many of our concepts, knowledge, and beliefs about schooling have been socially constructed over the last hundred years. For example, a group of teachers may look at why fewer female students enroll in upper-level science courses at their school. This question deals directly with the social construction of gender and specifically what careers females have been conditioned to pursue. We know this is a social construction in some school social systems because in other parts of the world, or even the United States, there are schools that have more females enrolled in upper level science courses than male students. Therefore, the educators conducting the research have to recognize the socially constructed reality of their school and consider this reality throughout the research process. Action researchers will use methods of data collection that support their ontological stance and clarify their theoretical stance throughout the research process.

Koshy (2010, p. 23-24) offers another example of addressing the ontological challenges in the classroom:

A teacher who was concerned with increasing her pupils’ motivation and enthusiasm for learning decided to introduce learning diaries which the children could take home. They were invited to record their reactions to the day’s lessons and what they had learnt. The teacher reported in her field diary that the learning diaries stimulated the children’s interest in her lessons, increased their capacity to learn, and generally improved their level of participation in lessons. The challenge for the teacher here is in the analysis and interpretation of the multiplicity of factors accompanying the use of diaries. The diaries were taken home so the entries may have been influenced by discussions with parents. Another possibility is that children felt the need to please their teacher. Another possible influence was that their increased motivation was as a result of the difference in style of teaching which included more discussions in the classroom based on the entries in the dairies.

Here you can see the challenge for the action researcher is working in a social context with multiple factors, values, and experiences that were outside of the teacher’s control. The teacher was only responsible for introducing the diaries as a new style of learning. The students’ engagement and interactions with this new style of learning were all based upon their socially constructed notions of learning inside and outside of the classroom. A researcher with a positivist ontological stance would not consider these factors, and instead might simply conclude that the dairies increased motivation and interest in the topic, as a result of introducing the diaries as a learning strategy.

Epistemology, or the theory of knowledge, signifies a philosophical view of what counts as knowledge – it justifies what is possible to be known and what criteria distinguishes knowledge from beliefs (Blaikie, 1993). Positivist researchers, for example, consider knowledge to be certain and discovered through scientific processes. Action researchers collect data that is more subjective and examine personal experience, insights, and beliefs.

Action researchers utilize interpretation as a means for knowledge creation. Action researchers have many epistemologies to choose from as means of situating the types of knowledge they will generate by interpreting the data from their research. For example, Koro-Ljungberg et al., (2009) identified several common epistemologies in their article that examined epistemological awareness in qualitative educational research, such as: objectivism, subjectivism, constructionism, contextualism, social epistemology, feminist epistemology, idealism, naturalized epistemology, externalism, relativism, skepticism, and pluralism. All of these epistemological stances have implications for the research process, especially data collection and analysis. Please see the table on pages 689-90, linked below for a sketch of these potential implications:

Again, Koshy (2010, p. 24) provides an excellent example to illustrate the epistemological challenges within action research:

A teacher of 11-year-old children decided to carry out an action research project which involved a change in style in teaching mathematics. Instead of giving children mathematical tasks displaying the subject as abstract principles, she made links with other subjects which she believed would encourage children to see mathematics as a discipline that could improve their understanding of the environment and historic events. At the conclusion of the project, the teacher reported that applicable mathematics generated greater enthusiasm and understanding of the subject.

The educator/researcher engaged in action research-based inquiry to improve an aspect of her pedagogy. She generated knowledge that indicated she had improved her students’ understanding of mathematics by integrating it with other subjects – specifically in the social and ecological context of her classroom, school, and community. She valued constructivism and students generating their own understanding of mathematics based on related topics in other subjects. Action researchers working in a social context do not generate certain knowledge, but knowledge that emerges and can be observed and researched again, building upon their knowledge each time.

Researcher Positionality in Action Research

In this first chapter, we have discussed a lot about the role of experiences in sparking the research process in the classroom. Your experiences as an educator will shape how you approach action research in your classroom. Your experiences as a person in general will also shape how you create knowledge from your research process. In particular, your experiences will shape how you make meaning from your findings. It is important to be clear about your experiences when developing your methodology too. This is referred to as researcher positionality. Maher and Tetreault (1993, p. 118) define positionality as:

Gender, race, class, and other aspects of our identities are markers of relational positions rather than essential qualities. Knowledge is valid when it includes an acknowledgment of the knower’s specific position in any context, because changing contextual and relational factors are crucial for defining identities and our knowledge in any given situation.

By presenting your positionality in the research process, you are signifying the type of socially constructed, and other types of, knowledge you will be using to make sense of the data. As Maher and Tetreault explain, this increases the trustworthiness of your conclusions about the data. This would not be possible with a positivist ontology. We will discuss positionality more in chapter 6, but we wanted to connect it to the overall theoretical underpinnings of action research.

Advantages of Engaging in Action Research in the Classroom

In the following chapters, we will discuss how action research takes shape in your classroom, and we wanted to briefly summarize the key advantages to action research methodology over other types of research methodology. As Koshy (2010, p. 25) notes, action research provides useful methodology for school and classroom research because:

Advantages of Action Research for the Classroom

• research can be set within a specific context or situation;
• researchers can be participants – they don’t have to be distant and detached from the situation;
• it involves continuous evaluation and modifications can be made easily as the project progresses;
• there are opportunities for theory to emerge from the research rather than always follow a previously formulated theory;
• the study can lead to open-ended outcomes;
• through action research, a researcher can bring a story to life.

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 and how do we do it?

In this article, we explore the development of some different traditions of action research and provide an introductory guide to the literature., contents : what is action research ·  origins · the decline and rediscovery of action research · undertaking action research · conclusion · further reading · how to cite this article . see, also: research for practice ..

In the literature, discussion of action research tends to fall into two distinctive camps. The British tradition – especially that linked to education – tends to view action research as research-oriented toward the enhancement of direct practice. For example, Carr and Kemmis provide a classic definition:

Action research is simply a form of self-reflective enquiry undertaken by participants in social situations in order to improve the rationality and justice of their own practices, their understanding of these practices, and the situations in which the practices are carried out (Carr and Kemmis 1986: 162).

Many people are drawn to this understanding of action research because it is firmly located in the realm of the practitioner – it is tied to self-reflection. As a way of working it is very close to the notion of reflective practice coined by Donald Schön (1983).

The second tradition, perhaps more widely approached within the social welfare field – and most certainly the broader understanding in the USA is of action research as ‘the systematic collection of information that is designed to bring about social change’ (Bogdan and Biklen 1992: 223). Bogdan and Biklen continue by saying that its practitioners marshal evidence or data to expose unjust practices or environmental dangers and recommend actions for change. In many respects, for them, it is linked into traditions of citizen’s action and community organizing. The practitioner is actively involved in the cause for which the research is conducted. For others, it is such commitment is a necessary part of being a practitioner or member of a community of practice. Thus, various projects designed to enhance practice within youth work, for example, such as the detached work reported on by Goetschius and Tash (1967) could be talked of as action research.

Kurt Lewin is generally credited as the person who coined the term ‘action research’:

The research needed for social practice can best be characterized as research for social management or social engineering. It is a type of action-research, a comparative research on the conditions and effects of various forms of social action, and research leading to social action. Research that produces nothing but books will not suffice (Lewin 1946, reproduced in Lewin 1948: 202-3)

His approach involves a spiral of steps, ‘each of which is composed of a circle of planning, action and fact-finding about the result of the action’ ( ibid. : 206). The basic cycle involves the following:

This is how Lewin describes the initial cycle:

The first step then is to examine the idea carefully in the light of the means available. Frequently more fact-finding about the situation is required. If this first period of planning is successful, two items emerge: namely, “an overall plan” of how to reach the objective and secondly, a decision in regard to the first step of action. Usually this planning has also somewhat modified the original idea. ( ibid. : 205)

The next step is ‘composed of a circle of planning, executing, and reconnaissance or fact-finding for the purpose of evaluating the results of the second step, and preparing the rational basis for planning the third step, and for perhaps modifying again the overall plan’ ( ibid. : 206). What we can see here is an approach to research that is oriented to problem-solving in social and organizational settings, and that has a form that parallels Dewey’s conception of learning from experience.

The approach, as presented, does take a fairly sequential form – and it is open to a literal interpretation. Following it can lead to practice that is ‘correct’ rather than ‘good’ – as we will see. It can also be argued that the model itself places insufficient emphasis on analysis at key points. Elliott (1991: 70), for example, believed that the basic model allows those who use it to assume that the ‘general idea’ can be fixed in advance, ‘that “reconnaissance” is merely fact-finding, and that “implementation” is a fairly straightforward process’. As might be expected there was some questioning as to whether this was ‘real’ research. There were questions around action research’s partisan nature – the fact that it served particular causes.

The decline and rediscovery of action research

Action research did suffer a decline in favour during the 1960s because of its association with radical political activism (Stringer 2007: 9). There were, and are, questions concerning its rigour, and the training of those undertaking it. However, as Bogdan and Biklen (1992: 223) point out, research is a frame of mind – ‘a perspective that people take toward objects and activities’. Once we have satisfied ourselves that the collection of information is systematic and that any interpretations made have a proper regard for satisfying truth claims, then much of the critique aimed at action research disappears. In some of Lewin’s earlier work on action research (e.g. Lewin and Grabbe 1945), there was a tension between providing a rational basis for change through research, and the recognition that individuals are constrained in their ability to change by their cultural and social perceptions, and the systems of which they are a part. Having ‘correct knowledge’ does not of itself lead to change, attention also needs to be paid to the ‘matrix of cultural and psychic forces’ through which the subject is constituted (Winter 1987: 48).

Subsequently, action research has gained a significant foothold both within the realm of community-based, and participatory action research; and as a form of practice-oriented to the improvement of educative encounters (e.g. Carr and Kemmis 1986).

Exhibit 1: Stringer on community-based action research

A fundamental premise of community-based action research is that it commences with an interest in the problems of a group, a community, or an organization. Its purpose is to assist people in extending their understanding of their situation and thus resolving problems that confront them….

Community-based action research is always enacted through an explicit set of social values. In modern, democratic social contexts, it is seen as a process of inquiry that has the following characteristics:

• It is democratic , enabling the participation of all people.

• It is equitable , acknowledging people’s equality of worth.

• It is liberating , providing freedom from oppressive, debilitating conditions.

• It is life enhancing , enabling the expression of people’s full human potential.

(Stringer 1999: 9-10)

Undertaking action research

As Thomas (2017: 154) put it, the central aim is change, ‘and the emphasis is on problem-solving in whatever way is appropriate’. It can be seen as a conversation rather more than a technique (McNiff et. al. ). It is about people ‘thinking for themselves and making their own choices, asking themselves what they should do and accepting the consequences of their own actions’ (Thomas 2009: 113).

The action research process works through three basic phases:

Look -building a picture and gathering information. When evaluating we define and describe the problem to be investigated and the context in which it is set. We also describe what all the participants (educators, group members, managers etc.) have been doing.

Think – interpreting and explaining. When evaluating we analyse and interpret the situation. We reflect on what participants have been doing. We look at areas of success and any deficiencies, issues or problems.

Act – resolving issues and problems. In evaluation we judge the worth, effectiveness, appropriateness, and outcomes of those activities. We act to formulate solutions to any problems. (Stringer 1999: 18; 43-44;160)

The use of action research to deepen and develop classroom practice has grown into a strong tradition of practice (one of the first examples being the work of Stephen Corey in 1949). For some, there is an insistence that action research must be collaborative and entail groupwork.

Action research is a form of collective self-reflective enquiry undertaken by participants in social situations in order to improve the rationality and justice of their own social or educational practices, as well as their understanding of those practices and the situations in which the practices are carried out… The approach is only action research when it is collaborative, though it is important to realise that action research of the group is achieved through the critically examined action of individual group members. (Kemmis and McTaggart 1988: 5-6)

Just why it must be collective is open to some question and debate (Webb 1996), but there is an important point here concerning the commitments and orientations of those involved in action research.

One of the legacies Kurt Lewin left us is the ‘action research spiral’ – and with it there is the danger that action research becomes little more than a procedure. It is a mistake, according to McTaggart (1996: 248) to think that following the action research spiral constitutes ‘doing action research’. He continues, ‘Action research is not a ‘method’ or a ‘procedure’ for research but a series of commitments to observe and problematize through practice a series of principles for conducting social enquiry’. It is his argument that Lewin has been misunderstood or, rather, misused. When set in historical context, while Lewin does talk about action research as a method, he is stressing a contrast between this form of interpretative practice and more traditional empirical-analytic research. The notion of a spiral may be a useful teaching device – but it is all too easy to slip into using it as the template for practice (McTaggart 1996: 249).

Further reading

This select, annotated bibliography has been designed to give a flavour of the possibilities of action research and includes some useful guides to practice. As ever, if you have suggestions about areas or specific texts for inclusion, I’d like to hear from you.

Explorations of action research

Atweh, B., Kemmis, S. and Weeks, P. (eds.) (1998) Action Research in Practice: Partnership for Social Justice in Education, London: Routledge. Presents a collection of stories from action research projects in schools and a university. The book begins with theme chapters discussing action research, social justice and partnerships in research. The case study chapters cover topics such as: school environment – how to make a school a healthier place to be; parents – how to involve them more in decision-making; students as action researchers; gender – how to promote gender equity in schools; writing up action research projects.

Carr, W. and Kemmis, S. (1986) Becoming Critical. Education, knowledge and action research , Lewes: Falmer. Influential book that provides a good account of ‘action research’ in education. Chapters on teachers, researchers and curriculum; the natural scientific view of educational theory and practice; the interpretative view of educational theory and practice; theory and practice – redefining the problem; a critical approach to theory and practice; towards a critical educational science; action research as critical education science; educational research, educational reform and the role of the profession.

Carson, T. R. and Sumara, D. J. (ed.) (1997) Action Research as a Living Practice , New York: Peter Lang. 140 pages. Book draws on a wide range of sources to develop an understanding of action research. Explores action research as a lived practice, ‘that asks the researcher to not only investigate the subject at hand but, as well, to provide some account of the way in which the investigation both shapes and is shaped by the investigator.

Dadds, M. (1995) Passionate Enquiry and School Development. A story about action research , London: Falmer. 192 + ix pages. Examines three action research studies undertaken by a teacher and how they related to work in school – how she did the research, the problems she experienced, her feelings, the impact on her feelings and ideas, and some of the outcomes. In his introduction, John Elliot comments that the book is ‘the most readable, thoughtful, and detailed study of the potential of action-research in professional education that I have read’.

Ghaye, T. and Wakefield, P. (eds.) CARN Critical Conversations. Book one: the role of the self in action , Bournemouth: Hyde Publications. 146 + xiii pages. Collection of five pieces from the Classroom Action Research Network. Chapters on: dialectical forms; graduate medical education – research’s outer limits; democratic education; managing action research; writing up.

McNiff, J. (1993) Teaching as Learning: An Action Research Approach , London: Routledge. Argues that educational knowledge is created by individual teachers as they attempt to express their own values in their professional lives. Sets out familiar action research model: identifying a problem, devising, implementing and evaluating a solution and modifying practice. Includes advice on how working in this way can aid the professional development of action researcher and practitioner.

Quigley, B. A. and Kuhne, G. W. (eds.) (1997) Creating Practical Knowledge Through Action Research, San Fransisco: Jossey Bass. Guide to action research that outlines the action research process, provides a project planner, and presents examples to show how action research can yield improvements in six different settings, including a hospital, a university and a literacy education program.

Plummer, G. and Edwards, G. (eds.) CARN Critical Conversations. Book two: dimensions of action research – people, practice and power , Bournemouth: Hyde Publications. 142 + xvii pages. Collection of five pieces from the Classroom Action Research Network. Chapters on: exchanging letters and collaborative research; diary writing; personal and professional learning – on teaching and self-knowledge; anti-racist approaches; psychodynamic group theory in action research.

Whyte, W. F. (ed.) (1991) Participatory Action Research , Newbury Park: Sage. 247 pages. Chapters explore the development of participatory action research and its relation with action science and examine its usages in various agricultural and industrial settings

Zuber-Skerritt, O. (ed.) (1996) New Directions in Action Research , London; Falmer Press. 266 + xii pages. A useful collection that explores principles and procedures for critical action research; problems and suggested solutions; and postmodernism and critical action research.

Action research guides

Coghlan, D. and Brannick, D. (2000) Doing Action Research in your own Organization, London: Sage. 128 pages. Popular introduction. Part one covers the basics of action research including the action research cycle, the role of the ‘insider’ action researcher and the complexities of undertaking action research within your own organisation. Part two looks at the implementation of the action research project (including managing internal politics and the ethics and politics of action research). New edition due late 2004.

Elliot, J. (1991) Action Research for Educational Change , Buckingham: Open University Press. 163 + x pages Collection of various articles written by Elliot in which he develops his own particular interpretation of action research as a form of teacher professional development. In some ways close to a form of ‘reflective practice’. Chapter 6, ‘A practical guide to action research’ – builds a staged model on Lewin’s work and on developments by writers such as Kemmis.

Johnson, A. P. (2007) A short guide to action research 3e. Allyn and Bacon. Popular step by step guide for master’s work.

Macintyre, C. (2002) The Art of the Action Research in the Classroom , London: David Fulton. 138 pages. Includes sections on action research, the role of literature, formulating a research question, gathering data, analysing data and writing a dissertation. Useful and readable guide for students.

McNiff, J., Whitehead, J., Lomax, P. (2003) You and Your Action Research Project , London: Routledge. Practical guidance on doing an action research project.Takes the practitioner-researcher through the various stages of a project. Each section of the book is supported by case studies

Stringer, E. T. (2007) Action Research: A handbook for practitioners 3e , Newbury Park, ca.: Sage. 304 pages. Sets community-based action research in context and develops a model. Chapters on information gathering, interpretation, resolving issues; legitimacy etc. See, also Stringer’s (2003) Action Research in Education , Prentice-Hall.

Winter, R. (1989) Learning From Experience. Principles and practice in action research , Lewes: Falmer Press. 200 + 10 pages. Introduces the idea of action research; the basic process; theoretical issues; and provides six principles for the conduct of action research. Includes examples of action research. Further chapters on from principles to practice; the learner’s experience; and research topics and personal interests.

Action research in informal education

Usher, R., Bryant, I. and Johnston, R. (1997) Adult Education and the Postmodern Challenge. Learning beyond the limits , London: Routledge. 248 + xvi pages. Has some interesting chapters that relate to action research: on reflective practice; changing paradigms and traditions of research; new approaches to research; writing and learning about research.

Other references

Bogdan, R. and Biklen, S. K. (1992) Qualitative Research For Education , Boston: Allyn and Bacon.

Goetschius, G. and Tash, J. (1967) Working with the Unattached , London: Routledge and Kegan Paul.

McTaggart, R. (1996) ‘Issues for participatory action researchers’ in O. Zuber-Skerritt (ed.) New Directions in Action Research , London: Falmer Press.

McNiff, J., Lomax, P. and Whitehead, J. (2003) You and Your Action Research Project 2e. London: Routledge.

Thomas, G. (2017). How to do your Research Project. A guide for students in education and applied social sciences . 3e. London: Sage.

Acknowledgements : spiral by Michèle C. | flickr ccbyncnd2 licence

How to cite this article : Smith, M. K. (1996; 2001, 2007, 2017) What is action research and how do we do it?’, The encyclopedia of pedagogy and informal education. [ https://infed.org/mobi/action-research/ . Retrieved: insert date] .

© Mark K. Smith 1996; 2001, 2007, 2017

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• What Is Action Research? | Definition & Examples

What Is Action Research? | Definition & Examples

Published on 27 January 2023 by Tegan George . Revised on 21 April 2023.

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, frequently asked questions about action research.

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

• Participatory action research emphasises 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 visualised 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 contextualise systemic processes that are already ongoing (e.g., working retroactively to analyse 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 utilised, 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 standardised 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 mould 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 generalisability  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 .

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 for 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. (2023, April 21). What Is Action Research? | Definition & Examples. Scribbr. Retrieved 9 September 2024, from https://www.scribbr.co.uk/research-methods/action-research-cycle/

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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Article contents

Action research.

• Eileen S. Johnson Eileen S. Johnson Oakland University
• https://doi.org/10.1093/acrefore/9780190264093.013.696
• Published online: 29 May 2020

Action research has become a common practice among educational administrators. The term “action research” was first coined by Kurt Lewin in the 1930s, although teachers and school administrators have long engaged in the process described by and formally named by Lewin. Alternatively known as practitioner research, self-study, action science, site-based inquiry, emancipatory praxis, etc., action research is essentially a collaborative, democratic, and participatory approach to systematic inquiry into a problem of practice within a local context. Action research has become prevalent in many fields and disciplines, including education, health sciences, nursing, social work, and anthropology. This prevalence can be understood in the way action research lends itself to action-based inquiry, participation, collaboration, and the development of solutions to problems of everyday practice in local contexts. In particular, action research has become commonplace in educational administration preparation programs due to its alignment and natural fit with the nature of education and the decision making and action planning necessary within local school contexts. Although there is not one prescribed way to engage in action research, and there are multiple approaches to action research, it generally follows a systematic and cyclical pattern of reflection, planning, action, observation, and data collection, evaluation that then repeats in an iterative and ongoing manner. The goal of action research is not to add to a general body of knowledge but, rather, to inform local practice, engage in professional learning, build a community practice, solve a problem or understand a process or phenomenon within a particular context, or empower participants to generate self-knowledge.

• action research cycle
• educational practice
• historical trends
• philosophical assumptions
• variations of action research

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• Tes Explains

What is action research?

Action research is a practice-based research method, designed to bring about change in a context. In the context of schools, it might be a teacher-led investigation, usually conducted within the teacher’s own school or classroom.  

When a problem or question is identified, an initiative is devised to address it. Following implementation, the impact is then assessed and reflected upon. This reflection may lead to amendments to the initiative, before repeating the process again.

Where can I see this in action?

In 2021, teacher and apprenticeship manager Dave Shurmer conducted research that looked at the impact that increased physical activity has on pupils’ academic attainment in literacy and numeracy. 

He asked pupils to skip for two minutes before English or maths lessons. At the end of the term, pupils sat a Sats practice paper. Shurmer compared the data from before and after the term for a conditioned and unconditioned group, and found that when the pupils skipped before lessons once a week, they went on to gain an extra mark on their test papers. When they skipped twice a week, they gained an extra two marks. 

In 2022, English teacher Katie Packman wrote about her action research project for Tes : after struggling to enthuse key stage 3 students in creative writing, she decided to introduce a workshopping approach. For one hour a week, students were given free rein to choose a creative writing project to develop. She held one-on-one tutorials to give feedback, and students had to plan and then draft their piece, and continue to work on it or start planning their next piece, until their next tutorial. In the end-of-year assessment results, 27 out of 28 students had made expected progress, and 65 per cent of those had made higher-than-expected progress.

Further reading:

• How to lead whole-class guided reading in schools
• Action research in the classroom: a quick guide
• Active learning: make a drama out of teaching punctuation
• “Front loading” and how it can support learning
• Why we use action research as CPD

The Education Endowment Foundation (EEF) is an independent charity dedicated to breaking the link between family income and educational achievement.

To achieve this, it summarises the best available evidence for teachers; its Teaching and Learning Toolkit, for example, is used by 70 per cent of secondary schools.

The charity also generates new evidence of “what works” to improve teaching and learning, by funding independent evaluations of high-potential projects, and supports teachers and senior leaders to use the evidence to achieve the maximum possible benefit for young people.

The purpose of action research

The purpose of action research is to gather information in an attempt to solve a problem or make an improvement, which may have been highlighted as a result of self-evaluation or student feedback. While this is the core purpose, the motivations may be different, the motivation may be sincere and the goal to be improvement of student education, behaviour or morale, alternatively the motivation could be disingenuous, including aspects such as increasing chances of promotion, making teaching easier or promoting one’s own agenda.

The aim of action research is to be able to answer a question/hypothesis, such as will allowing mobile phone use within lessons increase the levels of referencing within assignments? Experienced teachers will be able to provide large quantities of anecdotal evidence, including aspects such as levels of engagement decreases and behaviour worsens, however, without performing an action research project, the question remains unanswered in any reliable form.

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• Introduction

Participatory

Many ways of knowing, worthwhile purposes, emergent form, first-, second-, and third-person research, methodological practices.

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• Table Of Contents

action research , an overall approach to knowledge and inquiry, concerned with forging a direct link between intellectual knowledge and moment-to-moment personal and social action. Action research seeks to contribute directly to the flourishing of individuals, their communities , and the ecosystems of which they are part.

Action research has two faces: one is practical, concerned with providing processes of inquiry that are useful to people in the everyday conduct of their lives; the other is philosophical and political, part of a movement to ensure that what is taken as knowledge is philosophically sound, participatory, and pragmatic .

Action-research practices aim to open communicative spaces where people can come together in open dialogue to address issues of concern and to engage in cycles of action and reflection, so that ideas that are tentatively articulated in reflection can be examined systematically in phases of active experimentation. Action research can be described in more detail in terms of the following dimensions.

A primary purpose of action research is to produce practical knowledge that addresses issues of concern in personal and professional life. A wider purpose is to contribute through this to the increased well-being—economic, political, psychological, spiritual—of individuals and communities and to a more equitable and sustainable relationship with the wider ecology of the planet of which they are an intrinsic part.

Action research is a participative and democratic process that seeks to do research with, for, and by people; to redress the balance of power in knowledge creation; and to do this in an educative manner that increases participants’ capacity to engage in inquiring lives. At a methodological level, participation is important because one cannot study and improve practice without the deep involvement of those engaged in that practice—the necessary perspective and information are simply not available—and one can study persons only if one approaches them as persons, as intentional actors and meaning makers. But participation is also an ethical and political process: people have the right and ability to contribute to decisions that affect them and to knowledge that is about them, and action research has an important place in the empowerment of people.

Action research draws on a wide range of ways of knowing as one encounters and acts in the world. This “extended epistemology ” starts with everyday experience and is concerned with the development of living knowledge. It thus includes the experiential and the tacit; presentational forms drawing on story, theatre , graphic arts, and so forth; propositional knowing through theory and models; and practical knowing as expressed in skill and accomplishment.

The focus on practical purposes draws attention to the moral dimension of action research—that it is not a values-free process but an inquiry in the pursuit of worthwhile purposes, raising questions of values, morals , and ethics . Here there can be no absolutes; moral choice is always a matter of balance between competing goods. So in the practice of action research, one must continually ask what worthwhile purposes one is pursuing and whether they continue to be appropriate and relevant.

Good action research emerges over time in an evolutionary and developmental process, as individuals develop skills of inquiry, as communities of inquiry develop, as understanding of the issues deepens, and as practice grows, develops, shifts, and changes over time. Emergence means that the questions may change, the relationships may change, the purposes may change, what is important may change. This means action research cannot be programmatic and cannot be defined in terms of hard-and-fast methods but is in a sense a continually emerging work of art.

Action research has encompassed the individual, the small group, and wider organizational and social entities. At an individual level—first-person research—action research has addressed questions of personal and professional change, addressing questions such as “How can I improve my practice?” At the level of the face-to-face group, second-person action research has allowed people to come together to address issues of common concern. Current debate is focused on how action research can address issues at wider social and organizational levels, for example, through networks of inquiry and a variety of large-group processes and dialogue conferences as vehicles of inquiry.

These broad principles of inquiry are applied in practice with different emphases by the various schools and traditions. Included under the broad rubric of action research are variations including action science , action inquiry, appreciative inquiry, cooperative inquiry, participatory action research, and others.

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Action Research: What it is, Stages & Examples

The best way to get things accomplished is to do it yourself. This statement is utilized in corporations, community projects, and national governments. These organizations are relying on action research to cope with their continuously changing and unstable environments as they function in a more interdependent world.

In practical educational contexts, this involves using systematic inquiry and reflective practice to address real-world challenges, improve teaching and learning, enhance student engagement, and drive positive changes within the educational system.

This post outlines the definition of action research, its stages, and some examples.

Content Index

What is action research?

Stages of action research, the steps to conducting action research, examples of action research, advantages and disadvantages of action research.

Action research is a strategy that tries to find realistic solutions to organizations’ difficulties and issues. It is similar to applied research.

Action research refers basically learning by doing. First, a problem is identified, then some actions are taken to address it, then how well the efforts worked are measured, and if the results are not satisfactory, the steps are applied again.

It can be put into three different groups:

• Positivist: This type of research is also called “classical action research.” It considers research a social experiment. This research is used to test theories in the actual world.
• Interpretive: This kind of research is called “contemporary action research.” It thinks that business reality is socially made, and when doing this research, it focuses on the details of local and organizational factors.
• Critical: This action research cycle takes a critical reflection approach to corporate systems and tries to enhance them.

All research is about learning new things. Collaborative action research contributes knowledge based on investigations in particular and frequently useful circumstances. It starts with identifying a problem. After that, the research process is followed by the below stages:

Stage 1: Plan

For an action research project to go well, the researcher needs to plan it well. After coming up with an educational research topic or question after a research study, the first step is to develop an action plan to guide the research process. The research design aims to address the study’s question. The research strategy outlines what to undertake, when, and how.

Stage 2: Act

The next step is implementing the plan and gathering data. At this point, the researcher must select how to collect and organize research data . The researcher also needs to examine all tools and equipment before collecting data to ensure they are relevant, valid, and comprehensive.

Stage 3: Observe

Data observation is vital to any investigation. The action researcher needs to review the project’s goals and expectations before data observation. This is the final step before drawing conclusions and taking action.

Different kinds of graphs, charts, and networks can be used to represent the data. It assists in making judgments or progressing to the next stage of observing.

Stage 4: Reflect

This step involves applying a prospective solution and observing the results. It’s essential to see if the possible solution found through research can really solve the problem being studied.

The researcher must explore alternative ideas when the action research project’s solutions fail to solve the problem.

Action research is a systematic approach researchers, educators, and practitioners use to identify and address problems or challenges within a specific context. It involves a cyclical process of planning, implementing, reflecting, and adjusting actions based on the data collected. Here are the general steps involved in conducting an action research process:

Identify the action research question or problem

Clearly define the issue or problem you want to address through your research. It should be specific, actionable, and relevant to your working context.

Review existing knowledge

Conduct a literature review to understand what research has already been done on the topic. This will help you gain insights, identify gaps, and inform your research design.

Plan the research

Develop a research plan outlining your study’s objectives, methods, data collection tools, and timeline. Determine the scope of your research and the participants or stakeholders involved.

Collect data

Implement your research plan by collecting relevant data. This can involve various methods such as surveys, interviews, observations, document analysis, or focus groups. Ensure that your data collection methods align with your research objectives and allow you to gather the necessary information.

Analyze the data

Once you have collected the data, analyze it using appropriate qualitative or quantitative techniques. Look for patterns, themes, or trends in the data that can help you understand the problem better.

Reflect on the findings

Reflect on the analyzed data and interpret the results in the context of your research question. Consider the implications and possible solutions that emerge from the data analysis. This reflection phase is crucial for generating insights and understanding the underlying factors contributing to the problem.

Develop an action plan

Based on your analysis and reflection, develop an action plan that outlines the steps you will take to address the identified problem. The plan should be specific, measurable, achievable, relevant, and time-bound (SMART goals). Consider involving relevant stakeholders in planning to ensure their buy-in and support.

Implement the action plan

Put your action plan into practice by implementing the identified strategies or interventions. This may involve making changes to existing practices, introducing new approaches, or testing alternative solutions. Document the implementation process and any modifications made along the way.

Evaluate and monitor progress

Continuously monitor and evaluate the impact of your actions. Collect additional data, assess the effectiveness of the interventions, and measure progress towards your goals. This evaluation will help you determine if your actions have the desired effects and inform any necessary adjustments.

Reflect and iterate

Reflect on the outcomes of your actions and the evaluation results. Consider what worked well, what did not, and why. Use this information to refine your approach, make necessary adjustments, and plan for the next cycle of action research if needed.

Remember that participatory action research is an iterative process, and multiple cycles may be required to achieve significant improvements or solutions to the identified problem. Each cycle builds on the insights gained from the previous one, fostering continuous learning and improvement.

Explore Insightfully Contextual Inquiry in Qualitative Research

Here are two real-life examples of action research.

Action research initiatives are frequently situation-specific. Still, other researchers can adapt the techniques. The example is from a researcher’s (Franklin, 1994) report about a project encouraging nature tourism in the Caribbean.

In 1991, this was launched to study how nature tourism may be implemented on the four Windward Islands in the Caribbean: St. Lucia, Grenada, Dominica, and St. Vincent.

For environmental protection, a government-led action study determined that the consultation process needs to involve numerous stakeholders, including commercial enterprises.

First, two researchers undertook the study and held search conferences on each island. The search conferences resulted in suggestions and action plans for local community nature tourism sub-projects.

Several islands formed advisory groups and launched national awareness and community projects. Regional project meetings were held to discuss experiences, self-evaluations, and strategies. Creating a documentary about a local initiative helped build community. And the study was a success, leading to a number of changes in the area.

Lau and Hayward (1997) employed action research to analyze Internet-based collaborative work groups.

Over two years, the researchers facilitated three action research problem -solving cycles with 15 teachers, project personnel, and 25 health practitioners from diverse areas. The goal was to see how Internet-based communications might affect their virtual workgroup.

First, expectations were defined, technology was provided, and a bespoke workgroup system was developed. Participants suggested shorter, more dispersed training sessions with project-specific instructions.

The second phase saw the system’s complete deployment. The final cycle witnessed system stability and virtual group formation. The key lesson was that the learning curve was poorly misjudged, with frustrations only marginally met by phone-based technical help. According to the researchers, the absence of high-quality online material about community healthcare was harmful.

Role clarity, connection building, knowledge sharing, resource assistance, and experiential learning are vital for virtual group growth. More study is required on how group support systems might assist groups in engaging with their external environment and boost group members’ learning. 

Action research has both good and bad points.

• It is very flexible, so researchers can change their analyses to fit their needs and make individual changes.
• It offers a quick and easy way to solve problems that have been going on for a long time instead of complicated, long-term solutions based on complex facts.
• If It is done right, it can be very powerful because it can lead to social change and give people the tools to make that change in ways that are important to their communities.

Disadvantages

• These studies have a hard time being generalized and are hard to repeat because they are so flexible. Because the researcher has the power to draw conclusions, they are often not thought to be theoretically sound.
• Setting up an action study in an ethical way can be hard. People may feel like they have to take part or take part in a certain way.
• It is prone to research errors like selection bias , social desirability bias, and other cognitive biases.

LEARN ABOUT: Self-Selection Bias

This post discusses how action research generates knowledge, its steps, and real-life examples. It is very applicable to the field of research and has a high level of relevance. We can only state that the purpose of this research is to comprehend an issue and find a solution to it.

At QuestionPro, we give researchers tools for collecting data, like our survey software, and a library of insights for any long-term study. Go to the Insight Hub if you want to see a demo or learn more about it.

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Frequently Asked Questions(FAQ’s)

Action research is a systematic approach to inquiry that involves identifying a problem or challenge in a practical context, implementing interventions or changes, collecting and analyzing data, and using the findings to inform decision-making and drive positive change.

Action research can be conducted by various individuals or groups, including teachers, administrators, researchers, and educational practitioners. It is often carried out by those directly involved in the educational setting where the research takes place.

The steps of action research typically include identifying a problem, reviewing relevant literature, designing interventions or changes, collecting and analyzing data, reflecting on findings, and implementing improvements based on the results.

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Frequently asked questions

What is the main purpose of action research.

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.

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

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 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.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Action research.

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• Finding Action Research Studies
• Bibliography & Additional Resources

Education Liaison Librarian

What is Action Research?

Action research involves a systematic process of examining the evidence. The results of this type of research are practical, relevant, and can inform theory. Action research is different than other forms of research as there is less concern for universality of findings, and more value is placed on the relevance of the findings to the researcher and the local collaborators.

Riel, M. (2020). Understanding action research. Center For Collaborative Action Research, Pepperdine University.  Retrieved January 31, 2021 from the Center for Collaborative Action Research.  https://www.actionresearchtutorials.org/  

-----------------------------------

The short video below by John Spencer provides a quick overview of Action Research.

How is Action Research different?

This chart demonstrates the difference between traditional research and action research. Traditional research is a means to an end - the conclusion. They start with a theory, statistical analysis is critical and the researcher does not insert herself into the research.

Action research is often practiced by practitioners like teachers and librarians who remain in the middle of the research process. They are looking for ways to improve the specific situation for their clientele or students. Statistics may be collected but they are not the point of the research.

Adapted from: Mc Millan, J. H. & Wergin. J. F. (1998). Understanding and evaluating educational research. Prentice-Hall, Inc.

• Next: Finding Action Research Studies >>
• Last Updated: Aug 26, 2024 12:10 PM
• URL: https://guides.library.ucmo.edu/actionresearch

Created by the Great Schools Partnership , the GLOSSARY OF EDUCATION REFORM is a comprehensive online resource that describes widely used school-improvement terms, concepts, and strategies for journalists, parents, and community members. | Learn more »

Action Research

In schools, action research refers to a wide variety of evaluative, investigative, and analytical research methods designed to diagnose problems or weaknesses—whether organizational, academic, or instructional—and help educators develop practical solutions to address them quickly and efficiently. Action research may also be applied to programs or educational techniques that are not necessarily experiencing any problems, but that educators simply want to learn more about and improve. The general goal is to create a simple, practical, repeatable process of iterative learning, evaluation, and improvement that leads to increasingly better results for schools, teachers, or programs.

Action research may also be called a cycle of action or cycle of inquiry , since it typically follows a predefined process that is repeated over time. A simple illustrative example:

• Identify a problem to be studied
• Collect data on the problem
• Organize, analyze, and interpret the data
• Develop a plan to address the problem
• Implement the plan
• Evaluate the results of the actions taken
• Identify a new problem
• Repeat the process

Unlike more formal research studies, such as those conducted by universities and published in peer-reviewed scholarly journals, action research is typically conducted by the educators working in the district or school being studied—the participants—rather than by independent, impartial observers from outside organizations. Less formal, prescriptive, or theory-driven research methods are typically used when conducting action research, since the goal is to address practical problems in a specific school or classroom, rather than produce independently validated and reproducible findings that others, outside of the context being studied, can use to guide their future actions or inform the design of their academic programs. That said, while action research is typically focused on solving a specific problem (high rates of student absenteeism, for example) or answer a specific question (Why are so many of our ninth graders failing math?), action research can also make meaningful contributions to the larger body of knowledge and understanding in the field of education, particularly within a relatively closed system such as school, district, or network of connected organizations.

The term “action research” was coined in the 1940s by Kurt Lewin, a German-American social psychologist who is widely considered to be the founder of his field. The basic principles of action research that were described by Lewin are still in use to this day.

Educators typically conduct action research as an extension of a particular school-improvement plan, project, or goal—i.e., action research is nearly always a school-reform strategy. The object of action research could be almost anything related to educational performance or improvement, from the effectiveness of certain teaching strategies and lesson designs to the influence that family background has on student performance to the results achieved by a particular academic support strategy or learning program—to list just a small sampling.

For related discussions, see action plan , capacity , continuous improvement , evidence-based , and professional development .

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Exploring Action Research Sponsorship: Role and Enactment

• Open access
• Published: 14 September 2024

Cite this article

You have full access to this open access article

• Henrik Saabye 1 , 2 ,
• Paul Coughlan 3 &
• Thomas Borup Kristensen 1  

This paper explores the complexities of involving partner organisations in co-generative learning processes within Action Research (AR) initiatives. Despite formal agreements, action researchers often face challenges in collaboratively addressing specific organisational issues through AR cycles. When action researchers adopt the “friendly outsider’ role, their initial task is to secure commitment to an AR initiative from senior leaders as sponsors. However, the existing literature lacks comprehensive guidance on facilitating this commitment. Therefore, drawing from both literature and empirical cases, this paper examines the pivotal role of the AR sponsor in securing funding and political backing, offering constructive critique, and facilitating learning. It provides insights into how action researchers can facilitate sponsors to enact these roles effectively so as to ensure the success and sustainability of organisational changes resulting from AR initiatives.

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Introduction

According to Greenwood and Levin ( 2006 ), action researchers can assume the friendly outsider role when engaging in research projects with organisations. A significant challenge for the action researcher as the friendly outsider is ensuring that the participating organisational members, as insiders, grasp the foundational premises driving the prospective collaborative and transformational activity (Levin 2004 ). This challenge remains of contemporary relevance and is the focus of this paper.

Recently, the primary author engaged in a conversation with a doctoral student who was struggling with involving an AR partner organisation in a co-generative learning process (Elden and Levin 1991 ). Despite the organisation signing an agreement and formally accepting to sponsor and participate in the AR initiative, the doctoral student still faced difficulties eight months later engaging leaders and employees in identifying, confronting, and framing a concrete organisational problem for the prospective collaborative work. The conversation prompted some practical questions: were these difficulties to be expected? Was there a misunderstanding in the original agreement? Was there a clear agreement but poor subsequent implementation? These questions and thoughts have prompted this article.

While facilitating co-generative learning as a collaborative and transformational activity is inherently challenging, the doctoral student’s experience highlights a recognisable but less reflected aspect of the AR literature regarding the preparedness of non-academic host organisations for participation in what they agree to sponsor (Bäckstrand et al. 2024 ). For instance, Coughlan and Coghlan ( 2024 , 232) noted that the organisation may be clear in its own terms on why it needs to engage in the action and why now. However, there is less developed guidance on how researchers should facilitate organisations in understanding the relevance of the research dimension of the initiative in prospect and their role as sponsors of the research process.

For Bäckstrand et al. ( 2024 ), engaging with senior organisational leaders to cultivate a commitment to AR represents a particularly significant and pivotal task for action researchers. However, facilitating genuine, active, and visible commitment from senior leaders as sponsors is challenging across different contexts (Kloppenborg et al. 2014 ) when aiming to involve them actively in the co-generative learning process of AR initiatives (Myers 2009 ). This commitment is crucial in ensuring the success of an AR initiative and the associated organisational change since research suggests that how sponsors enact their role is a significant contributor to the failure or success of such projects (Bryde 2008 ; Dolphin 2003 ).

So, what does this task mean for action researchers? Morten Levin emphasised the importance of making action researchers’ skills and personal traits explicit, focusing on their development, especially among doctoral students (Greenwood and Levin 2006 ; Levin and Martin 2007 ). While researchers may acknowledge a role for sponsors in the AR initiative, they may not anticipate their own role in facilitating a learning process where they must engage with and support the organisation’s members. Further, such active engagement and support may require them to evolve and undergo professional and personal change as the action learning associated with the AR initiative evolves (Saabye et al. 2024 ).

This perspective links to and extends the works of Morten Levin. While Klev and Levin ( 2012 ) contend that leadership is crucial in guiding learning processes, contributing significantly to the development and sustainable accumulation of resources within an organisation, the existing literature does not inform an understanding of the sponsor’s role and researchers’ responsibilities linked to an AR initiative. Yet, what is required to fill this gap includes understanding the sponsor’s role within a host organisation and how the action researcher can assist the sponsor in enacting this role. So, the research question arises: What role definition facilitates sponsorship of action research? How can action researchers facilitate impactful sponsorship of AR initiatives ?

At the heart of this paper is a contention that ensuring informed, active and visible engagement from sponsors is crucial for the success of AR initiatives and ensuring the sustainability of the emergent changes. In this paper, we aim to explore the role of the sponsor in AR in enacting three key responsibilities: (1) securing funding and political backing, (2) providing constructive critique as a critical friend, and (3) facilitating learning during an AR initiative.

We first review existing literature to identify relevant theoretical foundations, including project sponsorship, critical friendship, and action learning facilitation. Next, we extrapolate insights from two research cases illustrating how sponsors enacted their roles and how the action researcher facilitated this process and ensured valuable contributions to theory and practice. Overall, the paper, building on our case-based insights, aims to contribute actionable knowledge on how to sponsor co-generative learning in AR, ensuring long-term impact and sustainability of changes.

Research Design

In tackling the challenge of understanding the intricacies of sponsorship and facilitating the effective implementation of related roles, this research design is informed by Hansen and Madsen ( 2019 ). They characterise theorising as “the process through which a theory is created, from the first feeble hunch to the final theory, presented in print to the reader” (p. vii). For them, theorising entails a dynamic interplay of various intellectual activities within a scholarly community, including talking, listening, reading, and writing. These engagement processes are fundamental in shaping and refining our ideas as we engage in dialogue to explore different perspectives, actively listen to the insights and viewpoints of others, absorb knowledge from diverse sources through reading, and articulate our evolving thoughts and theories through writing this paper.

More specifically, the theoretical foundations were conceived through ongoing discussion and reflections among the authors, building on our combined insights into challenges faced when facilitating change in various settings as both researchers and practitioners, including (1) comprehending the nature of impactful sponsoring roles within action research, (2) understanding how senior leaders enact these roles, and (3) determining how action researchers can facilitate the development of impactful AR sponsorship. These discussions prompted our location of the theoretical foundations in the extant literature.

Reflecting on our collective experience engaging in action research, we identified two illustrative cases that exemplify impactful sponsorship emerging from an AR initiative at a Building Material firm (Case 1) and an action learning research project at a Toy company (Case 2). These two cases inform our understanding of the roles that can characterise impactful sponsorship of AR initiatives and how action researchers can effectively facilitate their enactment by senior leaders.

Each illustrative case offers a particular perspective on sponsorship, collectively showcasing ways sponsors can actively contribute to the success of AR initiatives. The cases also build empirically on the theoretical foundation emerging from the paper’s literature review of what constitutes impactful sponsorship to shed light on the interactions between sponsors and researchers. We conclude with actionable insights and recommendations for facilitating impactful sponsorship.

Locating our Theoretical Foundation

The foundations for our work are diverse but related. We begin by examining AR as a co-generative process. We examine then sponsorship in AR initiatives, considering what is known about the formality of the role, where the sponsor may be a critical friend or a learning facilitator. We conclude with a reflection on the potential for the interplay between these roles to foster a co-generative learning environment while engaging in real-world problem solving.

Action Research as a Co-Generative Learning Process

AR is a strategic approach to knowledge production that integrates various methods to address practical issues, empowering participants and researchers while expanding beyond disciplinary boundaries (Levin and Martin 2007 ). Greenwood and Levin ( 2006 ) portray the action researcher as a “friendly outsider”. The researcher often initiates the developmental process, guiding participants with supportive rather than critical insights (Levin 2004 ). Effective facilitation involves opening dialogue and uncovering tacit knowledge to enhance local conduct (Greenwood and Levin 2006 ). Moreover, researchers aid individuals in recognising internal resources within the company (Greenwood and Levin 2006 ), building on Argyris and Schön’s ( 1978 ) concept of open feedback to facilitate actionable possibilities (Finnestrand 2023 ).

The co-generative learning model, pioneered by Elden and Levin ( 1991 ), underpins action research, emphasising mutual learning between researchers and problem owners within a democratic framework. This model encapsulates organisational development as a learning process, delineating process elements, their interconnections, and the learning process itself (Klev and Levin 2012 ).

Practically, the co-generative learning model starts with jointly identifying real-life problems through dialogue between insiders (local participants) and outsiders (consultants or researchers) (Levin 1993 ). This problem-driven approach motivates experimentation within the AR circle, facilitating collective learning and continual improvement (Levin 2004 ). Facilitators play a crucial role in directing learning opportunities, with the model featuring dual learning circles for both insiders and outsiders (Levin 2004 ).

By serving as a framework for organisational learning, the co-generative model facilitates mutual learning between company insiders and researchers, leveraging theory in action to develop shared frameworks and eventually contribute to broader general theories and actionable strategies.

Existing literature has identified essential skills necessary for conducting action research, establishing a consensus that acquiring these skills transcends classroom instruction (Greenwood and Levin 2006 ). However, mastery necessitates experiential learning and deliberate practice, complemented by critical reflection upon one’s thoughts and behaviours as an action researcher (Reason and Torbert 2001 ). Yet, despite these insights, the challenge of effectively educating action researchers remains underdeveloped, including the more specific process of facilitating senior leaders in enacting the role of genuine sponsors (Levin and Martin 2007 ; Levin and Ravn 2007 ).

Sponsorship in Action Research

The sponsor of AR initiatives can play a multifaceted role with three primary responsibilities. Firstly, the sponsor plays a pivotal role in securing funding and garnering political support, advocating for budget allocations and navigating organisational and external dynamics to ensure financial stability and backing (Englund and Bucero 2006 ). Secondly, in the capacity of a critical friend, the sponsor offers invaluable feedback and constructive criticism to the initiative team, guiding them in refining goals, strategies, and implementation plans (Mat Noor and Shafee 2020 ). This role facilitates an environment of open communication, collaboration, and continual improvement throughout the initiative’s lifecycle. Lastly, as a learning facilitator, the sponsor promotes and facilitates a culture of knowledge-sharing, experimentation, and reflective practice within the initiative (Saabye 2023 ). By creating opportunities for learning and development, the sponsor empowers the initiative team to adapt to evolving circumstances, seize new opportunities, and drive innovation. Overall, the sponsor’s diverse involvement significantly contributes to the success and sustainability of the AR initiative and subsequent organisational change endeavours.

On reflection, the role of a sponsor evolves along a continuum, as illustrated in Fig.  1 , encompassing the three roles of setting direction as a formal project sponsor, ensuring progress as a critical friend, and facilitating learning through questioning and reflection. The task of the action researcher is to assist the sponsor in becoming consciously aware of these diverse roles, guiding the sponsor on how to enact them effectively and when to do so. We explore each role in turn.

AR Sponsor role continuum

Formal Sponsorship Role

In project management, a sponsor’s role is widely recognised as pivotal for success (West 2017 ). A sponsor is a guiding force, providing strategic direction, support, and advocacy throughout the project or change initiative (Englund and Bucero 2006 ). According to Kloppenborg et al. ( 2014 ), three sponsor behaviours significantly contribute to project success: (1) Defining success criteria by setting expectations, empowering project managers, communicating strategic value, and establishing success metrics; (2) Mentoring project managers by giving them a broader understanding, developing their interpersonal skills, and monitoring progress to increase value and contribute to the organisation’s future success; (3) Prioritising by aligning project objectives with stakeholder expectations and ensuring understanding and agreement on expected benefits among stakeholders, thus facilitating decision-making consistent with desired future outcomes.

An integral aspect of a sponsor’s project role is effectively managing organisational politics. Sponsors play a pivotal role in navigating interdepartmental dynamics and bolstering the project’s credibility within the organisation (Coghlan 2019 ; Holgersson & Melin 2015 ). This role entails mediating conflicts, aligning various stakeholders’ interests, championing the project’s objectives, and ensuring its visibility and support across different departments and hierarchies. By adeptly handling organisational politics, sponsors can mitigate resistance, facilitate collaboration, and facilitate smoother project execution, ultimately enhancing the project’s chances of success and its positive impact on the organisation.

In the context of AR, the role of the formal sponsor is essential, acting as a vital foundation for the initiative. The formal sponsor is not necessarily a titular appointee but with involvement can be a fundamental enabler for effective learning and collaboration. By securing resources, providing strategic direction, and navigating organisational politics, the sponsor creates the necessary conditions for co-generative learning. Therefore, this role can be conceptualised as a “hygiene factor,” signifying that while it may not directly motivate innovative outcomes, it establishes the foundation for successful collaboration and mutual knowledge creation (Herzberg 2003 ). Without the formal sponsor’s support, the potential for effective co-generative learning diminishes significantly.

Sponsors as a Critical Friend

The literature suggests that AR can be significantly enhanced through the involvement of critical friends, who play a multifaceted role in the initiative (Coghlan and Brydon-Miller 2014 ). Defined as trusted individuals selected for their knowledge, experience, and skills, critical friends serve as advocates for the initiative’s success (Campbell et al. 2004 ). They ask provocative questions, provide alternative perspectives on data, and offer constructive critiques of the work (Mat Noor and Shafee 2020 ; Costa and Kallick 1993 ). It is crucial to understand that critique from a critical friend is not intended to be negative but rather generative, aimed at facilitating more profound understanding and exploration (Coghlan and Brydon-Miller 2014 ). By assuming roles such as participant observers, peer reviewers, and facilitators, critical friends contribute to the initiative’s richness and depth (Costa and Kallick 1993 ). They seek to uncover deeper meanings, explore alternative explanations, and encourage the use of iterative protocols or processes. The roles of critical friends are diverse and context-dependent, ranging from facilitator and supporter to critic and challenger (Swaffield 2004 ). They can also act as external conversationalists, financiers, project consultants, rapport builders, and much more, playing a vital role in the success and evolution of AR initiatives (Foulger 2010 ; Kember et al. 1997 ).

The formal sponsor and the critical friend are both crucial for the success of AR initiatives. As noted earlier, the formal sponsor provides essential support and strategic direction, ensuring the allocation of resources, maintaining organisational alignment, and managing logistical challenges. This foundational backing is vital for initiating and sustaining the process. Meanwhile, the critical friend enhances the learning experience by offering constructive feedback, promoting reflective practice, challenging assumptions, and providing alternative perspectives. Moreover, the critical friend helps the action research participants to close the gap between their espoused theories and theories-in-use (Argyris and Schön 1978 ). They create an environment conducive to co-generative learning, facilitating effective problem-solving and continuous improvement. This collaborative approach addresses real-world problems, generates valuable academic insights, and contributes to broader research.

Sponsors as Learning Facilitators

AR and action learning are related (Coghlan and Coughlan 2008 ). While they share values and learning cycles, they differ in focus: action learning emphasises education and learning in action, while AR emphasises contributions to actionable knowledge. AR involves research methodologies that engage participants in addressing real-world issues through a collaborative and iterative process, contrasting with the more detached approach of positivist research methods (Coghlan and Coughlan 2008 ; Saabye et al. 2024 .). Both involve collaborative relationships, with action learning focusing on individual and group learning processes and AR emphasising inquiry and reflection, often within a group setting (Revans 1982 ; Reason and Torbert 2001 ).

Action learning centres on resolving complex organisational problems through a set of interactive components (Marquardt et al. 2018 , p. 28). These components encompass selecting and tackling a problem or opportunity, establishing a relevant group, commitment to action and learning, engagement in questioning and reflection, and the guidance of a learning coach or facilitator (similar to the ‘friendly outsider’ of action research). The guidance is essential for designing, initiating, and implementing action learning, fulfilling three roles: accoucheur or designer, set advisor or action-learning facilitator, and organisational learning facilitator (Pedler and Abbott 2013 ).

In the context of this paper, Revans’ ( 1982 ) theoretical foundations of action learning focus on developing middle and top managers’ capacity to learn and transform their own organisations. In this regard, Revans framed the principle of insufficient mandate: “Those unable to change themselves cannot change what goes on around them” (Revans 2011 ,76). Revans ( 2011 ) cautioned managers and leaders involved in AR to avoid several pitfalls. These include idolising past experiences, which can cloud interpretation and recollection; succumbing to the charismatic influence of other successful managers; prioritising immediate activity over thoughtful planning; and maintaining a hierarchical approach that keeps others confined to their roles. Revans ( 2011 ) also stated that top managers (as sponsors) must demonstrate unwavering confidence in all aspects of an action-learning intervention, actively define their roles, and participate in problem selection while being available to lower levels. The sponsors need to acknowledge their role as learners and are open to discussing unexpected turns in the mission. Additionally, they ensure all necessary conditions for initiative success, including support from other senior managers, are met. While they show interest and provide support, they refrain from making significant interventions, maintaining a balance between involvement and allowing participants to take the lead in advancing the initiative.

In other words, leaders as sponsors serve as learning facilitators (Saabye et al. 2022 ; Saabye 2023 ). Their role involves two key components. Firstly, they facilitate learning and reflection through questioning, recognising the inherent challenges of engaging in critical self-development, and openly acknowledging their mistakes and insights. Secondly, they embrace the idea that while leaders may not have all the answers, employees directly facing challenges, such as those on the shop floor, can possess valuable insights. This shift in mindset associated with these components allows leaders to adopt a curiosity-driven approach, making it easier to ask relevant questions and facilitate meaningful learning experiences (Saabye et al. 2024 ).

The roles of a critical friend and a learning facilitator are interlinked, each contributing to the co-generative learning process of AR. A critical friend provides constructive feedback and challenges assumptions, fostering deeper reflection and encouraging participants to examine their practices critically. Meanwhile, the learning facilitator role supports the learning process by structuring group activities, fostering an environment conducive to open dialogue, and ensuring that learning objectives are achieved. The learning facilitator role helps participants integrate the critical feedback the critical friend role provides into their reflective practices and learning activities.

For the formal sponsor to enact both the critical friend and the learning facilitator roles is what fosters a co-generative learning environment where constructive critique and structured guidance work synergistically. The formal sponsor establishes the setting for the intervention. The critical friend’s feedback drives reflective practice by encouraging participants to analyse and learn from their experiences. At the same time, the learning facilitator ensures that this reflection is systematically integrated into the co-generative learning process. This interplay enhances solving real-world problems and establishes a supportive framework for continuous learning, fostering a culture where reflective practices can thrive and contribute to meaningful, sustained change.

Illustrative Cases

In this section, we present two illustrative case studies, each set within a unique context, to demonstrate how sponsors of action research (AR) initiatives have enacted the three distinct roles associated with their opportunities and responsibilities.

Case 1: Building Material Firm

This AR initiative unfolded at a production site within a large international building materials firm that had embarked on introducing digital data-gathering systems across its production line. The aim was to provide operators with real-time data access, facilitating problem-solving and enhancing production performance. However, despite months of engagement, it became evident that the anticipated outcomes were not materialising. Neither did the production line witness improvement, nor did the operators actively engage in problem-solving endeavours. To address this emergent challenge, the first author, serving as an action researcher and industrial doctoral student, initiated a novel AR initiative in collaboration with a university. The primary objective of this initiative was to identify systemic obstacles hindering the successful adoption and utilisation of the new real-time digital data-gathering system. Subsequently, the focus shifted towards developing organisational capabilities to address these challenges effectively through an extensive action learning program (Saabye 2023 ; Saabye et al. 2022 ).

The AR initiative was deemed successful, effectively addressing a real problem within the host organisation and contributing to existing theory by publishing several academic articles. This success can largely be attributed to the exemplary executive sponsorship of the General Manager of the production site at the international building material firm. His proactive approach to fulfilling his role as formal sponsor played a crucial role in the project’s achievements. Initially, he was instrumental in defining the scope of the AR initiative, securing funding and political support, and laying the groundwork for its initiation.

As a formal sponsor, my primary responsibility was ensuring everyone understood the Action Research project was moving forward. At the same time, I focused on securing the necessary time and resources for our success and made it clear that I would actively work to remove any obstacles that might hinder our progress.

However, his impact on the project’s success was primarily due to his active involvement as a critical friend throughout its duration. He consistently participated in steering group meetings, learning sessions, and reflection sessions with participants and local managers. In these interactions, he demonstrated exceptional skill in asking thought-provoking questions, offering alternative perspectives on data, and providing constructive critiques.

When engaging with leaders and employees during the AR project, I concentrated on asking questions that challenged their thinking and reflections without causing too much frustration or anxiety. I also avoided questions with overly simple answers and remained mindful not to fall victim to my own assumptions or biases.

Notably, he understood that critique should be constructive and aimed at facilitating more profound understanding and exploration rather than being negative. As a learning facilitator, he played a pivotal role in creating a supportive learning environment. He encouraged learning and reflection by posing challenging questions and openly acknowledging mistakes and insights. By embodying the idea that leaders do not have all the answers and that frontline employees possess valuable insights, he empowered the senior leaders and first-line managers to adopt a curiosity-driven approach. This approach facilitated meaningful learning experiences and made it easier to address relevant questions and challenges effectively.

I focus on asking questions and guiding the process by applying situational leadership, finding the right balance, and establishing a strong framework. I also ensure trust, vulnerability, and psychological safety within the team by sharing personal examples of my own failures and the valuable lessons I’ve learned from those experiences.

Part of the success in fulfilling the sponsor role was also attributed to the General Manager’s innate curiosity and commitment to facilitating learning, personal development, and self-reflection, which provided a solid foundation. To ensure genuine and committed sponsorship, the action researcher initiated individual AR cycles with the executive sponsor and his senior leaders to comprehensively analyse what prevented the adoption and utilisation of the digital data-gathering system. This served a dual purpose: understanding the situation while also developing the sponsor’s and senior leader’s capabilities as a critical friend and learning facilitator. As part of this process, the sponsors engaged in self-evaluation regarding effective sponsorship behaviours supported by the first author.

This introspection revealed instances where senior leaders had often been passive or unsupportive throughout the lifespan of the digitalisation project, hindering their success. Furthermore, it became apparent that senior leaders sometimes exhibited overly directive or micromanaging behaviours, inadvertently stifling empowerment, initiative, and learning among project leaders and participants. Therefore, the objective of this first AR cycle was to transition sponsors and senior leaders from unconscious incompetence to conscious incompetence as critical friends and learning facilitators, thereby facilitating a shift toward a focus on learning that would lead to the success of the AR initiative.

He (the action researcher) helped us to see that we as leaders should not try to provide the right solutions. Instead, our job is to help our people learn to find a solution themselves.

Case 2: Toy Company

The second case study focuses on a toy manufacturer’s product development support department, where collaboration with a university characterised an AR initiative. The primary goal was to transfer lean problem-solving practices from a production environment to the knowledge work setting within the support department. This initiative aimed to empower department leaders to act as facilitators of action learning, supporting performance enhancement and cultivating a culture of continuous learning. As with many organisations, the company encountered challenges balancing short-term efficiency improvements with long-term learning capabilities necessary for evolving into a learning organisation. Serving as an insider action researcher, the first author navigated this endeavour.

Echoing the success of the first case study, where the AR initiative effectively tackled a real organisational challenge and contributed to academic literature. The success of this project also owed much to the inherent curiosity and dedication of the executive sponsors towards nurturing learning, personal development, and self-reflection (Kristensen et al. 2022 ). Initially, the sponsor was critical in securing necessary resources and political support. However, his impact on the project’s success was predominantly attributed to his active engagement as a critical friend throughout the project’s lifecycle. In meetings with his organisation, he was adept in posing thought-provoking questions, offering alternative viewpoints on data, and providing constructive feedback.

I used to ask a few questions and then draw my own conclusions. Now, I ask questions that lead employees to form their own conclusions, allowing me to see how they perceive and understand the problem.

Emphasising that critique should be constructive, facilitating more profound understanding and exploration, he created a supportive environment for learning and reflection. By embodying the belief that leaders do not have all the answers and that frontline employees hold valuable insights, he empowered senior leaders and first-line managers to embrace a curiosity-driven approach, facilitating meaningful learning experiences and effective problem-solving.

He (the sponsor) has helped me develop a completely different way of thinking about improving my area as a leader. The most significant insight I’ve gained is that the skills I have developed, such as critical analysis, goal setting, and challenging assumptions, are applicable to my professional work and enrich my everyday life beyond the workplace.

Similar to the first case, the initial AR cycle involved preparing and engaging the head of the support department as a sponsor. The sponsor was supported in experimenting towards facilitating learning and problem-solving using lean practices and thinking on selected projects. Once the department head had developed sufficient proficiency as a learning facilitator, the department initiated the second AR cycle, engaging first-line managers and employees.

Diccussion and Conclusion

The two cases illustrate how enacting and integrating the roles of the formal sponsor, critical friend, and learning facilitator can be crucial for the success of an Action Research (AR) initiative. The formal sponsor provides essential strategic direction, resources, and political support necessary to initiate and sustain the project (Englund and Bucero 2006 ; Coghlan 2019 ). Simultaneously, the critical friend enriches the co-generative learning process through constructive feedback and challenging assumptions, driving deeper reflection and exploration (Campbell et al. 2004 ). The learning facilitator bridges these roles by structuring learning activities, promoting open dialogue, and ensuring that reflections are integrated effectively into the co-generative learning process (Saabye 2023 ). These roles foster a learning environment that supports effective problem-solving, continuous improvement, and mutual knowledge creation. This ensures that the AR initiative achieves meaningful and sustained organisational change.

While both cases were deemed successful, it is essential to acknowledge the complexity of defining success in action research (AR). In the Building Material Firm case, success was reflected not just in the eventual adoption of the digital data-gathering system but also in the proactive involvement of the General Manager as a critical friend and learning facilitator, which significantly enhanced the process and engagement. Similarly, in the Toy Company case, success was evident in the transfer of lean practices and how the executive sponsor’s active engagement in asking thought-provoking questions and fostering a culture of continuous learning contributed to more profound reflections and effective problem-solving. Thus, success in AR is intimately linked to the quality of the processes, stakeholder satisfaction, and the specific context of the initiative. It encompasses the tangible outcomes and the effectiveness of engagement strategies, the responsiveness to feedback, and the alignment with organisational needs. Reflecting on these multidimensional factors offers a more nuanced perspective on what constitutes true success in AR, emphasising the importance of both procedural and contextual considerations in evaluating the overall impact of the initiatives (Coghlan 2019 ).

On reflection, the two cases highlight that impactful sponsorship of AR relies on sponsors enacting three roles: securing funding and political backing, acting as a critical friend, and facilitating learning throughout the initiative. However, these roles may not evolve naturally. The anecdotal conversation noted earlier between the primary author and the doctoral student highlighted the difficulties in engaging an organisation in a co-generative learning process (Elden and Levin 1991 ) despite a formal agreement. The doctoral student’s challenges might indicate that her initiative’s sponsors were not fulfilling these three roles, leading to difficulties in identifying, confronting, and framing concrete organisational problems. These challenges underscore the importance of action researchers securing and facilitating impactful sponsorship. As illustrated in the Building Material Firm and Toy Company case studies, sponsors were crucial in securing resources, actively participating in meetings, offering constructive critiques, and consciously facilitating a supportive learning environment (Kristensen et al. 2022 ; Saabye et al. 2022 ).

Researchers can help facilitate sponsorship commitment and behaviour by engaging sponsors to help them understand their need to transition from passive participants to active learning facilitators driven by curiosity and a commitment to personal and organisational development (Revans 2011 ). An action researcher cannot assume senior leaders know how to enact the different roles of sponsoring an AR initiative. Table  1 outlines the AR sponsorship roles and details how researchers can ensure that sponsors fulfil these roles.

The doctoral student noted earlier and other action researchers can better prepare the sponsors for their roles by initiating individual AR cycles with the sponsors, perhaps as a pre-step, much like the approach in the building material firm case (Saabye et al. 2022 ). Initiating individual AR cycles with the sponsors would involve working closely with sponsors to analyse project challenges and engage in conversations on how they can best mitigate these challenges through the lenses of the different sponsorship roles and especially develop their capabilities as a critical friend and learning facilitator.

These conversations can then inform a process of upstream and downstream learning where the sponsor identifies actions to enact the different roles, carries these actions out and reflects together with the AR as the “friendly outsider”, prompting them to scrutinise their existing flawed assumptions and adjusting their behaviours accordingly (Reason and Torbert 2001 ). Through these learning cycles, sponsors can better understand their responsibilities and how to support the initiative’s goals effectively. Ideally, this individual learning cycle should become regular check-ins and feedback loops with the sponsors. This continuous engagement allows for real-time adjustments and ensures sponsors remain actively involved and committed to the project’s success. By maintaining open lines of communication, the action researcher can address any emerging issues promptly and reinforce the importance of the sponsors’ roles in driving the AR forward (Saabye et al. 2024 ).

Looking ahead, we acknowledge from our combined experiences as action researchers that it can be challenging for a single sponsor to effectively fulfil all three critical roles required for a successful AR initiative in many organisations. That challenge presents an opportunity for further research. The demanding schedules of senior or middle managers may make it difficult for them to take on the role of critical friend and learning facilitator. Hence, in corporate settings where senior managers might be preoccupied with other responsibilities, it may be relevant to explore the separation of the formal sponsor role from the critical friend and learning facilitator roles. It can be asked how consciously allocating distinct individuals for the formal sponsor, critical friend, and learning facilitator roles might impact the effectiveness of support for the AR initiative. From the insights in this paper, we can consider, for example, what happens when the sponsor focuses on securing funding and high-level support, and how a dedicated critical friend and learning facilitator can enrich the learning process by providing necessary challenges and reflections. This separation may allow each role to be performed more effectively, enhancing the overall success and impact of the AR initiative, especially in cases where the actions researcher is coming from outside the organisation.

Data Availability

No datasets were generated or analysed during the current study.

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• Published: 12 September 2024

Promoting sustainable behavior: addressing user clusters through targeted incentives

• Laura Höpfl 1 , 2 , 3 ,
• Maximilian Grimlitza 2 , 4 ,
• Isabella Lang 2 , 5 &
• Maria Wirzberger   ORCID: orcid.org/0000-0003-3072-2875 1 , 3 , 6  

Humanities and Social Sciences Communications volume  11 , Article number:  1192 ( 2024 ) Cite this article

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Given the urgency of climate change action and the significant climate impact of household emissions, understanding the drivers of individuals’ sustainable behavior patterns is more important than ever. Consequently, we investigate whether different clusters of individual users can be distinguished regarding sustainability-related values, attitudes, and intentions. If these diverse clusters exist, we can explore tailored approaches to promote sustainable behavior patterns among them based on their unique needs and targets. For this purpose, we employ a mixed-method approach combining qualitative interviews with a quantitative survey. The obtained insights help us identify core factors that drive sustainable behavior, develop representations of different user groups, and suggest individualized interventions for supporting sustainable behavior patterns. The qualitative part comprised interviews with ten participants, resulting in the development of qualitative personas. Emerging differences could subsequently be used to select validated psychological scales for the quantitative part to confirm the differences. Applying data-driven clustering, we identify five intention-based clusters that vary regarding factors such as belief in climate change, collaboration, or skepticism concerning sustainability. Building on both qualitative and quantitative insights, five validated personas are created for research and practical use. These personas include Socially Sustainable, Responsible Savers, Unconcerned Spenders, Comfort-Oriented, and Skeptical Consumers. Individuals corresponding to the selected persona may, for example, respond positively to sustainability benefits, while others may be more receptive to hedonistic benefits. Addressing related varying motivational factors raises the demand for individualized interventions. These could be achieved by incorporating the personas’ needs with more individualized products and services to promote sustainable behavior.

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Introduction.

Humankind is already experiencing the consequences of unsustainable lifestyles in numerous areas. Climate change and the crossing of other planetary boundaries (Rockström et al., 2009 ) are more present in our daily lives than ever. Heat waves, crop failures, wildfires, droughts with severely decreasing water levels of lakes, floods, and loss of biodiversity are just a few examples of many. One way to address this urgency is through individual behavior change. Mitigating the demand could lead to reductions of 62% (5.8 Gigatons of Carbon Dioxide equivalent [GtCO2e]) in the transport sector and 41% (7.3 GtCO2e) in the food sector (Creutzig et al., 2021 ).

Various groups and initiatives have been working on sustainability-related behavior change of individuals, such as consumer choices. The Intergovernmental Panel on Climate Change (IPCC) describes three factors that mitigate the demand on the consumer side: technology adaptation, infrastructure use, and socio-cultural adaptation. First, technology adaptations like electric vehicles and efficient, lightweight cars could mitigate the greenhouse gas-intensive demand (Creutzig et al., 2022 ). Second, infrastructure use could be enhanced by utilizing sharing concepts like pooled mobility (Creutzig et al., 2022 ), which involves using shared transportation, such as carpooling or public transport. Third, adjusting socio-cultural factors holds significant potential, such as avoiding long-haul flights and transitioning to train travel (Creutzig et al., 2022 ). Systemic change is required to enable individuals to behave more sustainably (Abson et al., 2016 ) as it alters a system’s structures, policies, and processes. Impactful opportunities exist in the interaction of governments, the private sector, civil society, and individuals (Shukla et al., 2022 ). Nevertheless, changing behavior regarding technology adaptation, infrastructure use, and socio-cultural factors is not trivial due to the versatility of strategies and arguments that motivate people to think and act sustainably.

Understanding the parameters of sustainable behavior and the associated theoretical frameworks is crucial for gaining insight into behavioral adaptation possibilities. Behavior is considered sustainable if it meets present needs without jeopardizing the ability of future generations to meet their needs (Brundtland, 1987 ). Several theories and frameworks attempt to explain people’s behavior, each incorporating different behavioral factors. These frameworks can help to identify barriers and develop targeted interventions for sustainable behavior change. Approaching various factors influencing an individual’s behavior can encourage behavioral changes more effectively. The COM-B model proposes Capability, Opportunity, and Motivation as critical components for Behavior change (Michie et al., 2014 ; Michie et al., 2011 ). Capability refers to the ability to do something, opportunity is determined by external factors, and motivation describes the willingness to engage in the behavior. An alternative framework is the theory of planned behavior (Ajzen, 1985 , 1991 ), which specifies attitude, subjective norm, and perceived behavioral control as factors influencing intention and, consequently, the resulting behavior. Lastly, the protection-motivation theory (Rogers, 1995 ) focuses on influencing factors such as self-efficacy, rewards, and costs. These theoretical frameworks help to determine factors that highly influence sustainable behavior, which can vary between individuals. These factors can be grouped into psychological and social components. Psychological factors include attitudes (Haustein & Hunecke, 2013 ; Vermeir & Verbeke, 2008 ), values (McCarty & Shrum, 1994 ; Schultz et al., 2005 ; Schwartz, 2021 ; Vermeir & Verbeke, 2008 ), emotions (Brosch & Steg, 2021 ), self-efficacy (Hanss & Böhm, 2010 ; Hanss & Doran, 2020 ) and personality traits such as narcissism (Lin et al., 2021 ), product involvement (Tarkiainen, Sundqvist 2009 ), convenience (McKenzie-Mohr & Schultz, 2014 ), general interest in sustainability (Matthes & Wonneberger, 2014 ), motivation (McKenzie-Mohr, 2011 ), and behavioral intention (Wang et al., 2014 ). Furthermore, social circumstances such as demographics such as gender (Tautscher et al., 2020 ; Bloodhart & Swim, 2020 ) and social norms (Salazar et al., 2012 ); Schultz et al., 2007 ) can influence behavior. Finally, other factors, such as knowledge (Steg & Nordlund, 2019 ) and greenwashing (Hameed et al., 2021 ), may also impact behavior. These factors have the potential to either support or inhibit sustainable behavior.

Moreover, sustainable behavior might be influenced by cognitive mechanisms of perception and processing. For instance, the attitude-behavior gap, or attitude-action gap, describes a discrepancy between people’s stated environmental concerns and their actual behavior (Juvan & Dolnicar, 2014 ; Terlau & Hirsch, 2015 ; Young et al., 2009 ). For example, an individual can be aware of the high air travel emissions but still choose to travel by plane. Second, individuals are likely to make error-prone assumptions regarding behavior that causes significant harm to the environment. Most individuals overestimate, for example, the effect of CO 2 emissions from the plastics industry, while underestimating the impact of meat consumption or thermal insulation (Bilstein & Rietmann, 2020 ). Interventions can address specific behaviors influenced by the highlighted factors and cognitive effects. According to the behavior change wheel, behavior can be controlled or changed by strategies such as incentives, persuasion, constraints, restrictions, or training (Michie et al., 2014 ). Incentives can provide rewards or benefits for people who behave sustainably, and persuasion can lead to the transmission of social norms that promote sustainable behavior.

People have unique needs due to individual factors, social circumstances, and cognitive mechanisms. These individual influencing factors suggest the effectiveness of personalized targeting. For instance, incentives may be more effective than restrictive strategies for individuals with hedonistic tendencies, as they are primarily motivated by seeking pleasure and personal satisfaction. By contrast, a socially orientated person might be more motivated by feedback from their social environment. Therefore, we need an enhanced understanding of individual factors for sustainable intentions and behavior for different user groups. The analysis of individual influencing factors could help change individuals’ behavior. Clustering of users can account for individual differences and is an appropriate way to identify and describe distinguished groups. Here, personas are a valuable tool for understanding users’ attitudes and behaviors at a descriptive level. A persona represents an individual within a group (Chang et al., 2008 ). This approach is well-established in user-centered research, which aims to create products or services that meet users’ needs more effectively. Employing a cluster analysis approach to create personas and subsequently design targeted interventions is relevant both to research (Brickey et al., 2012 ; Pruitt & Grudin, 2003 ; Tu et al., 2010 ) and practice, for example, in market research institutes (Czioska et al., 2021 ; Gatterer & Tewes, 2023 ; Kroth, 2019 ; Brincken, 2022 ; Tautscher et al., 2020 ).

Building on the outlined evidence, we investigate the effectiveness of an individualized targeting approach to promote sustainability among user groups. To this end, we explore which clusters of sustainable intentions can be identified among individual user groups. More precisely, we are interested in (a) the main differences between personas, and (b) how these differences can be validated with data-driven user clusters (Balderjahn et al., 2018 ; Malatesta & Breadsell, 2022 ; Tabianan et al., 2022 ). Consequently, our investigation consists of two successive parts: a qualitative interview aims to identify sustainable personas with individual influencing factors, and a quantitative survey aims to validate the identified personas by data-driven clustering. The second method employs statistical algorithms to identify patterns and cluster the data systematically. Five personas were identified across both parts, which differed in terms of underlying strategies towards sustainable behavior. Specifically, we identified the Unconcerned Spenders, Skeptical Consumers, Socially Sustainable, Comfort-Oriented , and Responsible Savers . Some of these personas respond to sustainability benefits, while others might be better approachable with benefits appealing to their hedonistic side. The personas also differ in their general affinity for technology, collaboration, and novelty factors. Overall, this work supports the envisioned effectiveness of personalized behavioral interventions.

Part 1: Interview to create sustainable personas

Existing literature extensively examines individual factors, yet formal analyses about different clusters of users are scarce. Awe conducted semi-structured interviews to learn about the needs of different user groups and ultimately create personas. Taking washing behavior as an example, the underlying aim was to identify differences in sustainability factors, enablers, and impediments to sustainable behavior.

Participants

We interviewed ten participants about behavioral characteristics and strategies related to washing habits. The participants ( n  = 10) included women ( n  = 6) and men ( n  = 4) between 26 and 64 years ( M age  = 36.5 years, SD age  = 11.44 years). Participants were recruited through an agency (Lämmler, 2020 ) and were intended to represent a small sample of the German population. In our pre-screening process, we inquired about the participants’ regular washing habits. This ensured that participants were accustomed to regular washing and could provide accurate self-reports based on their memory. Participants received a monetary compensation of 25 € for participating in the interview.

We examined three topics with sub-questions Footnote 1 to explore individual differences related to potential sustainability influencing factors. The interview started by reporting on washing habits as an example of everyday behavior, thereby ensuring that other questions did not influence answers to this question. The second topic included participants’ attitudes and social environment concerning sustainability in everyday life. The last topic focused on whether and how they consider sustainability a criterion in their purchasing decisions. The questions focused on factors from the literature concerning individual and social determinants of sustainable behavior, alongside insights derived from preliminary test interviews. On average, interviews lasted for M length  = 65.4 min (SD = 8.34 min).

Data collection and preparation

The interviews were conducted and recorded with Microsoft Teams, an online collaboration application. Each participant gave written consent. We ensured to closely follow the General Data Protection Regulation (GDPR) principles for storing personal data and checked compliance beforehand. The interview data obtained was transcribed with Adobe Creative Cloud and tagged in MAXQDA a program for qualitative data analysis.

Data exploration

Reflexive thematic analysis (RTA) was utilized for data analysis inductively to interpret qualitative datasets (Braun & Clarke, 2019 ). This method includes rereading the interview transcripts to analyze overreaching themes across participants. Related excerpts were collected, reread, and categorized by two independent raters. We conducted a systematic and iterative review of the transcripts and their associated categories using the KJ Method (Scupin, 1997 ), also known as affinity mapping, to identify and extract themes. First, excerpts were sorted and comparatively organized to discover emerging themes or patterns between excerpts. Afterward, we created personas by affinity mapping and assigning experts according to the main themes. Core differentiating characteristics of participants were synthesized, and in a final step, resulting clusters were supplemented by demographic information and a name for each persona.

Part 2: Online questionnaire to validate sustainable personas

Following the outlined phase of conceptual development, a quantitative exploration served the purpose of validating the obtained personas by formalized clustering. During the subsequent stages of the research, the conducted study adhered to the applicable guidelines and regulations as outlined in Standard 8 of the Ethical Principles and Code of Conduct for Psychologists (American Psychological Association, 2010 ) Footnote 2 . This involved obtaining informed consent from all participants and securing ethical approval. Furthermore, compliance with the General Data Protection Regulation (GDPR) was ensured.

Tying in with related prior research (e.g., Balderjahn et al., 2018 ; Lee & Haley, 2022 ; Niedermeier et al., 2021 ), the final sample included 342 participants aged 18 years or older (174 females, 165 males, and three diverse; M age  = 49.9 years, SD age  = 17.16). Participants were recruited by an agency (Bidou, 2020 ). The utilized age distribution was aligned with a report by the Federal Statistical Office to represent the age demographics of Germany’s adult population (Min age   ≧  18) (Destatis, 2022 ). Most participants lived in either 2-person households ( n  = 144, 42.1%) or alone ( n  = 94, 27.5%). Furthermore, most participants had no children living in the same household ( n  = 248, 72.5%). The household income per month was mainly in the range of 3600 € to 4999 € ( n  = 74, 21.6%), followed by ranges of 2600 € to 3599 € ( n  = 66, 19.3%), 2000 € to 2599 € ( n  = 47, 13.7%) and 1500 € to 1999 € ( n  = 39, 11.4%).

Building on the main themes emerging from the interviews and recent research on segmenting users’ sustainable behaviors (Balderjahn et al., 2018 ; Lee & Haley, 2022 ; Niedermeier et al., 2021 ), we selected a set of validated scales corresponding to five subtopics. These cover aspects such as individual attitudes, social factors, price, understanding of sustainability, and knowledge. More precisely, regarding the subtopic Sustainable Attitudes and Values , we collected data on personal environmental responsibility (Lai & Cheng, 2016 ; Lee, 2008 ), environmental concern (Lee, 2008 ; Straughan & Roberts, 1999 ), human values (Schwartz et al., 2001 ), and consciousness for sustainable consumption (CSC) scale, including an environmental, social, and economic dimension (Balderjahn et al., 2018 ; Ziesemer et al., 2016 ). Regarding the subtopic Knowledge and Information , we collected data on affinity for technology (Neyer et al., 2016 ), knowledge of pro-environmental products (Flynn & Goldsmith, 1999 ), skepticism towards pro-environmental advertising (Mohr et al., 1998 ; Obermiller & Spangenberg, 1998 ), and trust (Voon et al., 2011 ). We further included the Perceived Consumer Effectiveness scale, which builds on the planned behavior theory (Roberts, 1996 ). Regarding the subtopic of Social Factors , we collected data on social status and social norms (Lee, 2008 ; Suki & Suki, 2015 ). Additionally, we obtained data on economic benefits regarding the subtopic Price .

Data collection and preprocessing

Participants were asked to complete an online survey presented in a matrix-questionnaire format utilizing Qualtrics, an online survey platform. The survey was designed for 10–20 min and required a median duration of 19.05 min (SD = 5.97 min) for completion across participants. To ensure appropriate data quality, we had to exclude several of the initially recruited 444 participants, as they did not consent to data collection, did not meet the age requirements ( n  = 8), did not complete the survey ( n  = 16) or admitted inattentive processing ( n  = 6). In addition, participants with a survey completion time lower than 10% of the sample average, corresponding to an engagement of less than 553 s, were also excluded ( n  = 41).

Clustering procedure

Utilizing Python 3 (Van Rossum & Drake, 2009 ), Anaconda 3 (Anaconda, 2020 ), and the Scipy (Virtanen et al., 2020 ) and Sklearn (Pedregosa et al., 2011 ) libraries, we implemented a Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) clustering algorithm in Jupiter Notebook (Kluyver et al., 2016 ). Across all included variables, data was standardized and scaled with the MinMax-Scaler. Following general recommendations that the sample size should outperform the number of variables by a factor of 70 (Dolnicar et al., 2013 ), we first separated cluster variables and covariates to avoid including an overly high number of variables (Fraiman et al., 2008 ). Considering our sample size, we included a final set of five variables for dimensionality reduction. Thereby, we eliminated variables with a low variance ( \({Var} < =0.045)\) in the process of feature selection.

Following established two-stage clustering procedures (e.g., Balderjahn et al., 2018 ; Hellwig et al., 2015 ; Lee & Haley, 2022 ), we applied HDBSCAN, as this algorithm incorporates the advantages of hierarchical and density-based clustering approaches. Results were evaluated utilizing both the Calinski-Harabasz index (Calinski & Harabasz, 1974 ) and the silhouette scores (Rousseeuw, 1987 ). To enhance the interpretability of clustering outcomes, we employed an unsupervised machine-learning approach in the form of a decision tree (Laber et al., 2023 ). Decision trees can generally be helpful because they provide an approximate clustering result, facilitate examining feature interactions in the data, and offer reasonable explanations for clustered contents (Molnar, 2022 ). To evaluate the relationship between clusters, we employed descriptive statistics. The differences between obtained clusters were calculated using a Tukey Honestly Significant Difference (HSD) Test (Tukey, 1949 ), providing the foundation for deriving the personas.

Part 1: Identification of sustainability personas

Thematic analysis (Braun & Clarke, 2019 ) revealed three primary themes: prerequisites for sustainability, enabling factors, and barriers, as presented in Table 1 . More precisely, the prerequisites for sustainability included understanding sustainability, knowledge, self-efficacy, and sustainable attitudes and values. These conditions were present in the more sustainable personas, while the less sustainable personas lacked one or more of these prerequisites. Enablers and barriers can work in both directions, as social factors and tangibility can promote and hinder sustainable behavior. Participants reported that social factors, such as a sustainable peer group, increased sustainability intention, while stress and convenience hindered sustainable behavior.

Interactions of the themes outlined in Table 1 are presented in Fig. 1 . According to the depicted model, a weighing pan can be built for each user and sustainability decision. The example displays a user who can inform themselves but has lower environmental knowledge. The primary enabler for the user is their social group; the most significant hindering factor is the price. Factors could be switched in position (enabling or hindering) for another user or situation or appear on both sides. For each new decision concerning behavior, the scale can look different and swing to the other side. In this case, the user has performed the sustainable behavior since the enablers outperform the hindering factors. Analyzing different situations can help identify the main influencing factors across individuals. In the depicted example, unsustainable behavior emerges from the predominating right side, while a predominating left side results in sustainable behavior. Based on the model and the factors included, five qualitative personas were created.

The depicted example shows how the prerequisites, enablers, and hindering reasons play together, thereby emphasizing the significance of portraying mutual aspects of each entity rather than focusing solely on absolute values.

The Activist persona is 25 years old, male, and works in the solar industry. Typical statements from him include, “I am willing to spend more money on sustainable products.”, “I am very well able to inform myself about sustainability.”, and “I feel addressed by sustainable products and question them concerning greenwashing.” Factors influencing this persona are values, social contacts, political opinions, and belief in climate change.

The Sustainability-interested persona is 46 years old, female, has one child, and works as a florist. Typical statements from her include, “I can inform myself about sustainability, but I do not have enough time to do so.”, and “I feel addressed by sustainable products.” Values, social contacts, and time pressure mainly influence this persona.

The Hedonistic persona is a 30-year-old female who works as a cultural manager. Typical statements from her include, “I am attracted to products that I enjoy.”, “I can inform myself about sustainability, but I do not have enough time to do so.” and “I am only willing to spend more money on sustainability if it pays off for me, e.g., through money or fun.” Factors influencing this persona are convenience, monetary incentives, and time pressure.

The Indifferent persona is 37 years old, male, and works as an event technician. Typical statements from him include, “I am not willing to spend more money on sustainable products” and “I find it hard and not interesting to inform myself about sustainable products.” Factors influencing this persona are convenience, social contacts, monetary incentives, and specific situations.

The Dismissive persona is a 65-year-old retired male with two grown children. Typical statements from him include, “I am attracted to products that I enjoy.”, “I am not willing to spend more money on sustainability.”, and “I am not influenced by the opinions of others about sustainability”. Convenience, social contacts, and monetary incentives mainly influence this persona.

The personas, enablers, and barriers were used to select validated scales from the literature to enable the collection of quantitative data for executing a more formalized clustering related to behavioral prerequisites underneath.

Part 2: Validation of sustainable personas by clustering

Building on the results of the first part, we selected the features social status, trust, skepticism, economic benefit, and care for sustainability for clustering. Clustering the data with HDBSCAN (Campello et al., 2013 ) provided a silhouette coefficient of \({Sc} < =.0583\) , and the algorithm clustered 326 from the total of 342 samples. In general, a silhouette score can range from a perfect representation \(\left({Sc}=+1\right)\) to a wrong representation \(\left({Sc}=-1\right)\) (Shahapure & Nicholas, 2020 ). Based on the clustering results, a decision tree was used to explain cluster descriptions. Based on a grid search, the tree’s preferable depth was set to 5. The five layers pruned decision tree resulted in an entropy of \(H > =0.91\) and a Gini coefficient (Breiman et al., 2017 ) of \({G}^{{ML}}=83.5\) . While an entropy value close to \(H=1\) is considered ideal (Celeux & Soromenho, 1996 ), a value \(H > =0.80\) is considered acceptable. Figure 2 depicts one selected path from the decision tree as example, and the complete decision tree can be found in the supplement.

A decision tree based on the clustering results shows which factors lead to the clustering of certain groups. Clusters can include multiple paths of a decision tree. The averages of the scales are scaled to values between 0 and 1.

Table 2 outlines scaled results of the five clusters derived from the decision tree in terms of attitudes, values, and behavioral implications. Results for the post hoc Tukey’s HSD analysis are presented in Table 3 , exploring significant differences between clusters.

The Socially Sustainable Cluster (1) comprises 39 participants with strong beliefs in human-made climate change, concern for sustainability, engagement, perceived consumer effectiveness, environmental knowledge, trust in eco-labels, social status, social norms, environmental responsibility, and conservation values. Participants assigned to the Social Sustainable Cluster display a strong awareness of sustainable consumption across all areas of the CSC. They are motivated to protect the environment and recognized by their social peer group for exerting responsible behavior. Additionally, this cluster is more tech-savvy and collaborative than the average consumer and shows low skepticism toward sustainable labels.

The Responsible Savers Cluster (2) comprises 86 participants with a strong believe in human-made climate change, environmental responsibility, and conservation values. This cluster also values simplicity, collaboration, and debt-free consumption. Participants assigned to the Responsible Saver Cluster prioritize sustainable purchases and are motivated by environmental concerns, with a high level of knowledge about sustainable products. They are more sustainable and less tech-savvy than the average consumer.

The Unconcerned Spenders Cluster (3) comprises 84 participants exhibiting low beliefs in human-made climate change, care for sustainability, and environmental responsibility. This cluster is further characterized by valuing hedonism and spending money over sustainability, with little awareness of sustainable consumption practices. Participants assigned to the Unconcerned Spenders Cluster show average values for social status and trust in eco-labels but low values for all other subscales, including preference for socially sustainable products, collaboration, simplicity, and debt-free consumption. This cluster demonstrates little environmental concern and a focus on immediate gratification rather than long-term sustainability.

The Comfort-Oriented Cluster (4) cluster comprises 62 participants exhibiting the lowest engagement levels across all clusters and further scoring low on environmental concern, knowledge, and technical affinity. Furthermore, the cluster is characterized by below-average values for openness to change, belief in human-made climate change, care for sustainability, and perceived consumer effectiveness. Additionally, participants assigned to the Comfort-Oriented Cluster display slightly above-average values for social status, social norms, environmental responsibility, self-enhancement, skepticism towards sustainable labels, and trust in eco-labels. The Comfort-Oriented Cluster reports a low awareness of sustainable consumption and identifies money and comfort as the primary hindering reasons toward exerting sustainable behavior.

The Skeptical Consumer C luster (0) comprises 55 participants with the lowest values across all clusters for skepticism towards sustainable labels, trust in eco-labels, and care for sustainability. They also indicate below-average values for engagement, perceived consumer effectiveness, social norms, affinity for technology, conservation values, and beliefs in human-made climate change. Participants assigned to the Skeptical Consumers Cluster display average values for environmental concern and rank slightly above average for environmental knowledge, environmental responsibility, openness to change, self-enhancement, and self-transcendence. The Skeptical Consumers Cluster is not motivated to act sustainably by environmental concerns or social recognition and exhibits the highest cost values as a barrier to adopting sustainable behavior.

The qualitative-derived personas from the first part to the quantitative clusters from part two are mapped in the following. First, the Hedonists compare well with the Unconcerned Spenders , while the Skeptical Consumers roughly correspond to the Dismissive Cluster . The Indifferent persona corresponds to the Comfort-Oriented persona , and the Activists and Interests are distributed among the Socially Sustainable and Responsible Savers clusters. Generally, some contributing factors can be found in both analysis steps, but not all descriptively emerging aspects were included in the formal clustering process. For example, barriers like time pressure or stress were not part of this inspection. When considering all clusters, the factors of costs and convenience emerged as the most significant obstacles to behavior change.

Considering the substantial impacts of private household emissions on climate change, understanding the drivers of individual sustainable behavior gains increasing importance. Consequently, our research aimed to advance understanding of users’ thinking and acting related to sustainability practices and concerns. We conducted both qualitative interviews and a quantitative survey based on validated scales. Diverse influencing factors among individuals were identified as a critical challenge. To address this, we focused on clustering users based on their sustainable intentions and derived five distinct personas and conceptually related formalized clusters. Both descriptive personas and formal clusters can inform product development and communication strategies to promote sustainable behavior. Therefore, our findings provide a practical framework for translating user insights into actionable steps.

Overview of key findings

Critical factors of sustainable behavior emerged from the conducted qualitative semi-structured interviews. More precisely, responses highlight values, social norms, belief in climate change, and barriers such as price sensitivity, convenience, and tangibility as primary contributing factors. Based on these insights, we created five personas with unique perspectives on sustainability based on attitudes, behaviors, beliefs, and values: the Activist, Interested, Indifferent, Hedonist, and Dismissive . While the Activist actively promotes a more sustainable world and engages in sustainable behaviors, the Interested may invest less time in sustainable behavior but still practice it. By contrast, the Indifferent is not interested in the issue of sustainability, and the Hedonist is primarily interested in financial gains from their investments. Finally, the Dismissive is less likely to believe in human-caused climate change and may be resistant to adopting sustainable behaviors. By understanding these personas, efforts to engage and educate individuals about sustainability can be designed more effectively. Although these personas may appear stereotypical, they still hold descriptive value and provide directions for more thorough quantitative validation. It is vital to acknowledge that the personas presented serve as one potential cluster representation. As such, variables like age and gender serve as exemplary indicators and are based on the median of the clusters grounded in actual participant data.

We systematically assessed differentiating factors underneath the described personas using a quantitative survey. Therefore, we analyzed the obtained data for critical factors, including environmental concern, trust and skepticism for sustainable labels, economic benefit, and social status. These variables explained the highest variance in the data set, consequently serving as influential clustering factors. Building on interview insights and additional related research, we opted to use five clusters. Therefore, each data-driven cluster represents one persona, which differs regarding configuration and characteristics of influencing factors. In summary, the Unconcerned Spenders Cluster is more hedonistic and has higher scores on the economic benefit scale than the other clusters. By contrast, the Skeptical Consumer Cluster is less likely to believe in climate change and is skeptical about sustainable labels. In addition, the Socially Sustainable Cluster places a high value on its social image and is motivated to be sustainable as a result. The Responsible Saver Cluster is motivated by environmental awareness and has a deeper understanding of sustainable products. Finally, the primary motivators of the Comfort-Oriented Cluster are money and convenience.

From a methodological viewpoint, combining quantitative and qualitative data in mixed-methods research strengthens the robustness of the inference compared to using either approach alone. While qualitative research can add an in-depth understanding of the results, quantitative research can provide numerical data, offering possibilities for statistical analyses and generalization (Creswell, 2009 ). During the interviews, we noted that products require more personalized communication and product design strategies. Recognizing the emerging potential of individualized approaches, we aimed to validate the obtained insights through a subsequent structured survey. We carefully selected scales that had undergone rigorous validation to ensure the validity and robustness of our data. Upon inspection, we found a significant difference in the broad subtopics, such as values or hindering factors, among various users. To explore these subtopics further, we identified literature-based reliable measures that could be used in the second part.

Limitations

Taking on a critical perspective, our research faces several limitations. First, sample sizes were determined by reviewing the relevant literature, ensuring our results were scientifically rigorous and reliable (Dolnicar et al., 2013 ); Guest et al., 2006 ). However, it is essential to note that the observed outcomes may require further generalization to account for potential bias resulting from the sample characteristics. While our interviews aimed to identify and describe the factors influencing sustainable behavior, we faced capacity limits on the number of factors we could validate quantitatively. We also recognized a potential bias towards apparent factors and plan to explore subordinate factors in future studies. It is worth mentioning that the sample size for the validation part was appropriate, but having more samples could continuously improve the robustness and generalizability of the findings.

Moreover, the overall applicability of the current evidence might be slightly limited due to the use of an online access panel, the exclusion of participants, and the fact that the study was solely conducted in a German context. Given the chosen recruitment means, some participants may have only participated in our research for monetary reasons. To ensure the quality of the data, we set a shorter survey time limit during preregistration and excluded faster participants. Information about agency-specific recruitment methods for bias mitigation is available in online resources (Bidou, 2020 ; Lämmer, 2020 ). To exclude international dependencies, only German-speaking participants were accepted for the study, limiting the emerging personas to a particular set of national characteristics. Hence, other populations and contexts could be researched in the future.

While participating in the survey part, respondent fatigue is possible due to the survey’s length and matrix questionnaire format. To overcome this problem in future studies, we will utilize a short screening instrument to determine individual cluster assignments. In the development process, items with particularly high value for the explained variance were chosen. The underlying goal relates to assessing the effects of individualization measures on different user groups more easily by splitting samples in a construct-oriented manner. However, relying solely on self-reported intentions may not accurately predict actual behavior. Future research could measure user behavior by employing a broader methodological spectrum, including direct behavioral observations. On this base, personalized design strategies promoting sustainability could be implemented and tested. Randomized controlled experiments comparing individualized solutions could be an effective way to determine the effectiveness of selected strategies in promoting sustainable behavior change.

Regarding data exclusion , it is essential to note that excluding certain participants from a study is always prone to introducing selection bias into the data and lowering the sample’s representativeness. For example, excluding longer trials may systematically exclude parents who need to take breaks in the survey to supervise their children. Therefore, data exclusions may result in a biased depiction of the study’s target population, leading to distorted clusters. Identifying and removing participants with response patterns that do not provide valuable information but instead add noise and distortions to the dataset is essential. One such pattern is straight liners, where participants answer with the same response in every question, resulting in significantly shorter survey times than the sample average. To tackle this issue, we employed meaningful time limits and strategic analyses to identify and remove participants identified as straight-liners.

Lastly, it is essential to note that the variables do not necessarily capture the full complexity of the cluster. Nevertheless, the HDBSCAN algorithm identifies clusters with varying densities, regulates noise, and detects clusters with arbitrary shapes. In contrast, other algorithms, such as K-Means or DBSCAN, cannot handle noise or detect clusters with varying densities (McInnes et al., 2017 ).

Implications

The emerging personas gave user-centered insights into the characteristics of user groups. We have shown in qualitative and quantitative data that people possess diverse factors, including motivations, values, and the significance of social status, which could promote the adoption of sustainable behaviors. However, the described clusters are based on self-reported intentions, not actual behavior. Since the attitude-behavior gap is a recognized factor in psychological research (Claudy et al., 2013 ), these clusters should be validated based on behavior. In general, the attitude-behavior gap may be most prevalent in all sustainably oriented groups. Thereby, a lower awareness of sustainability might result in a smaller attitude-behavior gap. In particular, the Socially Sustainable Cluster may be prone to the attitude-behavior gap because of the higher influence of their peer group. Hence, it is essential to consider that other socially based biases can also influence whether individuals adopt sustainable strategies in everyday behavior. These insights ease the understanding and addressing of users and direct the focus to further differences in cognition, which could play a significant role.

We observed individuals making error-prone assumptions regarding high-impact behavior . High-impact behaviors concerning greenhouse gas emissions involve mobility, buildings (insulation and heating), and nutrition (Antony et al., 2020 ; Tukker & Jansen, 2006 ). Most participants wrongly assumed plastic bags consume more CO 2 than high-impact behavior, such as insulated living spaces. Furthermore, participants wrongly assumed that more energy is used in the eco-program than in the standard washing program because it takes twice the time. Individuals with sustainable attitudes had those incorrect assumptions, too. However, they were less prevalent compared to participants who were less concerned about sustainability. Increasing environmental knowledge can be beneficial for behavior change (Frick et al., 2004 ) and is a possible behavioral intervention. Including education about behaviors with high CO 2 emissions is crucial, even though knowledge alone is no significant predictor for sustainable behavior (Heeren et al., 2016 ). Hence, it is crucial to integrate knowledge with other effective strategies, such as modifications in choice architecture or socio-cultural transformations like cultivating an eco-surplus culture. This might entail promoting a shared set of pro-environmental attitudes, values, beliefs, and behaviors among a community to minimize adverse human-induced effects on the environment and preserve and rehabilitate nature (Nguyen & Jones, 2022 ). The extent of observed individual differences suggests that targeting different clusters with tailored strategies is promising. Therefore, individualized approaches according to the user’s key motivators must be developed and translated into practice, for example, through product design. An initial approach would involve the consideration of diverse group needs while providing products tailored to specific clusters. It might not be necessary to tailor the products entirely to each user if the requirements of diverse user groups are considered. Within this scope, mass individualization could play a role since it is also predicted to be the next paradigm in product design (Koren et al., 2015 ). Climate communication should also be targeted and tailored to the individual needs of the population (Bostrom et al., 2013 ). In communication, the focus could be on milieu-dependent role models to promote them. For some clusters, ecotainment could be used to address such purpose, as Reisch et al. ( 2016 ) have shown, for the early stages of consumer behavior adaptation.

Furthermore, the saturation of a specific behavior within the peer group is essential for individual behavior change, as known from diffusion research (Rogers, 1995 ). The importance of saturation could be especially true for the Socially Sustainable Cluster, as social norms influence this cluster in particular. Implementing small behavioral changes is crucial to enhance saturation. One possible approach is to change the behavior in small steps. The less an individual must change their behavior, the more likely the change will be adopted (Fogg, 2020 ). For instance, automated heating systems allow a straightforward adaption of the temperature on all radiators simultaneously, thus simplifying the regulation considerably.

Changing the behavior with small steps could be promoted by individualized product designs and communication. One way to analyze individualized behavior intervention relates to the COM-B model (Michie et al., 2011 ). Since the underlying approach allows to identify barriers and develop behavior-change interventions, it could be used to target specific clusters. Therefore, more hedonistic clusters could be addressed with interventions from the incentives category. Future research could investigate in detail which clusters respond particularly well to different interventions. On a related note, the change in the choice architecture relates to the behavior change wheel (Leal & Oliveira, 2020 ). In general, the choice architecture presents options or choices to the individual in a modified form to stimulate a specific behavior. Modifications include changing the default option, providing information or feedback, or simplifying complex choices. By changing the choice architecture, individuals can be encouraged into more sustainable choices or behaviors (Thaler & Sunstein, 2008 ). Choice architecture can also impact behavior change on a large scale (Marteau et al., 2021 ).

Individual differences provide insights for user-centered product development, services, and communication strategies that target segments on an individual level. Building on the obtained results, communication strategies and product development should prioritize individual factors like sociality or hedonism to promote sustainability. For example, a persona derived from the Socially Sustainable Cluster ranks high on social status and social norms scales. Simplified usage and feedback mechanisms can motivate Responsible Savers to behave more sustainably. While simplified usage benefits all clusters, the Comfort-Oriented Cluster would particularly benefit from simplification approaches as a predominant emphasis on comfort and money hinders them. Unconcerned Spenders may benefit from receiving feedback on monetary savings related to choosing sustainable strategies, considering their high levels of hedonism and spending. Similarly, Skeptical Consumers may respond positively to a product design that includes monetary feedback, allowing them to spend more money on enjoyable activities rather than unsustainable behavior like washing clothes at unnecessarily high temperatures. Engaging in dialog with Skeptical Consumers is especially important, as they may be open to change but put lower emphasis on mitigating climate change or trusting sustainable product labels. Furthermore, it may be promising to encourage perceived consumer effectiveness in less sustainable groups and then evaluate the impact.

While individualization may offer advantages in meeting specific user needs, it is essential to consider potentially related drawbacks. For example, individualization can create privacy issues regarding data collection, higher expenses, and restricting resources for both the user and the organizations involved. Creating individualized products and services can be more expensive than standardized ones, leading to higher costs for both the company and the consumer. Some companies may require more resources to create personalized products and services, limiting their ability to compete in the market. Additionally, creating customized products and services can take time and effort, slowing production and hindering innovation. Collecting and using individual data to create personalized products and services can raise consumer privacy concerns. When developing products and communication strategies, it is essential to keep these challenges in mind. Consequently, exploring ways to encourage sustainable behavior by individualization raises the necessity and offers promising avenues for further related research.

Future research

Addressing the previously outlined demands, future research should explore more thoroughly to what extent personalized interventions can promote sustainable behavior. Follow-up studies are necessary to validate the effectiveness of individualization for behavior change. Thereby, measuring actual behavior while robustly controlling for various factors is crucial. In addition, subsequent research should expand its scope to encompass other cultural contexts and larger sample sizes, enabling a more comprehensive understanding of the viability of personalized interventions. Given the dynamic nature of user behavior, regularly reviewing and updating segmentation variables holds essential value. Furthermore, to maintain accurate representations of the envisioned target audience (Brickey et al., 2012 ; Pruitt & Grudin, 2003 ), it is important to periodically reassess and refine the derived personas and related sustainability clusters in research and practice.

While individual behavior change is essential, it cannot foster the transition to a more sustainable world on its own. Governments, the private sector, civil society, and individuals must jointly create beneficial conditions for change. Examining derived strategies for product development, design, and communication for each cluster holds valuable potential for practical application. Communicators, product managers, and designers may evaluate the practical usefulness of the results. This evaluation could be done in a focus group, which applies the knowledge base related to the personas to a concrete product.

Climate change and related threats require immediate actions, not only by economic and political stakeholders but also by individuals in private households. Consequently, the outlined research attempted a holistic approach to systematically consider a broad range of individual factors, such as needs related to sustainable consumption, motivations, values, intentions, and social norms, allowing the development of more individualized interventions for behavior change. Our main contribution is the integration of individual needs into easily understandable and usable personas, which are backed up by data-driven clusters. The identified clusters and personas provide a starting point to bridge the gap between theory and practice, allowing to address the needs and characteristics of different user groups more systematically. Thereby, critical areas, such as high-impact sustainable behavior, could become a fundamental part of user-centered product development, design, and communication of products and services. In conclusion, by tailoring interventions to individuals, we can increase their motivation and engagement in sustainable practices, ultimately leading to a more environmentally conscious society.

Data availability

The datasets generated and analyzed during the current study as well as the supplementary material (preregistration, an overview of the questionnaires, utilized measurements, related interview excerpts and the personas) are available at https://osf.io/xhwne .

The raw data, an overview of the questionnaires, utilized measurements, related interview excerpts and the personas can be found in the supplementary material at https://osf.io/xhwne .

Deviations from preregistration: We decided to expand the included sample size beyond the preregistered records to better capture the diversity of the underlying population. In addition, the finally applied exclusion criteria for data sets differed from those previously specified, because the pretest on duration was performed with inexperienced questionnaire respondents. By contrast, the final sample consisted of participants very skilled in answering survey questions. Moreover, the main classification related to our clustering procedure was explorative in nature, since the preregistered CSC scale explained much less variance compared to the other included factors. Finally, we employed an initially not planned Tukey HSD test to thoroughly control for multiple hypothesis testing and avoid type-I-error inflation.

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Acknowledgements

The reported research was funded by the Robert Bosch GmbH and supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—EXC 2075-390740016. We acknowledge the support of the Stuttgart Center for Simulation Science (SimTech).

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L.H.: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing—original draft, Writing—review & editing. M.G.: Formal analysis, Investigation, Validation, Writing—review & editing. IL: Investigation, Writing—review & editing. M.W.: Conceptualization, Funding acquisition, Methodology, Resources, Supervision, Writing—review & editing. Use of generative AI: We acknowledge the use of Grammarly and Chat GPT for language editing purposes.

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Höpfl, L., Grimlitza, M., Lang, I. et al. Promoting sustainable behavior: addressing user clusters through targeted incentives. Humanit Soc Sci Commun 11 , 1192 (2024). https://doi.org/10.1057/s41599-024-03581-6

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DOI : https://doi.org/10.1057/s41599-024-03581-6

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Congress created the Public Service Loan Forgiveness (PSLF) Program in 2007 as part of the College Cost Reduction and Access Act, Public Law 110-84 , to encourage individuals to enter into and remain employed in public service professions. The program alleviates financial burdens associated with Federal Direct Loans for borrowers working for certain public service providers by forgiving all remaining loan balances following 10 years of public service while the borrower makes qualifying student loan payments. Since its creation in 2007, PSLF has been available to borrowers working for government at all levels, non-profit organizations that are tax-exempt under section 501(c)(3) of the Internal Revenue Code, and other non-profits that provide at least one of the specific services listed in the statute. This includes early care educators who work in the public sector or for non-profit organizations.

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Nasser Paydar,

Assistant Secretary, Office of Postsecondary Education.

1.  These estimates are from the Administration for Children and Families' National Survey of Early Care and Education, both the 2019 Home-Based NSECE chartbook and the 2019 Center-Based NSECE chartbook. These data show that approximately three-fourths of home-based providers had at least some college, and 72 percent of for-profit ECE workers had some college or higher.

2.   https://www.federalregister.gov/​documents/​2022/​07/​13/​2022-14631/​student-assistance-general-provisions-federal-perkins-loan-program-federal-family-education-loan .

3.   https://www.federalregister.gov/​documents/​2022/​11/​01/​2022-23447/​institutional-eligibility-under-the-higher-education-act-of-1965-as-amended-student-assistance .

4.  Section 103(8) of the Higher Education Act contains a definition of ”early childhood education program” that includes public preschool, Head Start, and State licensed and regulated child care programs. It does not speak to the tax-status of providers. Unlike the public Kindergarten through 12th grade system, which provides free access to education for all age-eligible children and youth, there is no parallel system for our country's youngest children. As a result, ECE is delivered through a system of mixed delivery that includes public programs, non-profit settings, and for-profit settings. https://www.acf.hhs.gov/​ecd/​policy-guidance/​dear-colleague-letter-mixed-delivery . The vast majority of ECE settings are home-based, and do not carry non-profit tax designations. Compensation across settings is low generally, regardless of the tax-status of the ECE provider. https://www.bls.gov/​oes/​current/​oes_​va.htm .

5.   Federal Register : Increasing Access to High-Quality Care and Supporting Caregivers.

[ FR Doc. 2024-13446 Filed 6-18-24; 8:45 am]

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