Video 1: What can a book guiding integrative thinking with complexity offer to mixed methods researchers?
Over the years as a mixed methods researcher, I experienced my share of successes as well as challenges. Sharing how I overcame the dilemmas I faced and the lessons I learned from these dilemmas is why I wrote this book. Let me explain – when I talk about dilemmas I mean the moments where the traditional ways of doing mixed methods research simply don’t work. These traditional approaches can come from what you learn from others or see others doing or read about in books and other literature. What I set out to write about and have provided practical examples to guide are what I call adaptive practices that served as workarounds when the traditional mixed methods practices did not fit. For example, have you sought to fit a study into an existing mixed methods design by changing what you really want to do? If so, you are not alone - I myself had experienced this as well as I have heard countless times the same scenario where a researcher is counselled not to the research the way they wanted to because no design existed.
Rather than changing the idea or procedures guiding the study, I advocate for changing the way we think about designs. Don’t get me wrong there are plenty of great resources out there for guiding mixed methods research and great diversity in the designs that exist but what I am especially interested in guiding with this book are those times when researchers need other options. Before I even started writing I had begun to see patterns in the types of research conditions that needed a different approach from what I was seeing. What I noticed was that many of the study conditions could be considered more complex and this realization gave me a framework for being able to talk about situations in which the traditional approaches to MMR worked and contrast with the more complex situations which required a new approach.
I then tried out my ideas in my own program of research. What I learned was to be better positioned for conducting research under conditions of complexity, I needed to adopt a more adaptable approach where I pay attention to how my understandings are evolving. In this book, I identify 6 adaptive practices for guiding a more complexity-sensitive approach to mixed methods research. Integrative thinking comes into play to inform how I will adapt to these changing conditions by drawing upon the principles of complexity science.
I wrote this book to help guide researchers in their work by recognizing and making mindful decisions about how to deal with the varying conditions of complexity surrounding their research. By identifying the extent to which the conditions are complex and apply integrative thinking to my mixed methods research, I began to see new possibilities for guiding adaptive practices where researchers become more responsive to the varying conditions of complexity. This book can be used by many different audiences from graduate students and new mixed methods researchers to more experienced researchers but some familiarity with the foundations of mixed methods research related to designs and integration will be helpful on which to build new understandings of research under complex conditions.
In the videos I will introduce a variety of topics from describing book and website features and content to what is mixed methods research or how integrative thinking informs the work of mixed methods researchers. I hope you will join me on this learning journey.
Video 2: How can researchers distinguish more complex conditions and the need for adaptive practices?
All research takes place under conditions of complexity – regardless of whether we, as researchers, recognize and respond to this complexity. All too often, our responses involve attempts to reduce, control, or simply ignore the effects of complexity. Among the pressing challenges for many researchers is simply to recognize the extent to which the conditions should be considered complex. The purpose of this book is serve as a guide for researchers to assume responsibility for recognizing and making mindful decisions about how to deal with the varying conditions of complexity surrounding their research. This is because how the researcher chooses to initially diagnose their research along a continuum of varying conditions has important implications for the possible outcomes.
The first step is distinguishing between more complex and less complex – I will use a well-known metaphor comparing a recipe to parenting a child. But first let me tell you that I tend to use the term less complex because I want to avoid the use of simple as a descriptor because nothing in research is really simple. I consider less complex conditions for research involve situations that are more predictable – perhaps you have worked with this study population and site before or you have explored this issue in many places so you can know what the outcomes might be. Greater prediction means that the research procedures can be predetermined because they are likely implemented as planned. Can you think about an example from your own research? I think about the work I have done in higher education environments where the intention of the study was to use qualitative data gathered in focus groups to explain the quantitative patterns we were seeing in teaching and learning indices. The design reminds me of when I follow a baking recipe to make the muffins – where there are times that I might need to change something, like substitute oil for eggs but for the most part the outcomes are predictable. To compare, more complex conditions involve situations where not only are the outcomes unpredictable but perhaps even unknowable at this point. This is often the case because we simply don’t have enough experience to be able to know even how to design the study so the study design must evolve over time in response to what we figure out about the situation. Think about a problem related to poverty – how might we tackle this research? It reminds of the process of parenting whereby I constantly must be adapting my approach to my evolving understandings of what my 5 year old daughter wants and the world in which she is interacting. I can’t predict what the outcomes will be as she grows up! If I had another child I would need to start all over again. Consider whether your study conditions should be considered more or less complex?
So if you’re interested in learning more about conditions of complexity that students find challenging watch that topic on a video as well another focused on the five adaptive practices.
Video 3: How can this book support learning about mixed methods research under conditions of greater complexity?
Hi – I would like to tell you a little about myself and how this book can help you learn about mixed methods research under conditions of complexity. Over the years, I have developed and taught graduate-level research methods and programme evaluation courses in addition to supervising and mentoring students, faculty, and community members in qualitative, quantitative, and mixed methods research. I took my first mixed methods research workshop over 15 years ago from John Creswell early in my doctoral work. Before then I had been a high school science teacher with an interest in how we can enhance the learning environment and assessment practices for all students. I bring a global perspective to my work having worked in classrooms in several places around the world. After graduation, I joined the University of Alberta that is located in Western Canada where I have been for the last decade. During this time, I became active in founding the Mixed Methods International Research Association and recently finished my term as its fourth president. I continue to love teaching and listen keenly to my students about what they find challenging. This is why I wrote this book – to guide researchers through what they find challenging. Another reason I wrote this book was to fill a gap I saw in many textbooks. If we don’t introduce students to pay attention to the complexity in their study conditions then we not preparing them for the realities they will face in their work and indeed for tackling future problems that have yet to be known.
How I approached this book is similar to my approach as an instructor, researcher and workshop leader. I tend to be known as someone who is very organized and intentional in crafting meaningful learning experiences and focus on building capacity no matter what I am doing.
In this book, I do the same – at the beginning of each chapter I introduce key questions that help orient you to the topics within each chapter – kind of like a road map of sorts. Then I highlight key vocabulary in the list of new chapter terms. Embedded throughout the chapters are practice alerts that extend your learning by applying new understandings of material covered in the book so that you can use it directly in your own mixed methods research studies. To bridge theory with real mixed methods research projects, I use the Featured studies that have been carefully chosen to represent a range of mixed methods article examples under conditions of complexity. These articles are available on the companion website for the book. One of my favorite parts of this this book are the researcher spotlights which report the experiences and viewpoints of several prominent and emerging mixed methods researchers from around the globe on key pressing and future challenges. These same researchers offer advice for navigating the complexity of mixed methods research in the feature guiding tips. The chapter summaries reinforce the key concepts and then to help you assess the extent to which you have developed the intended knowledge and skills, I offer check ins. It is my hope these opportunities help you to build confidence in your emerging mixed methods research capacity.
To support your own learning or if you are helping to build capacity of others as a supervisor, research team leader, or instructor – the digital resources available on the companion site can be helpful. For each chapter I provide access to some of the further readings and offer opportunities through the chapter-specific activities for checking your understanding of the chapter content. Finally, for those assuming a more instructor role I have created PowerPoint slides to accompany each of the chapters that I hope useful to you and your students.
Video 4: What is mixed methods research and how does this book distinguish it?
Hi – A lot of people ask me about what is mixed methods research and when to use it. I would like to tell you a little about what is found in the literature and how I think about mixed methods research in my own work. My reason for doing this is that I am often asked about the difference between the terms mixed methods research and multiple methods research. I want to briefly introduce what I see as both a challenge and an opportunity for the global community of mixed methods researchers. The challenge is that is that I have seen some people use the terms mixed methods and multiple methods interchangeably whereas I define the terms very differently in this book. While this can be confusing it is not unusual in research - for example, you might have heard people use the term triangulation or research methods in different ways. What is of paramount importance is that you make explicit your perspective of mixed methods research either by creating your own definition or by adopting an existing one and citing its source. For many people the differences in terms lies in what types of data are used – for example many, including myself, define mixed methods research as requiring the integration of qualitative and quantitative data and define multiple methods as any combinations- for example more than one type of qualitative data. In this book, I position my mixed methods definition as:
A cohesive approach to ethical and rigorous research where qualitative and quantitative data are collected, analyzed, and integrated to generate novel inferences drawing on the collective contributions of data types to address the mixed methods study purpose.
Defining mixed methods research has been a topic of great discussion over the years and has led some to focus on characteristics of mixed methods research as a way of providing a definition. These characteristics can be helpful when determining if mixed methods research is a suitable approach for a research problem you want to pursue.
I will now describe my six core characteristics for mixed methods research:
⇒ First, methodological congruence is maintained meaning that the design and data procedures must generate information that is relevant for answering the research problem.
⇒ Second, ethical standards are followed, meaning that the three guiding principles for the conduct of ethical research are considered and followed throughout the study: respect for persons, concern for welfare, and justice.
⇒ Third, procedural rigour is upheld, meaning that efforts to enhance reliability and validity of the findings is embedded throughout the study.
⇒ Fourth, qualitative and quantitative data are collected, analyzed, and importantly - integrated.
⇒ Fifth, novel insights are generated meaning that mixed methods research should only be undertaken when something new is generated by the integration of qualitative and quantitative data.
⇒ Sixth and final, the rationale(s) for point(s) of interface is/are specified meaning that mixed methods research should only be undertaken when there is a clear need for integration of qualitative and quantitative data.
I hope you will take this opportunity to define what mixed methods research means to you and that this book helps you in this quest. In a subsequent video I talk about purposes for mixed methods research and when it is needed.
Video 5: When is mixed methods research needed and how can this book help determine if the approach is suitable?
When exactly is mixed methods research needed? How do you recognize and then tell others about the need mixed methods research? You will not be surprised that many people have written about different rationales for mixing also known as mixing purposes. In the book my own list includes seven specific needs for integrating qualitative and quantitative data:
⇒ First, for more complete understandings – let’s say much research has identified quantitative factors but we need more contextual understandings
⇒ Second, to explain initial results – let’s say a quantitative finding have not been found to be significant in the areas that had been expected and we want to know why
⇒ Third, to first explore before administering instruments – let’s say there is a new area of study that needs exploratory work
⇒ Fourth, to enhance an experimental study with a qualitative method – let’s say there is an intervention for which understanding the impacts would greatly benefit from rich description.
⇒ Fifth, to describe and compare different data – let’s say we can predetermine constructs on which it makes sense to compare data from qualitative and quantitative sources.
⇒ Six, to involve a particular sample in the study – let’s say I want to find a particular sample and I use either a qualitative or quantitative method to identify those meet certain criteria.
⇒ Seventh, to develop an instrument, let’s say the area of study lacks a reliable and valid instrument and so we use qualitative data to inform the development of a survey instrument.
When you look at quality criteria for mixed methods research or to books that guide mixed methods researchers – you will see that early on there is a need to define the purpose for doing mixed methods research. In the book this list of seven mixing purposes provide a jumping off place this book but what is innovative about this book is that we are not limited by them – indeed one of the key dilemmas this book attempts to resolve is what to do when the need for MMR does not fit one of the existing needs and is focused on innovation? Have you had such experiences where your need for mixed methods research does not fit an existing mixing purpose typology? What have you done in such situations? I hope this book will help guide you in times where the mixing purpose cannot be predetermined under complex conditions.
Video 6: What dilemmas are most challenging in mixed methods research and how can the adaptive practices described in this book help?
I wrote this book because I wanted to help guide the responses of students and colleagues under conditions of complexity. In this book I help you diagnose the conditions of complexity so that you are better positioned for responding to dilemmas. Let me tell you a little about the five most common dilemmas I have faced and how paying attention to these conditions can help you to diagnose the extent of complexity you recognize in our study.
Do any of the following scenarios seem familiar to you?
⇒ In the video about when mixed methods research is suitable, I mention the scenario I experienced when I could not find literature specifying the gaps related to my problems I wanted to pursue. What this meant was we simply did not know enough about the problem to be able to identify the mixing purpose beyond the need for innovation. I came to realize that when researchers consider possibilities that are yet to be known for mixing purposes, they are better positioned to realize innovations in mixed methods research outcomes. In this book, I advance adaptive practices to move the traditional practices of fitting my need for doing mixed methods research into existing mixing purposes.
⇒ Another scenario I discuss in this book is when I could not describe the context where I would conduct my study addressing the problem I wanted to pursue. What this meant was that I simply did not know enough about the problem to be able to identify where I could study it. I came to realize that when researchers adopt a systems perspective for identifying research systems that are yet to be known, they are more able to accurately represent the sources for changing conditions. In this book, I advance adaptive practices to move the traditional practices of situating research within contexts that are already known.
⇒ A dilemma is posed when I cannot identify the expertise I need to do the study to address the problem I want to pursue. What this meant was that I did not yet understand the problem enough to know what methodological and disciplinary expertise I needed. I came to realize when researchers consider the nature of the interactions required by the research problem, they can make more informed choices about whom to involve in the study. In this book, I advance adaptive practices to move the traditional practices for establishing research teams and capacities based on what we expect their contributions to be.
⇒ Like others, I experienced times when I cannot fit my studies within the predetermined designs to address the problem I want to pursue. I came to realize that the design I needed may simply not yet exist and when researchers consider possibilities that are yet to be known for designs, they are better positioned to realize the intended research outcomes. In this book I advance a descriptive design approach as necessary for planning based on emerging understandings of the procedures for integration.
⇒ Finally, when I cannot predetermine the necessary validity evidence for my study this means that we simply don’t know enough about the problem I want to pursue. I came to realize that when researchers consider possibilities that are yet to be known for evidence of methodological rigour, they are better positioned to realize more innovative research outcomes. In this book, I advance adaptive practices for assessing evidence of research outcomes.
These dilemmas can help us begin to identify areas where our traditional practices limit our ability to respond to more complex circumstances. The adaptive practices in the book provide important guidance in this respect and I hope you find it useful in your work!
Video 7: How are conditions of complexity distinguishable in mixed methods research and how can the book help?
Video 7: How are conditions of complexity distinguishable in mixed methods research and how can this book help?
I wrote this book to help others manage mixed methods research under conditions of varying complexity– when I talk about conditions of complexity I mean those conditions that ever changing and thus less stable. Some people have asked me whether any research can be considered to happen under conditions of stability and I simply say it depends on whether you are willing and perhaps able to recognize change and instability because yes – in my view all research takes place under some conditions of complexity regardless of whether we, as researchers, are prepared to recognize it.
The challenge for many of us is that we are ill-equipped to recognize complexity because much of our training and traditional research practices are based on assumptions of stability in our research conditions. This may be because it is simply easier to reduce complexity than to deal with it. What we are finding out is that a reductionist approach can limit the possibilities of our research and so we find ourselves with problems that force us to confront the complexity in our research conditions if we want to pursue certain problems.
Diagnosing the complexity in the research conditions can help us recognize potential sources so we are better equipped to respond appropriately to changes in the conditions as the research unfolds. To help us diagnose complexity of research conditions, I have identified five dimensions that I would like to introduce and describe some of the indicators to watch for – some of these will be familiar from other videos and others might be new to you:
First, what I call ‘intentions of research problems’ simply refers to what we have already mentioned - conditions where researchers do not know enough about the problems they want to pursue to be able to identify the mixing purpose for the study or even to identify the intended outcomes of the study at the onset of the study. This might seem strange but think about the problem related to child poverty – when we start pursuing this problem we might have some ideas of possible influences such as parental employment and that at least post-secondary education and availability of child care affect stable employment. What we don’t know yet is what the focus of mixing purpose will be: for example, to explain, to develop or even to innovate. We also don’t know yet what our desired outcomes are other than to potentially decrease child poverty because we don’t know the mechanism partly because we lack guiding literature relevant to the complex problem. Consider the potential if we adapt to emerging understandings over time in order to focus the mixing purpose. To diagnose the extent of complexity in this first dimension, we can look for evidence of what is known about the background influences by researchers or established in the literature and assume that a lack of information can be an indicator of greater complexity.
I refer to the second dimension as ‘systems of research contexts’ because we have also already mentioned conditions where researchers are not able to identify the contexts in which to study their problem at the onset of the study. If we consider our study of the problem of child poverty – again when we start planning we might have some ideas of possible contexts such as unemployment centres or school systems. What we don’t know yet is how the differing systems fit together or how systems interact with each other: for example, should the child be placed at the centre of the system or the parent– perhaps examples of optimal locations of the systems don’t yet exist in the literature and so we have yet to find this! Think about the possibilities if we remain open to building our understandings of contexts over time and thus to diagnose the extent of complexity in this second dimension we can look for evidence of what is known about the environmental influences or details about populations that could be studied in pursuit of the research problem by researchers or established in the literature and a lack of information can be an indicator of greater complexity. Another factor in increasing the complexity is if the study involves a large number of sites or that are geographically dispersed because these tend to be more complex than single site studies of more alike populations.
The third dimension I call “designs of research integrations” refers to conditions where researchers cannot identify or fit their study procedures to an existing design at the onset of the study. If we consider our study of the problem of child poverty – it would not be surprising that we are unable to predetermine our design if we cannot identify our mixing purpose nor our study contexts and populations. What remains uncertain is what data we will collect, where we will collect it and from whom and we certainly cannot predetermine how this data will be integrated as is necessary in mixed methods research. Think about the design possibilities if we describe our designs in response to our emerging understandings of the data we eventually collect and integrate. To diagnose the extent of complexity in this third dimension we can look for evidence of what is known about the feasibility influences in the literature and examples of timelines and resources and assume that a lack of information about established procedures for addressing the problem can be an indicator of greater complexity.
I refer to the fourth dimension as ‘capacity of research interactions’ to describe the conditions where researchers cannot identify the necessary methodological and disciplinary expertise for addressing the problem at the onset of the study. If we consider our study of the problem of child poverty – it is natural if we don’t know what research capacity is necessary if we are unable to predetermine our design and data procedures. What we don’t know yet are the nature of the and roles for the study. Think about the capacity possibilities if we assemble a research team in response to our emerging understandings of the necessary researcher contributions and interactions. To diagnose the extent of complexity in this fourth dimension we can look for evidence of what is known about the social influences among researchers and the nature of the desired outcomes from the interactions because many social influences, less role definitions, and more intensive collaborations can be indicators of greater complexity
The fifth and final dimension ‘evidence of research outcomes’ refer to the conditions where researchers cannot identify the validity evidence for having confidence in the outcomes at the onset of the study. If we consider the problem of child poverty – it is logical if we cannot predict the research procedures or outcomes then we are unable to predetermine the validity evidence we would seek for the study. Think about the evidence possibilities if we adjust our strategies in response to the emerging understandings of the possible outcomes. To diagnose the extent of complexity in this fifth dimension we can look for evidence of what is known about practical influences and the certainty of the outcomes because less predictability can be an indicator of greater complexity.
This is just a quick overview please see the book for guidance for diagnosing the conditions of complexity in your research using the five dimensions. I also provide templates for creating complexity study profiles as way of helping you to recognize and convey the complexity in your study. Diagnosing the complexity in the conditions provides important information for justifying the need for complexity sensitivities in your approach involving adaptive mixed methods research practices.
Video 8: What specific skills do students need to do mixed methods and how can the book support this?
I wrote this book because I wanted to help researchers become more adaptive in how to approach mixed methods research. Research is by nature a social process involving people undertaking practices and negotiating processes. In this book I help you build capacity for responding appropriately under varying conditions of complexity through the use of 6 adaptive practices. To begin it is important to identify the specific stills need under conditions of complexity and I will now describe five roles that researchers assume when engaging in the six mixed methods research practices and realizing the organic research process.
⇒ As a practitioner, you develop a distinct ethos for mixed methods research by engaging in professional learning about qualitative, quantitative, and mixed methods research and promote the field of mixed methods research as a member of mixed methods research communities.
⇒ As an architect, you formulate research problems and align the plans guiding a mixed methods study by designing procedures that are appropriate for the mixed methods study conditions.
⇒ As an engineer, you conduct the procedures and guide the technical aspects of a mixed methods study by negotiating and adapting the mixed methods study implementation.
⇒ As a collaborator, you facilitate the social interactions and develop capacity for a mixed methods study by monitoring and mitigating issues as they arise.
⇒ As a manager, you coordinate data management and oversee logistics in a mixed methods study by organizing and maintaining records.
As you begin to develop these roles, experience tells me that it is important to assess your own readiness for undertaking mixed methods research under conditions of complexity. The following seven questions can help you in this self-assessment. To what extent:
Can you adopt a worldview that is philosophically open to mixed methods research by demonstrating tolerance of methodological and disciplinary differences to draw from diverse expertise as needed?
Can you embrace learning specific to the field of mixed methods research by becoming familiar with practices and literature relevant to initiating, planning, and implementing mixed methods research and perhaps assume an advocacy role?
Can you adapt to dynamic mixed methods research conditions by situating the research within its problems, members, contexts, and procedures?
Can you commit to extensive data and resource management by spending plenty of time managing data collection, budget, and human resources involved in mixed methods research?
Can you attend to anticipated and emergent ethical issues by addressing anticipated ethical issues as well as to those that arise during the mixed methods study?
Can you engage in the complex task of data analysis, integration, interpretation, and representation by dedicating extensive effort to generating integrated findings through individual data type analysis, mixed analysis, and creation of joint displays?
Can you create authentic research accounts for dissemination by providing accurate portrayals of research processes and outcomes using a variety of communicating strategies?
Don’t worry if you are not ready across all the questions – but be ready to develop relevant skills. I hope you will take this opportunity to identify your existing strengths and areas for future learning about mixed methods research.
Video 9: How does integrative thinking with complexity described in this book inform the work of mixed methods researchers?
As we begin to unpack the opportunities afforded by integrative thinking with complexity science for mixed methods researchers, it is important to first define what we mean by these terms. In the book, I define integrative thinking as bringing a complexity lens to bear on what works in traditional mixed methods research practice tendencies to develop adaptive practices for use under varying conditions of complexity.
The reason a complexity lens is so helpful is that it helps me to see four opportunities for mixed methods researchers that I may otherwise have missed:
As a researcher, do you observe surprisingly large effects from what was initially perceived as an insignificant influence during a study? This was certainly my experience and I as struggled with the consequences, I began to consider the opportunities afforded if I could recognize the potential for these events to occur before they happened. Consider this example from my own research experience – I was working on a research team where one particular person challenged us to think beyond what we thought were possible outcomes during the planning stage of a project. This unique perspective shifted some decisions about how we collected data and opened up new opportunities during the analysis that we could not have imagined. In retrospect, I had erroneously anticipated only small contributions to the research by this person. In the end, their influence on the mixed insights we derived from the study had been large. Since then, similar experiences have reminded me of the need to remain open to research outcomes that are not yet known and develop comfort with tackling complex research problems in new ways!
As a researcher, do you realize the capacity that more than one research member, as part of a team, can generate outcomes that are greater than the sum of individual contributions? I have certainly struggled to optimize individual and team potential in a study and I began to consider the opportunities afforded if I could somehow facilitate integrating their individual contributions following a period of conflict. Consider this example from my own research experience – I was leading a research team where the group somehow managed to move beyond conflict to generate understandings that I genuinely believe would not have been otherwise accessible. In retrospect, I had mistakenly interpreted the conflict and negotiation as negative rather than a mechanism for collaborative work. In the end, they were able to manage their development as a collaborative team drawing on their unique expertise and experiences. Since then, similar experiences have reminded me of the need to focus on creating conditions diverse team members to interact so that they can develop their own emergence capacity as research teams members.
As a researcher, do you experience unusual results from what were initially perceived as similar research conditions? I have been surprised when studies that I assume are occurring in similar contexts produce very different outcomes and so I began to consider the opportunities afforded I could somehow develop better initial understandings of the initial research conditions to distinguish these contexts. Consider this example from my own research experience – I was an intervention study where two sample populations could not account for the differing results. In retrospect, I had not taken into account the differences beyond those that I could see nor pay any attention to the potential dynamic influences. Since then, similar experiences have reminded me of the need to assume changing conditions and to develop a more accurate understanding of contexts from a systems perspectives.
As a researcher, do you adapt your approach in response to dynamic influences? I have struggled with this yet I began to consider the opportunities afforded if I could somehow monitor and anticipate the need to adapt my approach. Consider this example from my own research experience – I was leading a study where the gatekeeper to a research site, the day before the study was about to begin, blocked access to the site in response to a policy shift in government. In retrospect, I had incorrectly assumed that the research would be buffered from societal-level influences and had not anticipated the need to attend to these influences. In the end, we were able to pivot our resources and access another site. Since then, similar experiences have reminded me of the need to monitor for dynamic influences and assume the need for responsive designs.
Integrative thinking with complexity underpins the six adaptive practices that comprise the complexity-sensitive approach to mixed methods research I advance in this book. While these ideas may not yet be mainstream they are generating a lot of interest. Remember that adaptive practices drawing upon insights gleaned from a complexity lens are not something that needs to be left to a mixed methods research expert; rather, those who are just learning about mixed methods research sometimes have the advantage that they are not constrained by traditional wisdom.
Video 10: Why are adaptive practices essential for a more complexity-sensitive approach to MMR and for future MMR innovations?
I believe that integrative thinking and adaptive practices can boost innovation in the field of mixed methods research and its yet-to-be-realized potential for tackling complex research problems. Evidence indicates that under conditions of complexity, mixed methods researchers benefit from being integrative in their thinking with complexity about their practices. This is because the tensions created by complexity can be harnessed to develop innovations in mixed insights and practices that emerge from a unique synthesis of researchers’ diverse methodological and disciplinary expertise, experiences, and intuitions beyond what would be accessible individually. I anticipate that as the capacity for integrative thinking with complexity develops, mixed methods researchers will become better positioned to tackle many of the complex social problems.
Integrative thinking with complexity transforms what might be familiar to become more adaptive research practices. This is because under conditions of complexity, mixed methods researchers need to continually adapt their responses since conditions are constantly changing and unpredictable. For example, as our understandings of complex mixed methods research problems evolve; adaptive responses are needed to access the expertise and data procedures to generate integrative solutions.
Throughout the past decade, I have become increasingly aware of sources of complexity and been humbled by the effects of the varying conditions of complexity I have witnessed. Across my experiences as an instructor of research methods, graduate student supervisor, and research team member, I have discerned a common theme running through the practices of researchers who successfully navigate the complexity inherent in much of mixed methods research: researchers who attend to changing conditions and respond appropriately in their framing, planning, and conduct of research practices experience fewer dilemmas than those who do not.
By challenging the status quo (i.e., established practices) and advancing new mixed methods research practices you can be an innovator. I ask you to consider the benefits, because integrative thinkers and the researchers that support adaptive practices can accelerate the acceptance and adoption of complexity-sensitive approaches to mixed methods research. If you think complex problems are important to study, then we need researchers to delve into the ‘messiness’ and advance new practices because we want to demonstrate to the mixed methods research community that innovations are not only possible but desirable. This also allows us to think and act with intention: do not default to the traditional mixed methods research practices, but rather use them deliberately when appropriate for the research conditions. If we want to pursue big challenges, then it is necessary to mobilize people to experiment. Many of the highly complex problems cannot be solved with existing technical expertise. Instead, we must advance new ways of thinking and doing. This is exactly what this book guides mixed methods researchers to do under conditions of complexity.