Best books about qualitative research! My recommendations
In this video I review the best books for learning about research , books about data analysis, books about grounded theory methodology, books about writing a thesis, and more… ➡️ The books I recommend in the video: 1. How to do Your Research Project (Gary Thomas) https://amzn.to/2R5eBOO 2. Real World Research (Robson) https://amzn.to/39OqejP 3. Introduction to research methods in education (Punch & Oancea) https://amzn.to/3g1yCk3 4. Designing and Conducting Mixed Methods Research (Creswell & Clark) https://amzn.to/3fP6aSk 5. Doing Interviews (Kvale) https://amzn.to/3sW5sGA 6. Constructing Grounded Theory (Charmaz) https://amzn.to/3fQhNZ4 7. Qualitative Data Analysis (Bazeley) https://amzn.to/31PKvB4 8. Qualitative Data Analysis with NVivo (Bazeley) https://amzn.to/31PKxJc 9. How to Write a Thesis (Murray) https://amzn.to/3rUuuVe 10. The Element (Ken Robinson) https://amzn.to/3cXfnpH 11. Happiness (Ricard) https://amzn.to/3um8VhU 12. The Art of Meditation (Ricard) https://amzn.to/3sYR9kz *** a collection of Ricard’s 3 books that I just found https://amzn.to/2PHb3lB 13. Bird by Bird (Lamott) https://amzn.to/3wyLGD9
Theorizing in Qualitative Research (Part II)
Theorizing in Qualitative Research (Part II) By Huayi Huang Cite as: Huang, Huayi (February 2020). Theorising in Qualitative Research (Part 2). https://drkriukow.com/theorizing-in-qualitative-research-part-ii/ DrKriukow.com In the last post, we challenged the myth that good researchers do not theorise, and directly observe without theory. In giving reason for this challenge, we drew on known facts from the qualitative research literature – documenting some of the competing ideas and ideals of truth, in the academic and wider world. We also introduced in part 1 the idea of abstract thinking, in terms of sets of academic ideas covering specific areas (‘small’ theories), mid-ranged areas (‘middle-ranged’ theories), and all encompassing areas (‘grand theories) of our lived experience. This post introduces and elaborates on an answer to our original question: what does theory mean, in qualitative research? To aid in clarification, we turn to a proposed way of thinking about all of this, from Anfara and Mertz (2015). To illustrate their proposal, we draw on existing knowledge about a middle-range sociological theory of ‘normalisation’ (http://www.normalizationprocess.org/): around changes in social and psychological structure in the implementation of new ideas and practices. In this way of thinking about theory in qualitative research, we start by reflecting on our daily sensations, key events, and other things we experience as we live our lives. An end point in seeking to learn and understand a theory then…could be “to travel into someone else’s mind and become able to perceive reality as that person does. To understand a theory is to experience a shift in one’s mental structure and discover a different way of thinking. To understand a theory is to feel some wonder that one never saw before what now seems to have been obvious all along. To understand a theory, one needs to stretch one’s mind to reach the theorist’s meaning.” (Anfara and Mertz 2015, p2) Back to the world of daily sensations, experiences, and events, then, we develop and name basic ideas for our systems of thinking (‘concepts’ in fig 1.1). In order for this to be possible, the names and vocabulary we use to describe new sensations, experiences, and events, must be meaningful and ‘true’ in some sense that connects with what we already know (and in this way connecting with the sensations, experiences, and events from our past). In the case of our theory of normalisation today then, a basic idea it proposes for our consideration is “relational integration”, aiming to furnish us with a sociological idea for naming and talking about how we seek to build new trust relationships (or sometimes fail to do so), with the new ideas, practices, and people coming into our lives. Moving up in the level of abstract description, and building on these ‘concepts’/subconstructs then, the inventors of this theory of normalisation, proposes that we cluster the idea of “Relational Integration”, alongside 3 other concepts of “Interactional Workability”, “Skill set Workability”, and “Contextual Integration”, clustered under the general idea of “Collective Action”. This larger unit of sociological thinking about “Collective Action” then, is offered to us as a way to reflect on how we might collectively ‘do’ or make concrete a new set of ideas or practices. For this theory of normalisation, aiming to help us order and structure our reflections on group based enactments of new ideas or practices, by encouraging us to generate and review evidence relevant to this unit of thinking. Once we are reasonably satisfied that a ‘construct’(see figure 1.1) like “Collective Action” is descriptive of a part of our worldly sensations, experiences, and events (in quantitative terms based on achieving some collective belief in the construct validity of ideas like “Collective Action” and “Relational Integration”), we come to ‘propositions’ which might be offered to others, in sharing the relationships between ideas – as we or our research participants experience in a research study. In everyday terms, ‘propositions’ are simply shareable/writable/readable claims about their reader’s worldly experiences – which somebody else may or may not accept and take onboard (e.g. as part of peer review). As typically written into papers, journals, books, etc. of the scientific literature, these claims bring into explicit relation (e.g. scrutiny by 3rd party colleagues, hypothesis testing, etc.) multiple constructs about the facets of worldly experience making up the target of a scientific theory or communication. For the theory of normalisation today, its target interest is broadly around significant changes in psychological and social structure, as we may experience in the presence of an intervention into the fabric of our professional lives; our research participants’ shared experiences of such changes in the presence of an intervention then, can be described and explained using its (sociological) vocabulary. Interestingly, as offered through the introductory knowledge of its key ‘constructs’ and their interrelationships on its website (http://www.normalizationprocess.org/what-is-npt/), there is a sense that potential users are invited to take the ideas (e.g. “Relational Integration”) and relationships offered (e.g. “Relational Integration” is always part of “Collective Action” sensations, events, and experiences) the rest of the way…into an interpretation and set of interrelationships that make sense for their own experiences of changes in psychological and social structure, in the presence of an intervention into their lives. In the work of its academic users, further work is of course needed to build on the sociological ideas and relationships offered, in such a way as to more clearly and fully define the key relationships between ideas (‘propositions’), as experienced by the researched or researchers (in working towards academic publication). In doing so, perhaps in illustration of Anfara and Mertz 2015’s early proposition, of ‘stretching one’s mind and abstract perceptions to reach these (sociological) theorists’ meaning by these ideas’. To our quantitative colleagues, this flexibility in the exact meaning of key theoretical ideas, and the relationships between them can bewilder. To working academic qualitative researchers, this same flexibility and intellectual plasticity is what we live for and are excited to rethink, in light of the new human experiences we discover in a research study. When any of us thinks qualitatively in any
Theorizing in Qualitative Research (Part I)
Theorizing in Qualitative Research (Part I) By Huayi Huang In talking with normal people, it is common to discuss ‘theory’ and see their eyes glaze over and their attention wander. In healthcare improvement practice and research, for example, the role of theory continues to be seriously under recognized . Whilst it sometimes benefits the academic agenda to mystify and obfuscate the idea of theory, the myth that some of us do not theorise is underpinned by the naïve realist idea that reality can be directly observed (without theory). But can reality be observed directly…when you really think about it? Reflecting on our experiences as qualitative researchers and fieldworkers, we observe usually using natural languages we know such as English. Such languages have evolved largely without premeditated planning of their lexicon and key ideas. In contrast to mathematical languages and ideas we learnt from school – from a branch of knowledge whose key lexicon and ideas largely predefined for us as impressionable school children, and often accepted as ‘true’ without serious questioning or scrutiny. Reflecting on our common experience as learners in a changing world though – the urge to look for explanations, understandings and causes from our personal and professional experiences is almost irresistible – to the extent that American social historian Charles Tilly argues for us to be defined as ‘reason-giving animals’. As Ravitch and Riggan (2011) point out in explaining how conceptual frameworks guide research, nearly everyone can agree on the idea of theory as ‘trying to give reason’ to things working the way they do. In another words, theorising aiming to help explain why – to the observations we rely on. The natural languages and ideas used in qualitative research continue to evolve largely without premeditated planning, so it makes sense for this evolutionary rather than premeditated development of ideas to carry through – to the explanations based on these observations. To understand their idea of ‘theory’ more deeply, Ravitch and Riggan (ibid.) also offer the thought: of thinking about theory as moving us towards answers to our whys, by way of identifying and examining relationships among things. In one of my current academic projects on international and national primary care transformation, for example, this amounts to identifying and examining transformation ‘actors’ key to that which is being theorised, and key relationships between these key actors. We’re all well used to identifying and examining key relationships among things and people we encounter. In our professional research lives – the general project is around ‘theorising’ on behalf of our research clients – to enable them to identify and (re)examine the key relationships, things, and people they have in mind. At a glance, theories from the academic literature may be distinguished as Grand, Middle-range, and Small. ‘Small’ academic theories focus on identifying and examining relationships among those things and people relevant to a specific intervention project. For example, in the thinking a teacher does around preparing to affect change, in existing key relationships, things and people involved in introducing a new classroom technology into their class. An example of middle-range theorising is in the current sociological theorising of Normalisation Process Theory (which I’m continuing to explore currently). It is a theory currently gaining traction in health care and services research and practice. This theory was intended to aid description and explanation, for thinking about how innovative practices can become routinely embedded in social contexts in healthcare. In a nutshell, Normalisation Process Theory helps us to think sociologically, in identifying and examining key relationships, things and people involved in embedding innovative practices across a broad area of work and society. Finally, so called ‘grand’ theories are the academic equivalent of the most encompassing ideas we find ourselves returning to – across the full range of personal and professional experiences we encounter in our lives. In Merton’s words, the ‘master conceptual scheme’ by which one tries to identify key relationships, things and people involved in the local fabric of our social structures. In supplying a general lexicon and set of key ideas for thinking, talking, and sharing with each other about our worlds – ‘grand’ theorising seeks to maximise the breadth of the applicability of your most encompassing current ideas, at the expense of these ideas not necessarily being well adapted for each and every domain of personal and professional experience you try to apply them to. In context of statistical theory in quantitative research, for example – neglecting research data on how changes and outcomes at a national level occur, in favour of measureable indicators of what is happening, where it is happening, and to whom. Will you be a naïve realist? To further demystify and erode the naïve realist idea that reality can be directly observed (without theory), let us dip into the fact that multiple distinct theories of truth exist, and continue their life in current academic work. According to Schwandt’s dictionary of qualitative inquiry, ‘Truth’ in qualitative research is one of the most difficult of all philosophical topics, intimately related to questions of meaning (to a knower). Schwandt describes the following theories of truth – on the premise that ‘truth’ might be a property of particular statements, propositions, beliefs, and assertions shared about our realities. Then, how is truth established and defined under these alternative working interpretations? 1) A correspondence theory of truth proposes that statements, propositions, beliefs, and assertions become established as true if they accurately represent or match some state of affairs in the world. Statements and propositions, for example, become false if the knower sees inaccuracies in the correspondence between the proposed truth and the relevant parts of their world. Under this premise for Grand, Middle-range, or Small theorising, we move towards answers to our whys, by seeking to identify and (re)examining correspondence relationships, between particular statements and propositions, and the experiences of ourselves or our research clients. In another words, the picture painted in the knower’s mind by the statements we offer as researchers must broadly correspond to the reality experienced by the
Understanding more about patterns of scientific inference. Part III – Abduction
Understanding more about patterns of scientific inference (Part III – Abduction) By Huayi Huang “As inquisitive learners, people like to try to explain things that surprise us. Abductive learning recognises that we do this through spontaneously generating explanatory ideas which come to hand, as we try to informally explain the surprising data or evidence” Whilst Deductive and Inductive inferences focus on movements, between specific and general ideas in learning, standard accounts of deduction and induction tend to draw examples from the formally published parts of what researchers’ actually learn. In particular, academic journal papers, funders’ reports, etc. tend to present highly sanitised versions, of the day to day learning from researchers’ professional lives and work. Treated as descriptive accounts of research-reality, such published accounts usually miss the various hunches, clues, metaphors, possible patterns, and informal explanations interacting with research practices. As inquisitive learners, people like to try to explain things that surprise us. Abductive learning recognises that we do this through spontaneously generating explanatory ideas which come to hand, as we try to informally explain the surprising data or evidence. This in essence seeks to turn the challenge (of the surprise!) to our currently held beliefs, into a more plausible thing – when the surprising data or evidence is viewed as a concrete example of one or more of the explanatory ideas we spontaneously generate. In relation to Deduction/Hypothetico-deductive inference, new ideas are not so much developed based on those already accepted as certain (Deductive) or most likely (hypothetico-deductive) by a knowledge community. Instead the focus of reasoning is on developing abstract ideas (and sharing accounts of them) so as to minimise surprise for the learner. We all spontaneously generate ideas to explain what we encounter, as we live. These are ‘informal’, in the sense that the ideas we explain by are not necessary already part of current published research literature. Some qualitative research experts link such Abductive inferences to the idea of qualitative research being the systematic empirical inquiry towards meaning (e.g. for both the researcher + researched). In another words, evolving our systems of thoughts and evidence in such a way as to make the new evidence (e.g. from primary data collection) meaningful – within the evolved (i.e. ‘more educated’ 🙂 system of thought. There are of course limits in practice however, in the extent to which the ideal of making all new evidence meaningful within the same system of thought, actually happens during 1 research project. In helping us to make sense of each others’ everyday social and human experiences, an academic qualitative research interest is to help readers discover and come to terms with the alternative or multiple meanings for any significant life event. Gilbert Harman’s characterization of abduction as “reasoning to the best explanation.” is a common experience of qualitative researchers who try to explain their data. Professor Robert Miller in his 2003 A-Z of Social Research (https://uk.sagepub.com/en-gb/eur/the-a-z-of-social-research/book211452), characterises this ongoing process of reasoning to the best explanation by analogy from the Darwinian idea of survival of the fittest, based on a foundation of reality being a singular thing accessible equally to all. The population in his case is not so much a biological one, but referring to the ‘population’ of one or more explanatory ideas spontaneously generated, as we learn about some data/evidence. As more interviews are done for example, some of a research team’s earlier explanatory ideas might be dismissed as ‘less fit’, in light of the current pool of qualitative research data (and therefore not followed up as a study progresses). In summary, Abductive inferences try to build explanatory patterns that turn a set of data or evidence, into one of least surprise/highest predictability to the knower. Abductive accounts of reasoning largely forego the historical issue of how particular explanatory schemas have come about, instead focusing on the utility of explanations in making our future predictable. As scientist go about their professional lives, abductive inferences (in the sense of hunches, clues, etc.) only become visible as one zooms down from a high level view, into the experiences recorded in specific project or personal journals, lab books, and so on. The messy world of near misses, inspirational moments, and the confusions set aside, are an all too often unpublicized part of the research world. Yet these are human experiences we all share. Due to the experiential and conceptual worlds typically accessed through qualitative research data, the world of abductive reasoning is all too familiar to the qualitative research experience. The human project of evolving our thinking towards a more consistently meaningful and therefore predictable world within our systems of thought (abduction), can progress without necessarily focusing on whether we are moving from smaller to bigger ideas (induction), or wider to narrower ideas (deduction).
Understanding more about patterns of scientific inference. Part II – Induction
Understanding more about patterns of scientific inference (Part II – Induction) By Huayi Huang Induction In Part I of this series we introduced the idea of inference, to refer to the known broad ways in which scientific learning about abstract ideas has been shared in the past, from limited evidence about these ideas. In this second article, we dig deeper into the idea of Induction, when trying to make sense of our social and human worlds. If you are reading this article, then no doubt you’ve engaged in the human project of trying to generalize from our current and past experiences, so as to interact with things and people around us with some certainty. Quantitative colleagues prefer to address generalizations from specifics through the idea of variables, but some qualitative colleagues find quantified ideas (eg. variables, uncertainty in terms of confidence intervals) rather sterile and distant from the blooming confusion of social and human lives – as lived and experienced in the everyday. Regardless, the idea of using current/past experience to generalize with some certainty about the future, is commonly referred to as induction in contemporary science and scholarship. Whilst deduction is arguably oldest in terms of recorded Western thought, our ancestors are likely to have been generalising on the basis of experience long before Western thinkers such as Aristotle got recorded! Two features help us understand the idea of induction… Feature 1: In moving from concrete specifics to generalities… The first feature of induction is in the inferential move from the specific -> more general. In the syllogism example from last time, Tom might come to believe that he is living on a round planet from being told by his mum for example [fact 1]. Later on, he might come to learn that the earth we share has ‘people’ living in it (including Tom), perhaps from what he remembers of a geography class at school for example. [fact 2] Finally, the inductive reasoning step in the example from Tom then, comes in his moment of realisation in inferring based on facts 1+2: that the earth might just be a round planet? Notably, in (inductively) creating this new general idea of his own – about ‘the earth’, ‘roundness’, ‘planets’, etc. – from the limited knowledge shared from Mum and school. Learning about general ideas of ‘the earth’, ‘roundness’, ‘planets’, etc. are all based on limited, specific evidence here, in the sense that our principal thoughts about the general ideas we live by usually form, based on partial rather than truly comprehensive experience (of these ideas). Let’s stick to just the physical features of our earth for example, for simplicity. Even if you are the most avid traveller/have read all sorts of interesting books on top of your globetrotting, it is arguably impossible for even a whole community of knowers to have truly directly experienced all parts of our wonderful planet – there’s the experience of the deep sea, high mountains, etc. and when you’ve covered all of those one might quite seriously argue that you haven’t yet stepped far into the past, or will die and miss out on the earth reaching the end of its natural life in the cosmos! Many major ideas we use to organise how we think about the world, are also abstract, in the sense that they are areas of human experience less clearly/concretely delineated in their own terms. ‘Operationalization’ approaches from the natural sciences try to meet the challenge of concrete delineation of human experience, by defining ideas in terms of referents from our physical reality. This ties abstract ideas down to observables readily accessible through our five (physical) senses. But a great unresolved question in this space is whether all that truly matters in our social and human lives is adequately covered, by approaching our social/human reality only in terms of physical, concrete, unambiguously delineated things? Feature 2: Accompanying uncertainties in wondering about the wider relevance of inductive ideas The second feature of inductive inferences is in their increased uncertainty, as the reasoning proceeds on the basis of the available facts. In the figure below around the 3 illustrative ‘facts’ of the earth being round, people living on earth, and Tom being one of these people. Fig 1: Differences in dependencies and (un)certainty, in deductive and inductive inferences around 3 facts In the deductive line of inference (Fig 1, LHS), we reason with certainty so long as we continue to believe the ‘premises’ within the system of evidence and thoughts to be certain. For example in fig 1 (LHS), the certainty of point 3 depends on the certainty of both points 1 and 2 – as the 2 premises in inference here. Inductively (Fig 1, RHS), in working through the same facts, we pay more attention to the uncertainties residing within each claim. Drawing attention for instance to the fact that Tom living on the planet is not a fact which will remain forever (point 3, eg when Tom dies), the fact that people currently living on Earth is no guarantee of the ecological future of our species on this blue planet (point 2), and Tom making the inductive inference that the Earth may/may not remain basically round (at least in his lifetime) based on the arguably partial evidence shared through the opinions from Mum and school? Is it important to emphasis that the 3 facts in question here haven’t really fundamentally changed, as we consider them in terms of Deduction or Induction. What has changed, is in the certainty of knowledge we’re reading each fact with in context of its community of knowers, as well as distinctly different dependencies and lines of thinking around the 3 facts. In the deductive case, a community of knowers proceed to develop new thoughts based on those already accepted as certain – ie. facts 1 and 2 in LHS of figure 1 (even if those accepted thoughts are clearly disputable from a more inductive vantage point). In the inductive case the community of knowers has
Understanding more about patterns of scientific inference. Part I – Deduction
Understanding more about patterns of scientific inference (Part I – Deduction) By Huayi Huang In being curious of our social and human worlds, the scientist seeks to learn new things from diverse sources of evidence, ideas, and beliefs. For the qualitative researcher for example, personal interviews are a common source of evidence – on the categorical ideas and beliefs we hold. In quantitative learning interval and ratio data is often preferred, enabling application of mathematical ideas and beliefs to quantitative kinds of evidence. Regardless of your disciplinary education and affiliation, questions of inference come up commonly in practice. Let’s think about inference here, as the known broad ways in which we’ve learnt about abstract ideas in the past, from limited evidence about these ideas. We review Deduction, Induction, Abduction, and Retroduction as 4 forms of inference from scientific history, in a series of blog articles, the first of which is the following post devoted to Deduction. Deduction Let us start with Deduction. Arguably the oldest form of inference (at least in terms of recorded Western thought), deduction is exemplified by Aristotle’s ideas around syllogisms – as a sort of codified set of general rules for logical reasoning. The basic logic of the Aristotelian approach is to start with a set of premises accepted as true by a community of knowers (eg the idea that the earth is a round planet, alongside the idea that Tom is a person living on the earth) and learn implications(e.g. Tom is living on a round planet) guaranteed to be true so long as the premises were. This idea of truth and its discovery hung around for centuries as the main type of ‘valid’ inference, until further studies of logic and reasoning occurred during medieval times. In aligning with the experience and values of modern industrial society, references to the so called ‘scientific method’ often connects with ideas of hypothetico-deductive methods of inference, rather than purely deductive methods. The hypothetico part represents an expansion of the original scope of deductive inference – based only on premises regarded as true – into hypothetical realms where learners start from premises which are only most likely true – eg in developing specific quantified hypotheses based on some most likely premises sourced from the existing literature. Assuming these hypothetical truths/premises to be true(at least for the initial parts of a study), deductive inferences can then be made by assuming the truth of these most likely ideas. In context of a literature review, the many previously published premises provide a rich range of premises, analogous to the roles played by the ideas that the earth is a round planet, and that Tom is a person living on the earth, in the example of deductive inference given at the beginning of this section. In practice, an idea documented in existing literature can be evaluated quite differently by different knowledge communities. For example – continued controversy between academic knowledge communities regarding the idea of reality being a singular thing accessible equally to all, or more of a pluralistic thing depending on your point of view. Suffice to say that there is credible scientific literature/argumentation/evidence on both sides of this debate, with qualitative researchers tending more commonly towards the idea of reality being more of a pluralistic thing depending on your point of view (eg. https://doi.org/10.1177/1468794110366802 2010). From a knowledge community which assume that reality is a singular thing accessible to all, we have the following idealisation, of the method of hypothetico-deductive inferences from experimental biology (Fig 1a). When zooming out beyond the context of individual studies conducted in a manner like in fig 1, the cyclical aspect of the hypothetico-deductive model of inference becomes clearer. Fig 1: An example of a single hypothetico-deductive cycle, in context of biological primary research (adapted from a biology textbook). Fig 1b presents a reading of these idealisations of experimental design/procedure, from a qualitative research perspective. In trying to translate the logic of hypothetico-deductivist thinking, into the context of qualitative research, a number of key points are highlighted regarding these 2 topics: A. In making hypothetico-deductive inferences, the learner is committed to the binary categorisation of all statements about the world as ultimately True or False (Step 7a/b). Research of the ‘predictive truth testing’ types (steps 5/6) benefit enormously from the application of mathematic ideas in learning, experimental rather than naturalistic research designs, and the idea of talking about most likely ideas from the past as only those that are True and those that are False (‘correct’ and incorrect’ respectively in light of your data as in fig 1). B. In using mathematical language – we benefit from the standardisation and clarity of meaning of its key ideas, eg in regard to what the idea of an average means exactly. These standardising and clarifying effects carry through into communications of what we learnt – in sharing experiences with each other using mathematical ideas. However, note the often unarticulated+unexamined premise (ie. a hypothetical truth/premise) underpinning the act of quantifying social and human reality: that all that truly matters in our social and human lives are expressible through mathematical language and ideas! C. In preferring experimental rather than naturalistic approaches to learning, we limit the evidence for our shared learning to the closed, highly controlled conditions possible in a lab-like study. Our social and human worlds – and the evidence of these worlds as we experience them in the day to day – usually take place in far more open and dynamic conditions. One role of a qualitative researcher is in trying to make sense of ideas arising from our social and human experiences, whatever and wherever these experiences may be. Following this thought through, it seems almost a misnomer to eg pose the question of whether the ideas shared by our interviewees are true or false(fig 1b). ” Arguably the oldest form of inference (at least in terms of recorded Western thought), deduction is exemplified by Aristotle’s ideas around syllogisms – as a sort of codified set of general rules for logical