does action research work

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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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the encyclopaedia of pedagogy and informal education

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 ., what is action research.

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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Action Research

What is action research.

Action research is a methodology that emphasizes collaboration between researchers and participants to identify problems, develop solutions and implement changes. Designers plan, act, observe and reflect, and aim to drive positive change in a specific context. Action research prioritizes practical solutions and improvement of practice, unlike knowledge generation, which is the priority of traditional methods.  

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Why is Action Research Important in UX Design?

Action research stands out as a unique approach in user experience design (UX design), among other types of research methodologies and fields. It has a hands-on, practical focus, so UX designers and researchers who engage in it devise and execute research that not only gathers data but also leads to actionable insights and solid real-world solutions. 

The concept of action research dates back to the 1940s, with its roots in the work of social psychologist Kurt Lewin. Lewin emphasized the importance of action in understanding and improving human systems. The approach rapidly gained popularity across various fields, including education, healthcare, social work and community development.  

Kurt Lewin, the Founder of social psychology.

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In UX design, the incorporation of action research appeared with the rise of human-centered design principles. As UX design started to focus more on users' needs and experiences, the participatory and problem-solving nature of action research became increasingly significant. Action research bridges the gap between theory and practice in UX design. It enables designers to move beyond hypothetical assumptions and base their design decisions on concrete, real-world data. This not only enhances the effectiveness of the design but also boosts its credibility and acceptance among users—vital bonuses for product designers and service designers. 

At its core, action research is a systematic, participatory and collaborative approach to research . It emphasizes direct engagement with specific issues or problems and aims to bring about positive change within a particular context. Traditional research methodologies tend to focus solely on the generation of theoretical knowledge. Meanwhile, action research aims to solve real-world problems and generate knowledge simultaneously .  

Action research helps designers and design teams gather first-hand insights so they can deeply understand their users' needs, preferences and behaviors. With it, they can devise solutions that genuinely address their users’ problems—and so design products or services that will resonate with their target audiences. As designers actively involve users in the research process, they can gather authentic insights and co-create solutions that are both effective and user-centric.  

Moreover, the iterative nature of action research aligns perfectly with the UX design process. It allows designers to continuously learn from users' feedback, adapt their designs accordingly, and test their effectiveness in real-world contexts. This iterative loop of planning, acting, observing and reflecting ensures that the final design solution is user-centric. However, it also ensures that actual user behavior and feedback validates the solution that a design team produces, which helps to make action research studies particularly rewarding for some brands. 

Designers can continuously learn from users’ feedback in action research and iterate accordingly.

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What is The Action Research Process?

Action research in UX design involves several stages. Each stage contributes to the ultimate goal: to create effective and user-centric design solutions. Here is a step-by-step breakdown of the process:  

1. Identify the Problem

This could be a particular pain point users are facing, a gap in the current UX design, or an opportunity for improvement.  

2. Plan the Action

Designers might need to devise new design features, modify existing ones or implement new user interaction strategies.  

3. Implement the Action

Designers put their planned actions into practice. They might prototype the new design, implement the new features or test the new user interaction strategies.  

4. Observe and Collect Data

As designers implement the action they’ve decided upon, it's crucial to observe its effects and collect data. This could mean that designers track user behaviors, collect user feedback, conduct usability tests or use other data collection methods.  

5. Reflect on the Results

From the collected data, designers reflect on the results, analyze the effectiveness of the action and draw insights. If the action has led to positive outcomes, they can further refine it and integrate it into the final design. If not, they can go back to plan new actions and repeat the process.  

An action research example could be where designers do the following: 

Identification : Designers observe a high abandonment rate during a checkout process for an e-commerce website. 

Planning : They analyze the checkout flow to identify potential friction points.  

Action : They isolate these points, streamline the checkout process, introduce guest checkout and optimize form fields.  

Observation : They monitor changes in abandonment rates and collect user feedback.  

Reflection : They assess the effectiveness of the changes as these reduce checkout abandonment.  

Outcome : The design team notices a significant decrease in checkout abandonment, which leads to higher conversion rates as more users successfully purchase goods.  

What Types of Action Research are there?

Action research splits into three main types: technical, collaborative and critical reflection.  

1. Technical Action Research

Technical action research focuses on improving the efficiency and effectiveness of a system or process. Designers often use it in organizational contexts to address specific issues or enhance operations. This could be where designers improve the usability of a website, optimize the load time of an application or enhance the accessibility of a digital product.  

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2. Collaborative Action Research

Collaborative action research emphasizes the active participation of stakeholders in the research process. It's about working together to identify issues, co-create solutions and implement changes. In the context of UX design, this could mean that designers collaborate with users to co-design a new feature, work with developers to optimize a process, or partner with business stakeholders to align the UX strategy with business goals.  

3. Critical Reflection Action Research

Critical reflection action research aims to challenge dominant power structures and social injustices within a particular context. It emphasizes the importance of where designers and design teams reflect on the underlying assumptions and values that drive research and decision-making processes. In UX design, this could be where designers question the design biases, challenge the stereotypes, and promote inclusivity and diversity in design decisions.  

What are the Benefits and Challenges of Action Research?

Like any UX research method or approach, action research comes with its own set of benefits and challenges.  

Benefits of Action Research

Real-world solutions.

Action research focuses on solving real-world problems. This quality makes it highly relevant and practical. It allows UX designers to create solutions that are not just theoretically sound but also valid in real-world contexts.  

User Involvement

Action research involves users in the research process, which lets designers gather first-hand insights into users' needs, preferences and behaviors. This not only enhances the accuracy and reliability of the research but also fosters user engagement and ownership long before user testing of high-fidelity prototypes.  

Continuous Learning

The iterative nature of action research promotes continuous learning and improvement. It enables designers to adapt their designs based on users' feedback and learn from their successes and failures. They can fine-tune better tools and deliverables, such as more accurate user personas, from their findings.

Author and Human-Computer Interaction Expert, Professor Alan Dix explains personas and why they are important: 

Challenges of Action Research

Time- and resource-intensive.

Action research involves multiple iterations of planning, acting, observing and reflecting, which can be time- and resource-intensive. 

Complexity of Real-world Contexts

It can be difficult to implement changes and observe their effects in real-world contexts. This is due to the complexity and unpredictability of real-world situations.  

Risk of Subjectivity

Since action research involves close collaboration with stakeholders, there's a risk of subjectivity and bias influencing the research outcomes. It's crucial for designers to maintain objectivity and integrity throughout the research process. 

Ethical Considerations

It can be a challenge to ensure all participants understand the nature of the research and agree to participate willingly. Also, it’s vital to safeguard the privacy of participants and sensitive data.  

Scope Creep

The iterative nature of action research might lead to expanding goals, and make the project unwieldy.  

Generalizability

The contextual focus of action research may limit the extent to which designers can generalize findings from field studies to other settings.  

Best Practices and Tips for Successful Action Research

1. define clear objectives.

To begin, designers should define clear objectives. They should ask the following: 

What is the problem to try to solve? 

What change is desirable as an outcome?  

To have clear objectives will guide their research process and help them stay focused.  

2. Involve Users

It’s vital to involve users in the research process. Designers should collaborate with them to identify issues, co-create solutions and implement changes in real time. This will not only enhance the relevance of the research but also foster user engagement and ownership.  

3. Use a Variety of Data Collection Methods

To conduct action research means to observe the effects of changes in real-world contexts. This requires a variety of data collection methods. Designers should use methods like surveys, user interviews, observations and usability tests to gather diverse and comprehensive data. 

UX Strategist and Consultant, William Hudson explains the value of usability testing in this video: 

4. Reflect and Learn

Action research is all about learning from action. Designers should reflect on the outcomes of their actions, analyze the effectiveness of their solutions and draw insights. They can use these insights to inform their future actions and continuously improve the design.  

5. Communicate and Share Findings

Lastly, designers should communicate and share their findings with all stakeholders. This not only fosters transparency and trust but also facilitates collective learning and improvement.  

What are Other Considerations to Bear in Mind with Action Research?

Quantitative data.

Action research involves both qualitative and quantitative data, but it's important to remember to place emphasis on qualitative data. While quantitative data can provide useful insights, designers who rely too heavily on it may find a less holistic view of the user experience. 

Professor Alan Dix explains the difference between quantitative and qualitative data in this video: 

User Needs and Preferences

Designers should focus action research on understanding user needs and preferences. If they ignore these in favor of more technical considerations, the resulting design solutions may not meet users' expectations or provide them with a satisfactory experience.  

User Feedback

It's important to seek user feedback at each stage of the action research process. Without this feedback, designers may not optimize design solutions for user needs. For example, they may find the information architecture confusing. Additionally, without user feedback, it can be difficult to identify any unexpected problems that may arise during the research process.  

Time Allocation

Action research requires time and effort to ensure successful outcomes. If designers or design teams don’t permit enough time for the research process, it can lead to rushed decisions and sloppy results. It's crucial to plan ahead and set aside enough time for each stage of the action research process—and ensure that stakeholders understand the time-consuming nature of research and digesting research findings, and don’t push for premature results. 

Contextual Factors

Contextual factors such as culture, environment and demographics play an important role in UX design. If designers ignore these factors, it can lead to ineffective design solutions that don't properly address users' needs and preferences or consider their context.  

Professor Alan Dix explains the need to consider users’ culture in design, in this video: 

Copyright holder: Tommi Vainikainen _ Appearance time: 2:56 - 3:03 Copyright license and terms: Public domain, via Wikimedia Commons

Copyright holder: Maik Meid _ Appearance time: 2:56 - 3:03 Copyright license and terms: CC BY 2.0, via Wikimedia Commons _ Link: https://commons.wikimedia.org/wiki/File:Norge_93.jpg

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Overall, in the ever-evolving field of UX design, this is one methodology that can serve as a powerful research tool for driving positive change and promoting continuous learning. Since to do action research means to actively involve users in the research process and research projects, and focus on real-world problem-solving, it allows designers to create more user-centered designs. These digital solutions and services will be more likely to resonate with the target users and deliver exceptional user experiences.  

Despite its challenges, the benefits of action research far outweigh the risks. Action research is therefore a valuable approach for UX designers who are keen on creating a wide range of impactful and sustainable design solutions. The biggest lesson with action research is to ensure that user needs and preferences are at the center of the research process. 

Learn More about Action Research  

Take our User Research: Methods and Best Practices course.  

Take our Master Class Radical Participatory Design: Insights From NASA’s Service Design Lead with Victor Udoewa, Service Design Lead, NASA SBIR/STTR Program. 

Read more in-depth information in 3 things design thinking can learn from action research by Amin Mojtahedi, PhD . 

Find additional insights in What Technical Communicators and UX Designers Can Learn From Participatory Action Research by Guiseppe . 

Discover more insights and tips in Action Research: Steps, Benefits, and Tips by Lauren Stewart .

Questions related to Action Research

Action research and design thinking are both methodologies to solve problems and implement changes, but they have different approaches and emphases. Here's how they differ:  

Objectives  

Action research aims to solve specific problems within a community or organization through a cycle of planning, action, observation and reflection. It focuses on iterative learning and solving real-world problems through direct intervention.  

Design thinking focuses on addressing complex problems by understanding the user's needs, re-framing the problem in human-centric ways, creating many ideas in brainstorming sessions, and adopting a hands-on approach in prototyping and testing. It emphasizes innovation and the creation of solutions that are desirable, feasible and viable.  

Process  

Action research involves a cyclic process that includes:  

- Identify a problem.  

- Plan an action.  

- Implement the action.  

- Observe and evaluate the outcomes.  

- Reflect on the findings and plan the next cycle. 

Design thinking follows a non-linear, iterative process that typically includes five phases:  

- Empathize: Understand the needs of those you're designing for.  

- Define: Clearly articulate the problem you want to solve.  

- Ideate: Brainstorm a range of creative solutions.  

- Prototype: Build a representation of one or more of your ideas.  

- Test: Return to your original user group and test your idea for feedback.  

User Involvement  

Action research actively involves participants in the research process. The participants are co-researchers and have a direct stake in the problem at hand.  

Design thinking prioritizes empathy with users and stakeholders to ensure that the solutions are truly user-centered. While users are involved, especially in the empathy and testing phases, they may not be as deeply engaged in the entire process as they are in action research.  

Outcome  

Action research typically aims for practical outcomes that directly improve practices or address issues within the specific context studied. Its success is measurable by the extent of problem resolution or improvement.  

Design thinking seeks to generate innovative solutions that may not only solve the identified problem but also provide a basis for new products, services or ways of thinking. The success is often measurable in terms of innovation, user satisfaction and feasibility of implementation.  

In summary, while both action research and design thinking are valuable in addressing problems, action research is more about participatory problem-solving within specific contexts, and design thinking is about innovative solution-finding with a strong emphasis on user needs. 

Take our Design Thinking: The Ultimate Guide course. 

To define the research question in an action research project, start by identifying a specific problem or area of interest in your practice or work setting. Reflect on this issue deeply to understand its nuances and implications. Then, narrow your focus to a question that is both actionable and researchable. This question should aim to explore ways to improve, change or understand the problem better. Ensure the question is clear, concise and aligned with the goals of your project. It must invite inquiry and suggest a path towards finding practical solutions or gaining deeper insights. 

For instance, if you notice a decline in user engagement with a product, your research question could be, "How can we modify the user interface of our product to enhance user engagement?" This question clearly targets an improvement, focuses on a specific aspect (the user interface) and implies actionable outcomes (modifications to enhance engagement). 

Take our Master Class Radical Participatory Design: Insights From NASA’s Service Design Lead with Victor Udoewa, Service Design Lead, NASA SBIR/STTR Program.  

Designers use several tools and methods in action research to explore problems and implement solutions. Surveys allow them to gather feedback from a broad audience quickly. Interviews offer deep insights through personal conversations, focusing on users' experiences and needs. Observations help designers understand how people interact with products or services in real environments. Prototyping enables the testing of ideas and concepts through tangible models, and allows for immediate feedback and iteration. Finally, case studies provide detailed analysis of specific instances and offer valuable lessons and insights. 

These tools and methods empower designers to collect data, analyze findings and make informed decisions. When designers employ a combination of these approaches, they ensure a comprehensive understanding of the issues at hand and develop effective solutions. 

CEO of Experience Dynamics, Frank Spillers explains the need to be clear about the problem that designers should address: 

To engage stakeholders in an action research project, first identify all individuals or groups with an interest in the project's outcome. These might include users, team members, clients or community representatives. Clearly communicate the goals, benefits and expected outcomes of the project to them. Use presentations, reports, or informal meetings to share your vision and how their involvement adds value. 

Involve stakeholders early and often by soliciting their feedback through surveys, interviews or workshops. This inclusion not only provides valuable insights but also fosters a sense of ownership and commitment to the project. Establish regular update meetings or newsletters to keep stakeholders informed about progress, challenges and successes. Finally, ensure there are clear channels for stakeholders to share their input and concerns throughout the project. 

This approach creates a collaborative environment where stakeholders feel valued and engaged, leading to more meaningful and impactful outcomes. 

To measure the impact of an action research project, start by defining clear, measurable objectives at the beginning. These objectives should align with the goals of your project and provide a baseline against which you can measure progress. Use quantitative metrics such as increased user engagement, sales growth or improved performance scores for a tangible assessment of impact. Incorporate qualitative data as well, such as user feedback and case studies, to understand the subjective experiences and insights gained through the project. 

Conduct surveys or interviews before and after the project to compare results and identify changes. Analyze this data to assess how well the project met its objectives and what effect it had on the target issue or audience. Document lessons learned and unexpected outcomes to provide a comprehensive view of the project's impact. This approach ensures a holistic evaluation, combining numerical data and personal insights to gauge the success and influence of your action research project effectively. 

Take our Master Class Design KPIs: From Insights to Impact with Vitaly Friedman, Senior UX consultant, European Parliament, and Creative Lead, Smashing Magazine. 

When unexpected results or obstacles emerge during action research, first, take a step back and assess the situation. Identify the nature of the unexpected outcome or obstacle and analyze its potential impact on your project. This step is crucial for understanding the issue at hand. 

Next, communicate with your team and stakeholders about the situation. Open communication ensures everyone understands the issue and can contribute to finding a solution. 

Then, consider adjusting your research plan or design strategy to accommodate the new findings or to overcome the obstacles. This might involve revisiting your research questions, methods or even the design problem you are addressing. 

Always document these changes and the reasons behind them. This documentation will be valuable for understanding the project's evolution and for future reference. 

Finally, view these challenges as learning opportunities. Unexpected results can lead to new insights and innovations that strengthen your project in the long run. 

By remaining flexible, communicating effectively, and being willing to adjust your approach, you can navigate the uncertainties of action research and continue making progress towards your goals. 

Professor Alan Dix explains externalization, a creative process that can help designers to adapt to unexpected roadblocks and find a good way forward: 

Action research can significantly contribute to inclusive and accessible design by directly involving users with diverse needs in the research and design process. When designers engage individuals from various backgrounds, abilities and experiences, they can gain a deeper understanding of the wide range of user requirements and preferences. This approach ensures that the products or services they develop cater to a broader audience, including those with disabilities. 

Furthermore, action research allows for iterative testing and feedback loops with users. This quality enables designers to identify and address accessibility challenges early in the design process. The continuous engagement helps in refining designs to be more user-friendly and inclusive. 

Additionally, action research fosters a culture of empathy and understanding within design teams, as it emphasizes the importance of seeing the world from the users' perspectives. This empathetic approach leads to more thoughtful and inclusive design decisions, ultimately resulting in products and services that are accessible to everyone. 

By prioritizing inclusivity and accessibility through action research, designers can create more equitable and accessible solutions that enhance the user experience for all. 

Take our Master Class How to Design for Neurodiversity: Inclusive Content and UX with Katrin Suetterlin, UX Content Strategist, Architect and Consultant. 

To ensure the reliability and validity of data in action research, follow these steps: 

Define clear research questions: Start with specific, clear research questions to guide your data collection. This clarity helps in gathering relevant and focused data. 

Use multiple data sources: Collect data from various sources to cross-verify information. This triangulation strengthens the reliability of your findings. 

Apply consistent methods: Use consistent data collection methods throughout your research. If conducting surveys or interviews, keep questions consistent across participants to ensure comparability. 

Engage in peer review: Have peers or experts review your research design and data analysis. Feedback can help identify biases or errors, and enhance the validity of your findings. 

Document the process: Keep detailed records of your research process, including how you collected and analyzed data. Documentation allows others to understand and validate your research methodology. 

Test and refine instruments: If you’re using surveys or assessment tools, test them for reliability and validity before using them extensively. Pilot testing helps refine these instruments, and ensures they accurately measure what they intend to. 

When you adhere to these principles, you can enhance the reliability and validity of your action research data, leading to more trustworthy and impactful outcomes. 

Take our Data-Driven Design: Quantitative Research for UX course.  

To analyze data collected during an action research project, follow these steps: 

Organize the data: Begin by organizing your data, categorizing information based on types, sources or research questions. This organization makes the data manageable and prepares you for in-depth analysis. 

Identify patterns and themes: Look for patterns, trends and themes within your data. This might mean to code qualitative data or use statistical tools for quantitative data to uncover recurring elements or significant findings. 

Compare findings to objectives: Match your findings against the research objectives. Assess how the data answers your research questions or addresses the issues you set out to explore. 

Use software tools: Consider using data analysis software, especially for complex or large data sets. Tools like NVivo for qualitative data or SPSS for quantitative data can simplify analysis and help in identifying insights. 

Draw conclusions: Based on your analysis, draw conclusions about what the data reveals. Look for insights that answer your research questions or offer solutions to the problem you are investigating. 

Reflect and act: Reflect on the implications of your findings. Consider how they impact your understanding of the research problem and what actions they suggest for improvement or further investigation. 

This approach to data analysis ensures a thorough understanding of the collected data, allowing you to draw meaningful conclusions and make informed decisions based on your action research project. 

Professor Ann Blandford, Professor of Human-Computer Interaction, UCL explains valuable aspects of data collection in this video: 

Baskerville, R. L., & Wood-Harper, A. T. (1996). A critical perspective on action research as a method for information systems research . Journal of Information Technology, 11(3), 235-246.   

This influential paper examines the philosophical underpinnings of action research and its application in information systems research, which is closely related to UX design. It highlights the strengths of action research in addressing complex, real-world problems, as well as the challenges in maintaining rigor and achieving generalizability. The paper helped establish action research as a valuable methodology in the information systems and UX design fields.  

Di Mascio, T., & Tarantino, L. (2015). New Design Techniques for New Users: An Action Research-Based Approach . In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct (pp. 83-96). ACM. 

This paper describes an action research project that aimed to develop a novel data gathering technique for understanding the context of use of a technology-enhanced learning system for children. The authors argue that traditional laboratory experiments struggle to maintain relevance to the real world, and that action research, with its focus on solving practical problems, is better suited to addressing the needs of new ICT products and their users. The paper provides insights into the action research process and reflects on its value in defining new methods for solving complex, real-world problems. The work is influential in demonstrating the applicability of action research in the field of user experience design, particularly for designing for new and underserved user groups. 

Villari, B. (2014). Action research approach in design research . In Proceedings of the 5th STS Italia Conference A Matter of Design: Making Society through Science and Technology (pp. 306-316). STS Italia Publishing.  

This paper explores the application of action research in the field of design research. The author argues that design is a complex practice that requires interdisciplinary skills and the ability to engage with diverse communities. Action research is presented as a research strategy that can effectively merge theory and practice, linking the reflective dimension to practical activities. The key features of action research highlighted in the paper are its context-dependent nature, the close relationship between researchers and the communities involved, and the iterative process of examining one's own practice and using research insights to inform future actions. The paper is influential in demonstrating the value of action research in addressing the challenges of design research, particularly in terms of bridging the gap between theory and practice and fostering collaborative, user-centered approaches to design.  

Brandt, E. (2004). Action research in user-centred product development . AI & Society, 18(2), 113-133.  

This paper reports on the use of action research to introduce new user-centered work practices in two commercial product development projects. The author argues that the growing complexity of products and the increasing importance of quality, usability, and customization demand new collaborative approaches that involve customers and users directly in the development process. The paper highlights the value of using action research to support these new ways of working, particularly in terms of creating and reifying design insights in representations that can foster collaboration and continuity throughout the project. The work is influential in demonstrating the applicability of action research in the context of user-centered product development, where the need to bridge theory and practice and engage diverse stakeholders is paramount. The paper provides valuable insights into the practical challenges and benefits of adopting action research in this domain. 

1. Reason, P., & Bradbury, H. (Eds.). (2001). Handbook of action research: Participative inquiry and practice . SAGE Publications.  

This comprehensive handbook is considered a seminal work in the field of action research. It provides a thorough overview of the history, philosophical foundations, and diverse approaches to action research. The book features contributions from leading scholars and practitioners, covering topics such as participatory inquiry, critical action research, and the role of action research in organizational change and community development. It has been highly influential in establishing action research as a rigorous and impactful research methodology across various disciplines. 

 2. Stringer, E. T. (2013). Action Research (4th ed.) . SAGE Publications.  

This book by Ernest T. Stringer is a widely recognized and accessible guide to conducting action research. It provides clear, step-by-step instructions on the action research process, including gathering information, interpreting and explaining findings, and taking action to address practical problems. The book is particularly valuable for novice researchers and practitioners in fields such as education, social work, and community development, where action research is commonly applied. Its practical approach and real-life examples have made it a go-to resource for those seeking to engage in collaborative, solution-oriented research. 

3. McNiff, J. (2017). Action Research: All You Need to Know (1st ed.) . SAGE Publications.   

This book by Jean McNiff provides a comprehensive guide to conducting action research projects. It covers the key steps of the action research process, including identifying a problem, developing an action plan, implementing changes, and reflecting on the outcomes. The book is influential in the field of action research as it offers practical advice and strategies for practitioners across various disciplines, such as education, healthcare, and organizational development. It emphasizes the importance of critical reflection, collaboration, and the integration of theory and practice, making it a valuable resource for those seeking to engage in rigorous, transformative research. 

Answer a Short Quiz to Earn a Gift

What is a primary characteristic of action research in UX design?

• It drives practical changes through iterative cycles.
• It focuses solely on theoretical knowledge.
• It relies on external consultants to dictate changes.

Which type of action research improves system efficiency and effectiveness?

• Collaborative Action Research
• Critical Reflection Action Research
• Technical Action Research

What role do stakeholders play in collaborative action research?

• They participate actively in co-creating solutions.
• They provide financial support only.
• They review and approve final designs.

How do users in action research benefit the design process?

• They help make sure designs meet actual user needs and preferences.
• They help speed up the design process significantly.
• They limit the scope of design innovations.

What is the purpose of the reflection stage in the action research process?

• To document the research process for publication only
• To evaluate the effectiveness of actions and plan further improvements
• To finalize the product design without further changes

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Literature on Action Research

Here’s the entire UX literature on Action Research by the Interaction Design Foundation, collated in one place:

Learn more about Action Research

Take a deep dive into Action Research with our course User Research – Methods and Best Practices .

How do you plan to design a product or service that your users will love , if you don't know what they want in the first place? As a user experience designer, you shouldn't leave it to chance to design something outstanding; you should make the effort to understand your users and build on that knowledge from the outset. User research is the way to do this, and it can therefore be thought of as the largest part of user experience design .

In fact, user research is often the first step of a UX design process—after all, you cannot begin to design a product or service without first understanding what your users want! As you gain the skills required, and learn about the best practices in user research, you’ll get first-hand knowledge of your users and be able to design the optimal product—one that’s truly relevant for your users and, subsequently, outperforms your competitors’ .

This course will give you insights into the most essential qualitative research methods around and will teach you how to put them into practice in your design work. You’ll also have the opportunity to embark on three practical projects where you can apply what you’ve learned to carry out user research in the real world . You’ll learn details about how to plan user research projects and fit them into your own work processes in a way that maximizes the impact your research can have on your designs. On top of that, you’ll gain practice with different methods that will help you analyze the results of your research and communicate your findings to your clients and stakeholders—workshops, user journeys and personas, just to name a few!

By the end of the course, you’ll have not only a Course Certificate but also three case studies to add to your portfolio. And remember, a portfolio with engaging case studies is invaluable if you are looking to break into a career in UX design or user research!

We believe you should learn from the best, so we’ve gathered a team of experts to help teach this course alongside our own course instructors. That means you’ll meet a new instructor in each of the lessons on research methods who is an expert in their field—we hope you enjoy what they have in store for you!

All open-source articles on Action Research

An Introduction to Action Research

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Action Research

• Reference work entry
• First Online: 01 January 2023
• Cite this reference work entry

• David Coghlan 2  

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Action research is an approach to research which aims at both taking action and creating knowledge or theory about that action as the action unfolds. It starts with everyday experience and is concerned with the development of living knowledge. Its characteristics are that it generates practical knowledge in the pursuit of worthwhile purposes; it is participative and democratic as its participants work together in the present tense in defining the questions they wish to explore, the methodology for that exploration, and its application through cycles of action and reflection. In this vein they are agents of change and coresearchers in knowledge generation and not merely passive subjects as in traditional research. In this vein, action research can be understood as a social science of the possible as the collective action is focused on creating a desired future in whatever context the action research is located.

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Action Research As an Ethics Praxis Method

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Heron, J., & Reason, P. (2008). Extending epistemology within a cooperative inquiry. In P. Reason & H. Bradbury (Eds.), The Sage handbook of action research (2nd ed., pp. 366–380). London: Sage.

Huxham, C. (2003). Actionresearch as a methodology for theory development. Policy and Politics, 31 (2), 239–248. https://doi.org/10.1332/030557303765371726 .

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Coghlan, D. (2022). Action Research. In: Glăveanu, V.P. (eds) The Palgrave Encyclopedia of the Possible. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-90913-0_180

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Action Research

Action research can be defined as “an approach in which the action researcher and a client collaborate in the diagnosis of the problem and in the development of a solution based on the diagnosis” [1] . In other words, one of the main characteristic traits of action research relates to collaboration between researcher and member of organisation in order to solve organizational problems.

Action study assumes social world to be constantly changing, both, researcher and research being one part of that change. [2] Generally, action researches can be divided into three categories: positivist, interpretive and critical.

Positivist approach to action research , also known as ‘classical action research’ perceives research as a social experiment. Accordingly, action research is accepted as a method to test hypotheses in a real world environment.

Interpretive action research , also known as ‘contemporary action research’ perceives business reality as socially constructed and focuses on specifications of local and organisational factors when conducting the action research.

Critical action research is a specific type of action research that adopts critical approach towards business processes and aims for improvements.

The following features of action research need to be taken into account when considering its suitability for any given study:

• It is applied in order to improve specific practices.  Action research is based on action, evaluation and critical analysis of practices based on collected data in order to introduce improvements in relevant practices.
• This type of research is facilitated by participation and collaboration of number of individuals with a common purpose
• Such a research focuses on specific situations and their context

Advantages of Action Research

• High level of practical relevance of the business research;
• Can be used with quantitative, as well as, qualitative data;
• Possibility to gain in-depth knowledge about the problem.

Disadvantages of Action Research

• Difficulties in distinguishing between action and research and ensure the application of both;
• Delays in completion of action research due to a wide range of reasons are not rare occurrences
• Lack of repeatability and rigour

It is important to make a clear distinction between action research and consulting. Specifically, action research is greater than consulting in a way that action research includes both action and research, whereas business activities of consulting are limited action without the research.

Action Research Spiral

Action study is a participatory study consisting of spiral of following self-reflective cycles:

• Planning in order to initiate change
• Implementing the change (acting) and observing the process of implementation and consequences
• Reflecting on processes of change and re-planning
• Acting and observing

Kemmis and McTaggart (2000) do acknowledge that individual stages specified in Action Research Spiral model may overlap, and initial plan developed for the research may become obselete in short duration of time due to a range of factors.

The main advantage of Action Research Spiral model relates to the opportunity of analysing the phenomenon in a greater depth each time, consequently resulting in grater level of understanding of the problem.

Disadvantages of Action Research Spiral model include its assumption each process takes long time to be completed which may not always be the case.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  offers practical assistance to complete a dissertation with minimum or no stress. The e-book covers all stages of writing a dissertation starting from the selection to the research area to submitting the completed version of the work within the deadline.

References 

[1] Bryman, A. & Bell, E. (2011) “Business Research Methods” 3 rd  edition, Oxford University Press

[2] Collis, J. & Hussey, R. (2003) “Business Research. A Practical Guide for Undergraduate and Graduate Students” 2nd edition, Palgrave Macmillan

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Action research: what, why and how?

Affiliation.

• 1 Kempenhaeghe, Heeze, the Netherlands. [email protected]
• PMID: 19681439

Action research is a form of research that enables practitioners to investigate and evaluate their own work. It is increasingly used in health care research; it is a research strategy in which the researcher and practitioners from the setting under study work together in projects aimed at generating new knowledge and simultaneously improving practice. This article gives an overview of the theoretical background of action research, its international historical development and explanations of its varied forms and related practical applications. Ethical problems are discussed as are questions of rigour The article shows that action research can be used to bridge the gap between theory and practice by generating knowledge fitting the particular circumstances in the practical setting, thereby avoiding problems of implementation of research findings due to lack of fit or lack of motivation. Action research lastingly increases the capacities of practitioners to solve problems encountered in practice.

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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 24 June 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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Participatory action research

This glossary aims to clarify some of the key concepts associated with participatory action research.

Participatory action research (PAR) differs from most other approaches to public health research because it is based on reflection, data collection, and action that aims to improve health and reduce health inequities through involving the people who, in turn, take actions to improve their own health.

PAR has a number of antecedents. 1 It reflects questioning about the nature of knowledge and the extent to which knowledge can represent the interests of the powerful and serve to reinforce their positions in society. 2 It affirms that experience can be a basis of knowing and that experiential learning can lead to a legitimate form of knowledge that influences practice. 3 Adult educators in low income countries drew on these intellectual perspectives to develop a form of research that was sympathetic to the participatory nature of adult learning. This perspective was strongly supported by the work of Freire, 4 who used PAR to encourage poor and deprived communities to examine and analyse the structural reasons for their oppression. From these roots PAR grew as a methodology enabling researchers to work in partnership with communities in a manner that leads to action for change.

Definition of PAR

PAR seeks to understand and improve the world by changing it. At its heart is collective, self reflective inquiry that researchers and participants undertake, so they can understand and improve upon the practices in which they participate and the situations in which they find themselves. The reflective process is directly linked to action, influenced by understanding of history, culture, and local context and embedded in social relationships. The process of PAR should be empowering and lead to people having increased control over their lives (adapted from Minkler and Wallerstein 5 and Grbich 6 ).

The distinctiveness of PAR

PAR differs from conventional research in three ways. Firstly, it focuses on research whose purpose is to enable action. Action is achieved through a reflective cycle, whereby participants collect and analyse data, then determine what action should follow. The resultant action is then further researched and an iterative reflective cycle perpetuates data collection, reflection, and action as in a corkscrew action. Secondly, PAR pays careful attention to power relationships, advocating for power to be deliberately shared between the researcher and the researched: blurring the line between them until the researched become the researchers. The researched cease to be objects and become partners in the whole research process: including selecting the research topic, data collection, and analysis and deciding what action should happen as a result of the research findings. Wadsworth 7 sees PAR as an expression of “new paradigm science” that differs significantly from old paradigm or positivist science. The hallmark of positivist science is that it sees the world as having a single reality that can be independently observed and measured by objective scientists preferably under laboratory conditions where all variables can be controlled and manipulated to determine causal connections. By contrast new paradigm science and PAR posits that the observer has an impact on the phenomena being observed and brings to their inquiry a set of values that will exert influence on the study. Thirdly, PAR contrasts with less dynamic approaches that remove data and information from their contexts. Most health research involves people, even if only as passive participants, as “subjects” or “respondents”. PAR advocates that those being researched should be involved in the process actively. The degree to which this is possible in health research will differ as will the willingness of people to be involved in research

Methodology/method

Research methodology is a strategy or plan of action that shapes our choice and use of methods and links them to the desired outcomes. 8 In contrast with a decade ago, when epidemiological methods were regarded as the only gold standard in public health research, many authors agree 9 , 9a , 9b that effective public health research requires methodological pluralism. PAR draws on the paradigms of critical theory and constructivism and may use a range of qualitative and quantitative methods. For instance a participatory needs assessment would include extensive engagement with local communities and may also include a survey of residents who are less centrally engaged in the participatory process. 10

Application of PAR to health

In the 21st century PAR is increasingly used in health research. By contrast, in the 1980s and in earlier decades, very little research using PAR was reported in health journals. Through the 1990s more participatory research was reported and textbooks including PAR became more common. 11 , 11a An example of this interest is the special edition of the Journal of Interprofessional Care , with an editorial and 16 articles reporting on PAR. 12 Initially PAR was mainly used in low income countries for needs assessment (see for example De Kroning and Martin 13 ) and planning and evaluating health services (for examples see collection in Minkler and Wallerstein 14 ). The work by Howard‐Grabman 15 provides a typical description of developing a community plan to tackle maternal and neonatal health problems in rural Bolivia. The project built on and strengthened existing women's networks and the staff played the part of facilitators rather than educators. A community action cycle was developed whereby problems were identified and prioritised, joint planning took place, and the plan was implemented and then evaluated in a participatory way. The project developed innovative and engaging ways for staff and community members to work together effectively.

Recently PAR has been used more frequently in rich countries. In mental health research, for instance, PAR has been used in response to the survivor's movement and demands for a voice in planning and running services and to stimulate choices and alternative forms of treatment. 16 PAR principles also form the basis of “empowerment evaluation” 17 that argue that the evaluation of health promotion should include those whose health is being promoted. 18 While there has been some debate about the distinctiveness of empowerment evaluation 19 it certainly strives to be more democratic, to build capacity, to encourage self determination and make evaluation less expert driven.

PAR is increasingly recognised as useful in Indigenous health research, both internationally 20 , 21 and in Australia. 22 , 23 , 24 It has the potential to reduce the negative—and some would argue colonising—effects much conventional research has had on Indigenous people. It does this by avoiding some of the criticisms made of health research including: (1) Indigenous people being exploited and treated disrespectfully, (2) research processes that see non‐Indigenous researchers and research bodies retain all the power and control, (3) the lack of specified short and long term benefits to Indigenous communities and persons, and (4) the misrepresentation of Indigenous societies, cultures, and persons by non‐Indigenous academics and professionals. 25 , 26 , 27

An example of the application of PAR in a remote Aboriginal Australian community is the work to support a men's self help group to plan, implement, and evaluate their activities. 28 With support from the research team community members are acting as researchers exploring priority issues affecting their lives, recognising their resources, producing knowledge, and taking action to improve their situation. The ongoing PAR process of reflection and action, which incorporates participant observation, informal discussions, in‐depth interviews, and a “feedback box”, is viewed by the participants as contributing to their self reported increased sense of self awareness, self confidence, and hope for the future.

For academics, dilemmas arise in the use of PAR because it is time consuming and unpredictable, unlikely to lead to a high production of articles in refereed journals and its somewhat “messy” nature means it is less likely to attract competitive research funding. 29 Acceptance of PAR as a legitimate research methodology will require change from public health journals, funding bodies, and universities in the way that they judge research performance. For instance most public health academic units assess their academic researchers' suitability for promotion according to the number of peer reviewed journal articles. The ability of a researcher to engage with communities and bring about real change to their quality of life and health status rarely counts. The global research community is already being urged to adapt its grant assessment methods and its assessment of research performance to ensure that the engaged processes typical of PAR are valued and encouraged. 30

PAR also requires health researchers to work in close partnership with civil society and health policy makers and practitioners. This requires each of these players to learn methods of working together effectively and to manage the different and sometimes competing agendas of the partners. The focus of the research partners should also be on health improvement for the community involved. 31

Participation

Participation has been central to improving health since the WHO Health for All Strategy and its importance to health promotion strategies has been reinforced by subsequent statements on health promotion. 32 Participation has been seen as a means to overcome professional dominance, to improve strategies (whether they are for practice or research), and to show a commitment to democratic principles. In the 1970s debate on development emphasised that development should no longer be a top‐down process but should emphasise participation of those whose development was being attempted. 33 PAR came to be used in many development projects as a mechanism through which to put the rhetoric of participation into action. Associated methods are rapid assessment methods and rapid rural appraisal both of which aim to produce knowledge that combines professional and community perspectives.

Power/empowerment

Power is a crucial underpinning concept to PAR. PAR aims to achieve empowerment of those involved. Labonte 34 conceptualises empowerment as a shifting or dynamic quality of power relations between two or more people; such that the relationship tends towards equity by reducing inequalities and power differences in access to resources. Power itself is an elusive concept about which there has been considerable discussion. Foucault's position is particularly relevant to PAR because he sees power as something that results from the interactions between people, from the practices of institutions, and from the exercise of different forms of knowledge. 35 His work on discipline and control shows that disciplinary power functions through surveillance and internal discipline of people to achieve their subjugations and “docility”. 36 The PAR movement challenges the system of surveillance and knowledge control established through mainstream research. When communities seek control of research agendas, and seek to be active in research, they are establishing themselves as more powerful agents. In health services and public health initiatives in recent years community members and consumers have gained more power over the practices of institutions and the production of knowledge. Developments in participation have implications for health services and public health organisations that, if they are to be true to the principles of participation, must initiate organisational change to improve their capacity to work in partnership with a wide variety of communities. 37 , 37a

Many dilemmas of the PAR approach revolve around contested power dynamics in research relationships. Wallerstein detailed the power conflicts in research on New Mexico's Healthier Communities Initiatives and concluded that handling these requires “a painful self‐reflective process”. 38 These included differences in perceptions of priorities between researchers and community members, dealing with community politics in the different communities involved in the study and resolving different ways in which researchers and communities might interpret findings.

Lived experience

PAR stands in contrast with what Husserl (quoted in Crotty 39 ) describes as the mathematisation of the scientific world by Galileo, for whom the real properties of things were only those that could be measured, counted, and quantified. Husserl argued that the scientific world is an abstraction from the lived world, or the world we experience. This scientific world is systematic and well organised, unlike the uncertain, ambiguous, idiosyncratic world we know at first hand. 39 On the other hand, PAR draws on the work of phenomenologists who expand the breadth and importance of experience when they argue that humans cannot describe and object in isolation from the conscious being experiencing that object; just as an experience cannot be described in isolation from its object. Experiences are not from a sphere of subjective reality separate from an external, objective world. Rather they enable humans to engage with their world and unite subject and object. 40 One example of a use of lived experience is research using feminist theory, which refers to “women's ways of knowing or women's experience”. 41

Critical reflection and a critical edge

Crotty 42 argues that while interpretivists place confidence in the authentic accounts of lived experience that they turn up in their research, this is not enough for critical theorists who see in these accounts voices of an inherited tradition and prevailing culture. Critical theorists use critical reflection on social reality to take action for change by radically calling into question the cultures that they study. This critical edge is central to PAR.

Critical reflection on professional practice

PAR draws heavily on Paulo Freire's epistemology that rejects both the view that consciousness is a copy of external reality and the solipsist argument that the world is a creation of consciousness. For Freire, human consciousness brings a reflection on material reality, whereby critical reflection is already action. Freire's concept of praxis flows from the position that action and reflection are indissolubly united: “reflection and action on the world in order to transform it”. 43 It is from this position that Freire derives his famous dictum that reflection without action is sheer verbalism or armchair revolution and action without reflection is pure activism, or action for action's sake . 44 In the same vein, PAR sees that action and reflection must go together, even temporally so that praxis cannot be divided into a prior stage of reflection and a subsequent stage of action. When action and reflection take place at the same time they become creative and mutually illuminate each other. 45 Through praxis, critical consciousness develops, leading to further action through which people cease to see their situation as a “dense, enveloping reality or a blind alley” and instead as “an historical reality susceptible of transformation”. 46 This transformative power is central to PAR.

Acknowledgements

Thanks to our reviewers—Valery Ridde, Ruth Balogh, and one anonymous. Their comments have improved this glossary.

• Our Mission

How Teachers Can Learn Through Action Research

A look at one school’s action research project provides a blueprint for using this model of collaborative teacher learning.

When teachers redesign learning experiences to make school more relevant to students’ lives, they can’t ignore assessment. For many teachers, the most vexing question about real-world learning experiences such as project-based learning is: How will we know what students know and can do by the end of this project?

Teachers at the Siena School in Silver Spring, Maryland, decided to figure out the assessment question by investigating their classroom practices. As a result of their action research, they now have a much deeper understanding of authentic assessment and a renewed appreciation for the power of learning together.

Their research process offers a replicable model for other schools interested in designing their own immersive professional learning. The process began with a real-world challenge and an open-ended question, involved a deep dive into research, and ended with a public showcase of findings.

Start With an Authentic Need to Know

Siena School serves about 130 students in grades 4–12 who have mild to moderate language-based learning differences, including dyslexia. Most students are one to three grade levels behind in reading.

Teachers have introduced a variety of instructional strategies, including project-based learning, to better meet students’ learning needs and also help them develop skills like collaboration and creativity. Instead of taking tests and quizzes, students demonstrate what they know in a PBL unit by making products or generating solutions.

“We were already teaching this way,” explained Simon Kanter, Siena’s director of technology. “We needed a way to measure, was authentic assessment actually effective? Does it provide meaningful feedback? Can teachers grade it fairly?”

Focus the Research Question

Across grade levels and departments, teachers considered what they wanted to learn about authentic assessment, which the late Grant Wiggins described as engaging, multisensory, feedback-oriented, and grounded in real-world tasks. That’s a contrast to traditional tests and quizzes, which tend to focus on recall rather than application and have little in common with how experts go about their work in disciplines like math or history.

The teachers generated a big research question: Is using authentic assessment an effective and engaging way to provide meaningful feedback for teachers and students about growth and proficiency in a variety of learning objectives, including 21st-century skills?

Take Time to Plan

Next, teachers planned authentic assessments that would generate data for their study. For example, middle school science students created prototypes of genetically modified seeds and pitched their designs to a panel of potential investors. They had to not only understand the science of germination but also apply their knowledge and defend their thinking.

In other classes, teachers planned everything from mock trials to environmental stewardship projects to assess student learning and skill development. A shared rubric helped the teachers plan high-quality assessments.

Make Sense of Data

During the data-gathering phase, students were surveyed after each project about the value of authentic assessments versus more traditional tools like tests and quizzes. Teachers also reflected after each assessment.

“We collated the data, looked for trends, and presented them back to the faculty,” Kanter said.

Among the takeaways:

• Authentic assessment generates more meaningful feedback and more opportunities for students to apply it.
• Students consider authentic assessment more engaging, with increased opportunities to be creative, make choices, and collaborate.
• Teachers are thinking more critically about creating assessments that allow for differentiation and that are applicable to students’ everyday lives.

To make their learning public, Siena hosted a colloquium on authentic assessment for other schools in the region. The school also submitted its research as part of an accreditation process with the Middle States Association.

Strategies to Share

For other schools interested in conducting action research, Kanter highlighted three key strategies.

• Focus on areas of growth, not deficiency:  “This would have been less successful if we had said, ‘Our math scores are down. We need a new program to get scores up,’ Kanter said. “That puts the onus on teachers. Data collection could seem punitive. Instead, we focused on the way we already teach and thought about, how can we get more accurate feedback about how students are doing?”
• Foster a culture of inquiry:  Encourage teachers to ask questions, conduct individual research, and share what they learn with colleagues. “Sometimes, one person attends a summer workshop and then shares the highlights in a short presentation. That might just be a conversation, or it might be the start of a school-wide initiative,” Kanter explained. In fact, that’s exactly how the focus on authentic assessment began.
• Build structures for teacher collaboration:  Using staff meetings for shared planning and problem-solving fosters a collaborative culture. That was already in place when Siena embarked on its action research, along with informal brainstorming to support students.

For both students and staff, the deep dive into authentic assessment yielded “dramatic impact on the classroom,” Kanter added. “That’s the great part of this.”

In the past, he said, most teachers gave traditional final exams. To alleviate students’ test anxiety, teachers would support them with time for content review and strategies for study skills and test-taking.

“This year looks and feels different,” Kanter said. A week before the end of fall term, students were working hard on final products, but they weren’t cramming for exams. Teachers had time to give individual feedback to help students improve their work. “The whole climate feels way better.”

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4 Preparing for Action Research in the Classroom: Practical Issues

ESSENTIAL QUESTIONS

• What sort of considerations are necessary to take action in your educational context?
• How do you facilitate an action plan without disrupting your teaching?
• How do you respond when the unplanned happens during data collection?

An action research project is a practical endeavor that will ultimately be shaped by your educational context and practice. Now that you have developed a literature review, you are ready to revise your initial plans and begin to plan your project. This chapter will provide some advice about your considerations when undertaking an action research project in your classroom.

Maintain Focus

Hopefully, you found a lot a research on your topic. If so, you will now have a better understanding of how it fits into your area and field of educational research. Even though the topic and area you are researching may not be small, your study itself should clearly focus on one aspect of the topic in your classroom. It is important to maintain clarity about what you are investigating because a lot will be going on simultaneously during the research process and you do not want to spend precious time on erroneous aspects that are irrelevant to your research.

Even though you may view your practice as research, and vice versa, you might want to consider your research project as a projection or megaphone for your work that will bring attention to the small decisions that make a difference in your educational context. From experience, our concern is that you will find that researching one aspect of your practice will reveal other interconnected aspects that you may find interesting, and you will disorient yourself researching in a confluence of interests, commitments, and purposes. We simply want to emphasize – don’t try to research everything at once. Stay focused on your topic, and focus on exploring it in depth, instead of its many related aspects. Once you feel you have made progress in one aspect, you can then progress to other related areas, as new research projects that continue the research cycle.

Identify a Clear Research Question

Your literature review should have exposed you to an array of research questions related to your topic. More importantly, your review should have helped identify which research questions we have addressed as a field, and which ones still need to be addressed . More than likely your research questions will resemble ones from your literature review, while also being distinguishable based upon your own educational context and the unexplored areas of research on your topic.

Regardless of how your research question took shape, it is important to be clear about what you are researching in your educational context. Action research questions typically begin in ways related to “How does … ?” or “How do I/we … ?”, for example:

Research Question Examples

• How does a semi-structured morning meeting improve my classroom community?
• How does historical fiction help students think about people’s agency in the past?
• How do I improve student punctuation use through acting out sentences?
• How do we increase student responsibility for their own learning as a team of teachers?

I particularly favor questions with I or we, because they emphasize that you, the actor and researcher, will be clearly taking action to improve your practice. While this may seem rather easy, you need to be aware of asking the right kind of question. One issue is asking a too pointed and closed question that limits the possibility for analysis. These questions tend to rely on quantitative answers, or yes/no answers. For example, “How many students got a 90% or higher on the exam, after reviewing the material three times?

Another issue is asking a question that is too broad, or that considers too many variables. For example, “How does room temperature affect students’ time-on-task?” These are obviously researchable questions, but the aim is a cause-and-effect relationship between variables that has little or no value to your daily practice.

I also want to point out that your research question will potentially change as the research develops. If you consider the question:

As you do an activity, you may find that students are more comfortable and engaged by acting sentences out in small groups, instead of the whole class. Therefore, your question may shift to:

• How do I improve student punctuation use through acting out sentences, in small groups ?

By simply engaging in the research process and asking questions, you will open your thinking to new possibilities and you will develop new understandings about yourself and the problematic aspects of your educational context.

Understand Your Capabilities and Know that Change Happens Slowly

Similar to your research question, it is important to have a clear and realistic understanding of what is possible to research in your specific educational context. For example, would you be able to address unsatisfactory structures (policies and systems) within your educational context? Probably not immediately, but over time you potentially could. It is much more feasible to think of change happening in smaller increments, from within your own classroom or context, with you as one change agent. For example, you might find it particularly problematic that your school or district places a heavy emphasis on traditional grades, believing that these grades are often not reflective of the skills students have or have not mastered. Instead of attempting to research grading practices across your school or district, your research might instead focus on determining how to provide more meaningful feedback to students and parents about progress in your course. While this project identifies and addresses a structural issue that is part of your school and district context, to keep things manageable, your research project would focus the outcomes on your classroom. The more research you do related to the structure of your educational context the more likely modifications will emerge. The more you understand these modifications in relation to the structural issues you identify within your own context, the more you can influence others by sharing your work and enabling others to understand the modification and address structural issues within their contexts. Throughout your project, you might determine that modifying your grades to be standards-based is more effective than traditional grades, and in turn, that sharing your research outcomes with colleagues at an in-service presentation prompts many to adopt a similar model in their own classrooms. It can be defeating to expect the world to change immediately, but you can provide the spark that ignites coordinated changes. In this way, action research is a powerful methodology for enacting social change. Action research enables individuals to change their own lives, while linking communities of like-minded practitioners who work towards action.

Plan Thoughtfully

Planning thoughtfully involves having a path in mind, but not necessarily having specific objectives. Due to your experience with students and your educational context, the research process will often develop in ways as you expected, but at times it may develop a little differently, which may require you to shift the research focus and change your research question. I will suggest a couple methods to help facilitate this potential shift. First, you may want to develop criteria for gauging the effectiveness of your research process. You may need to refine and modify your criteria and your thinking as you go. For example, we often ask ourselves if action research is encouraging depth of analysis beyond my typical daily pedagogical reflection. You can think about this as you are developing data collection methods and even when you are collecting data. The key distinction is whether the data you will be collecting allows for nuance among the participants or variables. This does not mean that you will have nuance, but it should allow for the possibility. Second, criteria are shaped by our values and develop into standards of judgement. If we identify criteria such as teacher empowerment, then we will use that standard to think about the action contained in our research process. Our values inform our work; therefore, our work should be judged in relation to the relevance of our values in our pedagogy and practice.

Does Your Timeline Work?

While action research is situated in the temporal span that is your life, your research project is short-term, bounded, and related to the socially mediated practices within your educational context. The timeline is important for bounding, or setting limits to your research project, while also making sure you provide the right amount of time for the data to emerge from the process.

For example, if you are thinking about examining the use of math diaries in your classroom, you probably do not want to look at a whole semester of entries because that would be a lot of data, with entries related to a wide range of topics. This would create a huge data analysis endeavor. Therefore, you may want to look at entries from one chapter or unit of study. Also, in terms of timelines, you want to make sure participants have enough time to develop the data you collect. Using the same math example, you would probably want students to have plenty of time to write in the journals, and also space out the entries over the span of the chapter or unit.

In relation to the examples, we think it is an important mind shift to not think of research timelines in terms of deadlines. It is vitally important to provide time and space for the data to emerge from the participants. Therefore, it would be potentially counterproductive to rush a 50-minute data collection into 20 minutes – like all good educators, be flexible in the research process.

Involve Others

It is important to not isolate yourself when doing research. Many educators are already isolated when it comes to practice in their classroom. The research process should be an opportunity to engage with colleagues and open up your classroom to discuss issues that are potentially impacting your entire educational context. Think about the following relationships:

Research participants

You may invite a variety of individuals in your educational context, many with whom you are in a shared situation (e.g. colleagues, administrators). These participants may be part of a collaborative study, they may simply help you develop data collection instruments or intervention items, or they may help to analyze and make sense of the data. While the primary research focus will be you and your learning, you will also appreciate how your learning is potentially influencing the quality of others’ learning.

We always tell educators to be public about your research, or anything exciting that is happening in your educational context, for that matter. In terms of research, you do not want it to seem mysterious to any stakeholder in the educational context. Invite others to visit your setting and observe your research process, and then ask for their formal feedback. Inviting others to your classroom will engage and connect you with other stakeholders, while also showing that your research was established in an ethic of respect for multiple perspectives.

Critical friends or validators

Using critical friends is one way to involve colleagues and also validate your findings and conclusions. While your positionality will shape the research process and subsequently your interpretations of the data, it is important to make sure that others see similar logic in your process and conclusions. Critical friends or validators provide some level of certification that the frameworks you use to develop your research project and make sense of your data are appropriate for your educational context. Your critical friends and validators’ suggestions will be useful if you develop a report or share your findings, but most importantly will provide you confidence moving forward.

Potential researchers

As an educational researcher, you are involved in ongoing improvement plans and district or systemic change. The flexibility of action research allows it to be used in a variety of ways, and your initial research can spark others in your context to engage in research either individually for their own purposes, or collaboratively as a grade level, team, or school. Collaborative inquiry with other educators is an emerging form of professional learning and development for schools with school improvement plans. While they call it collaborative inquiry, these schools are often using an action research model. It is good to think of all of your colleagues as potential research collaborators in the future.

Prioritize Ethical Practice

Try to always be cognizant of your own positionality during the action research process, its relation to your educational context, and any associated power relation to your positionality. Furthermore, you want to make sure that you are not coercing or engaging participants into harmful practices. While this may seem obvious, you may not even realize you are harming your participants because you believe the action is necessary for the research process.

For example, commonly teachers want to try out an intervention that will potentially positively impact their students. When the teacher sets up the action research study, they may have a control group and an experimental group. There is potential to impair the learning of one of these groups if the intervention is either highly impactful or exceedingly worse than the typical instruction. Therefore, teachers can sometimes overlook the potential harm to students in pursuing an experimental method of exploring an intervention.

If you are working with a university researcher, ethical concerns will be covered by the Institutional Review Board (IRB). If not, your school or district may have a process or form that you would need to complete, so it would beneficial to check your district policies before starting. Other widely accepted aspects of doing ethically informed research, include:

Confirm Awareness of Study and Negotiate Access – with authorities, participants and parents, guardians, caregivers and supervisors (with IRB this is done with Informed Consent).

• Promise to Uphold Confidentiality – Uphold confidentiality, to your fullest ability, to protect information, identity and data. You can identify people if they indicate they want to be recognized for their contributions.
• Ensure participants’ rights to withdraw from the study at any point .
• Make sure data is secured, either on password protected computer or lock drawer .

Prepare to Problematize your Thinking

Educational researchers who are more philosophically-natured emphasize that research is not about finding solutions, but instead is about creating and asking new and more precise questions. This is represented in the action research process shown in the diagrams in Chapter 1, as Collingwood (1939) notes the aim in human interaction is always to keep the conversation open, while Edward Said (1997) emphasized that there is no end because whatever we consider an end is actually the beginning of something entirely new. These reflections have perspective in evaluating the quality in research and signifying what is “good” in “good pedagogy” and “good research”. If we consider that action research is about studying and reflecting on one’s learning and how that learning influences practice to improve it, there is nothing to stop your line of inquiry as long as you relate it to improving practice. This is why it is necessary to problematize and scrutinize our practices.

Ethical Dilemmas for Educator-Researchers

Classroom teachers are increasingly expected to demonstrate a disposition of reflection and inquiry into their own practice. Many advocate for schools to become research centers, and to produce their own research studies, which is an important advancement in acknowledging and addressing the complexity in today’s schools. When schools conduct their own research studies without outside involvement, they bypass outside controls over their studies. Schools shift power away from the oversight of outside experts and ethical research responsibilities are shifted to those conducting the formal research within their educational context. Ethics firmly grounded and established in school policies and procedures for teaching, becomes multifaceted when teaching practice and research occur simultaneously. When educators conduct research in their classrooms, are they doing so as teachers or as researchers, and if they are researchers, at what point does the teaching role change to research? Although the notion of objectivity is a key element in traditional research paradigms, educator-based research acknowledges a subjective perspective as the educator-researcher is not viewed separately from the research. In action research, unlike traditional research, the educator as researcher gains access to the research site by the nature of the work they are paid and expected to perform. The educator is never detached from the research and remains at the research site both before and after the study. Because studying one’s practice comprises working with other people, ethical deliberations are inevitable. Educator-researchers confront role conflict and ambiguity regarding ethical issues such as informed consent from participants, protecting subjects (students) from harm, and ensuring confidentiality. They must demonstrate a commitment toward fully understanding ethical dilemmas that present themselves within the unique set of circumstances of the educational context. Questions about research ethics can feel exceedingly complex and in specific situations, educator- researchers require guidance from others.

Think about it this way. As a part-time historian and former history teacher I often problematized who we regard as good and bad people in history. I (Clark) grew up minutes from Jesse James’ childhood farm. Jesse James is a well-documented thief, and possibly by today’s standards, a terrorist. He is famous for daylight bank robberies, as well as the sheer number of successful robberies. When Jesse James was assassinated, by a trusted associate none-the-less, his body travelled the country for people to see, while his assailant and assailant’s brother reenacted the assassination over 1,200 times in theaters across the country. Still today in my hometown, they reenact Jesse James’ daylight bank robbery each year at the Fall Festival, immortalizing this thief and terrorist from our past. This demonstrates how some people saw him as somewhat of hero, or champion of some sort of resistance, both historically and in the present. I find this curious and ripe for further inquiry, but primarily it is problematic for how we think about people as good or bad in the past. Whatever we may individually or collectively think about Jesse James as a “good” or “bad” person in history, it is vitally important to problematize our thinking about him. Talking about Jesse James may seem strange, but it is relevant to the field of action research. If we tell people that we are engaging in important and “good” actions, we should be prepared to justify why it is “good” and provide a theoretical, epistemological, or ontological rationale if possible. Experience is never enough, you need to justify why you act in certain ways and not others, and this includes thinking critically about your own thinking.

Educators who view inquiry and research as a facet of their professional identity must think critically about how to design and conduct research in educational settings to address respect, justice, and beneficence to minimize harm to participants. This chapter emphasized the due diligence involved in ethically planning the collection of data, and in considering the challenges faced by educator-researchers in educational contexts.

Planning Action

After the thinking about the considerations above, you are now at the stage of having selected a topic and reflected on different aspects of that topic. You have undertaken a literature review and have done some reading which has enriched your understanding of your topic. As a result of your reading and further thinking, you may have changed or fine-tuned the topic you are exploring. Now it is time for action. In the last section of this chapter, we will address some practical issues of carrying out action research, drawing on both personal experiences of supervising educator-researchers in different settings and from reading and hearing about action research projects carried out by other researchers.

Engaging in an action research can be a rewarding experience, but a beneficial action research project does not happen by accident – it requires careful planning, a flexible approach, and continuous educator-researcher reflection. Although action research does not have to go through a pre-determined set of steps, it is useful here for you to be aware of the progression which we presented in Chapter 2. The sequence of activities we suggested then could be looked on as a checklist for you to consider before planning the practical aspects of your project.

We also want to provide some questions for you to think about as you are about to begin.

• Have you identified a topic for study?
• What is the specific context for the study? (It may be a personal project for you or for a group of researchers of which you are a member.)
• Have you read a sufficient amount of the relevant literature?
• Have you developed your research question(s)?
• Have you assessed the resource needed to complete the research?

As you start your project, it is worth writing down:

• a working title for your project, which you may need to refine later;
• the background of the study , both in terms of your professional context and personal motivation;
• the aims of the project;
• the specific outcomes you are hoping for.

Although most of the models of action research presented in Chapter 1 suggest action taking place in some pre-defined order, they also allow us the possibility of refining our ideas and action in the light of our experiences and reflections. Changes may need to be made in response to your evaluation and your reflections on how the project is progressing. For example, you might have to make adjustments, taking into account the students’ responses, your observations and any observations of your colleagues. All this is very useful and, in fact, it is one of the features that makes action research suitable for educational research.

Action research planning sheet

In the past, we have provided action researchers with the following planning list that incorporates all of these considerations. Again, like we have said many times, this is in no way definitive, or lock-in-step procedure you need to follow, but instead guidance based on our perspective to help you engage in the action research process. The left column is the simplified version, and the right column offers more specific advice if need.

Figure 4.1 Planning Sheet for Action Research

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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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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Morgan Stanley Wealth Management Announces Latest Game-Changing Addition to Suite of GenAI Tools

AI @ Morgan Stanley Debrief acts as notetaker, summarizer and first draft communication composer for client meetings, greatly enhancing efficiency and enabling scale for Advisors and their practices

Morgan Stanley Wealth Management (MSWM) today announced the next innovation milestone in its AI @ Morgan Stanley suite of GenAI tools for Financial Advisors (FAs). The new AI @ Morgan Stanley Debrief is an OpenAI-powered tool that, with client consent, generates notes on a Financial Advisors’ behalf in client meetings and surfaces action items.

After the meeting, it summarizes key points, creates an email for an Advisor to edit and send at their discretion, and saves a note into Salesforce.

“We are thrilled to add yet another groundbreaking tool to our FA toolkit—further enhancing our industry-leading1 Advisor platform,” said Vince Lumia, Head of Morgan Stanley Wealth Management Client Segments. “AI @ Morgan Stanley Debrief drives immense efficiency in an Advisors’ day-to-day, allowing more time to spend on meaningful engagement with their clients. Because at the end of the day, the Financial Advisor’s service, advice, and relationships with clients—the human touch—remains fundamental.”

Feedback from Financial Advisor teams has been overwhelmingly positive:

• "AI @ Morgan Stanley Debrief has revolutionized the way I work. It's saving me about half an hour per meeting just by handling all the notetaking. This has really freed up my time to concentrate on making decisions during client meetings. It's been a total game-changer." (Don Whitehead; Houston, Texas)
• “AI @ Morgan Stanley Debrief has become a crucial part of how I engage with my clients. After the meeting I can quickly review, edit and send an executive summary email while the meeting is still fresh in our minds. Clients have found these summaries to be a valuable addition to our process. I feel that all Financial Advisors should incorporate this tool into their client engagement model.” (Zach Goldberg; Short Hills, NJ)
• "Because of AI @ Morgan Stanley Debrief, I can have deeper, more personal conversations with my clients. I don't have to rely on my team to jot down notes and action items anymore. It summarizes discussion topics and outlines the next steps, which makes our meetings so much more productive. Clients are also really excited to take part in Morgan Stanley’s journey in adopting Artificial Intelligence." (Victoria Bailey; Menlo Park, CA)

AI @ Morgan Stanley Debrief comes after Morgan Stanley Wealth Management announced its relationship with OpenAI as its only wealth management strategic partner in March 2023 and fully rolled out the AI @ Morgan Stanley Assistant in September 2023—an award-winning2 GenAI powered chatbot offering FAs quick access to all of Morgan Stanley’s intellectual capital. To date, 98% of Financial Advisor teams have adopted the Assistant.

“As we reach critical mass in our AI @ Morgan Stanley endeavors, we envision a world where AI serves as an efficiency enhancing interaction layer that sits between our colleagues and the many applications they interact with such as execution and order entry, CRMs, reporting tools and risk analysis, just to name a few,” said Jeff McMillan, Head of Firmwide Artificial Intelligence at Morgan Stanley. “Through this rollout, Financial Advisors continue to see first-hand the real benefits GenAI delivers to their practices. And we’re just getting started in unlocking the true power of this technology for all of Morgan Stanley.”

• Euromoney: North America’s best bank for wealth management 2023: Morgan Stanley
• Morgan Stanley Wealth Management: AI @ Morgan Stanley Assistant

About Morgan Stanley Wealth Management Morgan Stanley Wealth Management is a leading financial services firm that provides access to a wide range of products and services to individuals, businesses, and institutions, including brokerage and investment advisory services, financial and wealth planning, cash management and lending products and services, annuities and insurance, retirement, and trust services.

About Morgan Stanley Morgan Stanley (NYSE: MS) is a leading global financial services firm providing a wide range of investment banking, securities, wealth management and investment management services. With offices in 42 countries, the Firm’s employees serve clients worldwide including corporations, governments, institutions and individuals. For further information about Morgan Stanley, please visit  https://www.morganstanley.com/ .

This material may provide the addresses of, or contain hyperlinks to, websites. Except to the extent to which the material refers to website material of Morgan Stanley Wealth Management, the firm has not reviewed the linked site. Equally, except to the extent to which the material refers to website material of Morgan Stanley Wealth Management, the firm takes no responsibility for, and makes no representations or warranties whatsoever as to, the data and information contained therein. Such address or hyperlink (including addresses or hyperlinks to website material of Morgan Stanley Wealth Management) is provided solely for your convenience and information and the content of the linked site does not in any way form part of this document. Accessing such website or following such link through the material or the website of the firm shall be at your own risk and we shall have no liability arising out of, or in connection with, any such referenced website. Morgan Stanley Wealth Management is a business of Morgan Stanley Smith Barney LLC.

Morgan Stanley Smith Barney LLC (“Morgan Stanley”) provides certain technology tools and services supported by artificial intelligence via an arrangement with OpenAI LLC (“OpenAI”), an unaffiliated third party. Morgan Stanley employees using such technology tools and services are bound by all applicable Morgan Stanley policies and procedures. Neither Morgan Stanley nor its affiliates are responsible any products or services offered by OpenAI on a basis separate from its arrangement with Morgan Stanley and any references to such in this material do not imply endorsement, sponsorship, or verification by Morgan Stanley.

Artificial intelligence (AI) is subject to limitations, and you should be aware that any output from an IA-supported tool or service made available by the Firm for your use is subject to such limitations, including but not limited to inaccuracy, incompleteness, or embedded bias. You should always verify the results of any AI-generated output.

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Media Relations: Lynn Cocchiola,  [email protected] Lindsey Madnick,  [email protected]

Statistics about Cultural and Racial Diversity

Australia is home to the world's oldest continuous culture as well as non-Indigenous Australians who identify with over 300 different ancestries. Migration to Australia is not a new phenomenon. In fact, Australia has a rich history of migration and trade pre-dating British colonisation. [1]

Cultural and racial diversity in Australia

• Over 29% of Australia's population was born overseas and 48% of Australians have a parent born overseas. [2]
• It is estimated that before British colonisation, over 250 First Nations languages and 800 dialects were in use. [3]  
• More than 1 in 5 Australians speak a language other than English at home. [4]  
• Between 2016 and 2021 the Aboriginal and Torres Strait Islander population increased by 25%. [5]

Did you know?

• Long before the colonisation, Aboriginal and Torres Strait Islander people traded with Macassans from the Indonesian archipelago. For hundreds of years, Macassan fishermen arrived in fleets of traditional wooden praus with goods to trade in exchange for access to annual sea urchin harvests. [6]  
• Maningrida, located on Australia’s north-central coast, is one of the most linguistically diverse communities in the world. 15 languages are spoken or signed every day among only a few thousand people. [7]  
• The discovery of gold in Australia in the mid-1800s led to a significant rise in migration. 
• Between 1852 and 1860, more than 600,000 people arrived in Australia, with 81% from the UK, 10% from Europe, and 7% from China. [8]  
• After Federation in 1901, the laws that formed the basis of the White Australia Policy were used to prevent non-Europeans from settling in Australia. This resulted in a rapid decrease in overseas-born residents from almost 30% in 1894 to around 17% in 1911, reaching a low of 10% in 1947. [9]  

Australian South Sea Islanders

• Between 1863 and 1901, over 62,000 people from the Pacific Islands were kidnapped from their home countries and forced to work on privately owned sugar plantations in Queensland. Referred to as 'blackbirding', this process involved working conditions that amounted to slavery. [10]  
• In 2021, 3 in 4 of all Australian South Sea Islanders were Queenslanders.
• 2 in 3 South Sea Islanders also identified as Aboriginal and Torres Strait Islander. [11]  

Racism and discrimination

It is important to celebrate cultural diversity in Australia, but we also need to understand the role of race in shaping society in order to challenge systemic racism. Often racism is hidden by narratives that emphasise multiculturalism and social harmony. Learn more about racism on the Commission's anti-racism campaign website.

• Around 60% of people believe that racism is a significant problem in Australia. [12]  
• In a 2020-21 Women of Colour Australia Workplace Survey Report, 26% of respondents said that their organisation was led by a person of colour and only 7% said that their organisation was led by a woman of colour. [13]  
• [1] Australian Bureau of Statistics, Understanding and using Ancestry data: Explaining the different ways ancestry data can be used and collected (ABS Website, 28 June 2022).
• [2] Australian Bureau of Statistics, Australia’s Population by Country of Birth (ABS Website, 26 April 2021).
• [3] National Archives of Australia, Dispossession and revival of Indigenous languages . 
• [4] Australian Bureau of Statistics, Cultural Diversity of Australia (ABS Website, 20 September 2022).
• [5] Australian Bureau of Statistics, Understanding change in counts of Aboriginal and Torres Strait Islander Australians: Census (ABS Website, 4 April 2023) .
• [6] Department of Immigration and Border Protection (Cth), A History of the Department of Immigration: Managing Migration in Australia (June 2017) .
• [7] Jill Vaughan, Meet the remote Indigenous community where a few thousand people use 15 different languages (5 December 2018) The Conversation.
• [8] The Guardian, How immigration changed Australia – an interactive journey through history (30 January 2023) <>.; Department of Immigration and Border Protection (Cth), A History of the Department of Immigration: Managing Migration in Australia (June 2017).
• [9] Department of Immigration and Border Protection (Cth), A History of the Department of Immigration: Managing Migration in Australia (June 2017) <>; Emil Jeyaratnam, Twelve charts on race and racism (28 November 2018) The Conversation.
• [10] Ibid; Jeff Sparrow, Friday essay: a slave state – how blackbirding in colonial Australia created a legacy of racism (5 August 2022) The Conversation.
• [11] Queensland Government Statistician’s Office, Australian South Sea Islanders in Queensland , Census 2021 (2022) The State of Queensland (Queensland Treasury).
• [12] Andrew Markus, Mapping Social Cohesion (Scanlon Foundation Report, 2021) 11.
• [13] Women of Colour Australia, Workplace Survey Report 2020-21 (Report, 2021) 5.
• Cultural and Racial Diversity Face the Facts with references PDF (358 KB)
• Cultural and Racial Diversity Face the Facts no references PDF (538 KB)
• Cultural and Racial Diversity Face the Facts Word (1.06 MB)

Further Reading

• Understand Why Racism is a Problem?
• Explore Who Experiences Racism?
• Explore human rights teaching resources relating to racism
• Understand the Australian Human Right's Commission work on Race Discrimination
• Review the Australian Human Rights Commission's Anti-Racism Framework  
• Commit to learning to address racism in a meaningful way on the It Stops With Me website