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).
How to select your research topic?
(you can enrol in my self-study course on how to select a research topic here: https://www.udemy.com/dr-kriukows-dissertation-training-part-one/?couponCode=DISCOUNT) I get this question a lot in your messages through Facebook, so I compiled some advice on how to select your research topic. First, choose a topic that is interesting to you. It may seem obvious, but this will make the research process more fun and engaging for you, especially if you are undertaking a Doctoral degree which means that you will spend not between 3 and 12 months researching and writing about it (as would be the case if you were working on your master’s dissertation), but 3 to 5 years! I have said it many times before and will say it again – considering the amount of workload, stress and frustration that I experienced throughout the 3 years of working on my doctoral thesis, there is no way on earth that I would have completed it if I hadn’t been genuinely interested in what I was researching! I treated this whole process as a personal quest for answers to the questions I asked in my thesis, and this enabled me to move forward whenever I had any sort of doubts. “OK”, I can hear you asking, “but how do I know what topic is interesting to me?”. Well, there are most likely some general topics within your field that attract your attention more than the others. So, within the field of Teaching English to Speakers of Other Languages (TESOL), for example, you may enjoy learning about Second Language Acquisition (SLA) theories more than about Language Testing, or prefer reading about teaching speaking skills more than about teaching reading skills, and so on. To have this, no matter how vague or distant, idea about what you find more engaging to read/learn about within your field is, in fact, a very good starting point to continue researching the topic in order to narrow down the specific issues that you may want to research. The next step is to read, and read, and read… Read academic articles on the topic that interests you. Use your university’s online library resources, or Google search engines if you are not a university student, to access all kinds of articles that relate to your area of interest. Start with broad searches – if you are looking for articles on “teaching speaking skills in English”, you can initially use this very phrase in your searches. In the process of reading, you are likely to become interested in more and more specific aspects of this topic. You may find, for example, that you find the methodology of task-based teaching interesting, particularly because in your own educational contexts you have never come across it. Therefore, you decide to read on “the use of task-based methodology for teaching speaking skills to [Chinese, Spanish, Russian, etc.] learners”, as you wonder whether it has ever been attempted and with what results. You can now both narrow down your searches by typing this kind of phrase next time you look for more articles to read, and use the reference list at the end of each article to find other relevant published work. You can read books too, but I think that at this stage it is more time-efficient to focus on articles, as not only are they shorter and you can, therefore, read about more different issues within the topic, which are discussed by more authors who adopt different perspectives on it, but also academic articles usually tend to cite more sources, be more critical and more explicit about the existing gaps in knowledge. To determine these gaps in research/knowledge, is an important step in developing a research topic that will not only be interesting to you, but also, potentially, to the wider audience. This does not mean that you absolutely have to find a topic that has never been researched before. Instead, this could be something that needs further research, the claims that need more evidence, etc. You will know about this not only by just reading these various articles that are now becoming increasingly focused on the issue of your interest and noticing certain patterns in them, but also by the authors’ explicitly stating that a given issue calls for further investigation. The sections of academic articles to be closely analysed in this regard are the Literature Review and Implications/Further Research/Conclusions sections. The Literature Review section will not only give you plenty of ideas about the existing discussions within the field and send you directly to the sources that may be of interest to you, but it will also outline the topics that are controversial and/or under-researched. The concluding sections, in turn, are almost certain to be very explicit about further research that needs to be carried out – and this is precisely what you were looking for, isn’t it? If you would like to learn more about developing a research topic, I have a whole self-study course about it! (You can enrol here https://www.udemy.com/dr-kriukows-dissertation-training-part-one/?couponCode=DISCOUNT) The course follows the following topics: – What is a “good” research idea and how to develop it? – What to read? How do you know what topics you should start with? What kind of materials to focus on? Which sections of published work should you pay particular attention to? – Where to look? Where can you find the literature that is relevant? – How to read? What reading techniques should you use? – How to organise your reading sources? How to make sure that you are on top of your reading and don’t loose these valuable sources and quotations? – When should you start writing? – What to write and when? – What is free writing and how is it going to help you? – Is Thinking just a waste of time, or is it just as important an activity as reading and writing? – How to come up with research questions? – What types of research questions are there? – What is a “good” research question? – How to incorporate your idea and research questions into the Literature Review chapter?