Qualitative Research Methods PDF

Qualitative Research Methods: Lead Teachers: Nicola Morant To be covered: Overview of Qualitative Research Methods Reading list: PSBS0002: Core Principles of Mental Health Research | University College London (talis.com) Class Prep:...

Qualitative Research Methods: Lead Teachers: Nicola Morant To be covered: Overview of Qualitative Research Methods Reading list: PSBS0002: Core Principles of Mental Health Research | University College London (talis.com) Class Prep: Read: Qualitative Research Methods in Mental Health Read: Chpt 2 - Ten Fundamentals of Qualitative Research Read/Watch: Qualitative Interviewing - More Than Asking Questions and Getting Answer Read: Qualitative Research Methodologies: Ethnography Webpage: The TQRMUL Dataset Teaching Resources: User Guide Notes: Qualitative = broad umbrella term Critical realism Epistemological frameworks Phenomenology Social constructionism Methodologies that prioritise Meaning, complexity and subjectivities Social contexts Inductive approaches Researcher reflexivity Untitled 1  Key Differences between Quantitative and Qualitative Research Quantitative Qualitative Starts with specific hypotheses Starts with broad research Theory testing / deductive questions Data = numbers Inductive / theory-generating Shallow but broad data; reduction Data = words of complexity; large Ns In-depth, rich data; retention of Seeks to produce generalisable complexity; small Ns predictions or patterns Seeks to understand Aims for objectivity; values contextdependent meanings reduction of bias Recognises personal positioning Fixed analysis methods (stats / perspective; values reflexivity tests) Analytic methods based on broad principles, less fixed. Models of Researcher and “Researched” Objectivity / neutrality → reduction of “bias” Assumption that a fixed “true” score exists (e.g. IQ) Researcher separate from “researched” Qualitative Research in Mental Health The State of the Art Although still seen by some as ‘poor relation’ to quant research (hierarchies of evidence etc) Untitled 2  general acceptance now of value of qualitative approaches in mental health Mixed methods actively encouraged by many funding bodies (eg NIHR), publication in high impact journals Qualitative Research and PPI Qualitative research often overlaps with PPI (Public and Patient Involvement) in mental health research Why? Both looking at individualised lived experiences What do qualitative research and PPI have in common ? Removing the hierarchy, learning from respondents and doing work with them Position respondents as experts in their own experience Getting their view on what’s important (re. research) Begin with broad research question and narrow / funnel down Why use qualitative approaches in mental health research? 1. Experiential or lived experience focus: What does it feel like to… In-depth verbal data – interviews, blogs, social media data Mental health conditions / psychological issues: (Keating 2021; Mawson et al 2011; Yuen et al 2019) Photo-voice (photos to show how it feels to live through a particular topic and then bring them in to discuss why they took those pictures and what it tells us) Narrative - tell stories and small group of people who experiences SA - and talked through their journey of getting treatment More ‘external’ things: services / treatments / social issues (Chilman et al 2021; Lawrence et al 2021; Morant et al 2017) Untitled 3  People’s experience of using home treatment teams for mental heath crises - inpatient vs HTT 2. Informing development (of treatments, interventions, service enhancements) Stakeholder perspectives on service / treatment needs (e.g. Zerihan et al 2021) iterative intervention design and development (e.g. Milton et al 2017) Thematic analysis of data collected via interviews or focus groups Can be used to assess appropriateness of treatment - either with users or clinicians FGs with staff to discuss improvements 3. Evaluation (of treatments, interventions, service enhancements) Experiences of receiving / delivering a treatment or service (Morant et al 2023; Lucksted et al 2008; Zaccharia et al 2020) E.g. look at women who recently had a baby - who had experienced MH problems and previously given diagnosis of PD - their experience of receiving and reaching out for help for MH problem w/potentially controversial diagnosis (more specific focus): Feasibility, acceptability of an intervention (before or within a RCT) (eg McKeague et al, 2017) RCT embedded process evaluations. Why and how does a treatment work / not work? (eg Wallace et al 2016) Collecting data re. people’s experiences during a trial Usually interviews (or focus groups) with thematic analysis 4. Implementation / Service-based work: (What happens in services / regular clinical practice) what happens in services (e.g. Morant et al, 2017) Find out what’s happening in services before deciding what can be reviewed/changed Untitled 4  Going into a service and observing the environment / potentially interviewing individuals during that time how are policies / interventions implemented Methods to capture what happens: verbal and / or observational; ethnography; language-based (Anderson et al 2020; Kaminskiy & Finlay 2019; McCabe et al 2002; Quirk et al 2006) Overview of Qualitative Methods Common features: Generating / analysing non-numerical, usually linguistic data Verbal methods Interviews, focus groups Observational Ethnography (eg Quirk et al 2006) Traditionally - long and immersive Currently - briefer and focused on specific aspect Written data: Generated: Open-ended survey or questionnaire data (eg Pitman et al 2018) Analysis of pre-existing material: Blogs / Social media / internet-based data (eg Chilman et al 2021) Queries around ethics using social media data Local or national policy documents; media material Visual methods E.g. participatory photography / “photovoice” (eg Keating 2021) Note on mixed methods research Untitled 5  Qualitative studies often part of larger mixed methods research programmes Triangulation of forms of data and perspectives; answering broad research questions in different ways (see book: Cresswell & Plano Clark, 2017 ‘Designing and conducting mixed methods research’) Group task: Research questions within the area of first episode psychosis Experiences of Staff in EIP Services regarding implementation of interventions as per NICE Guidelines Evaluation of Patients Acceptability of Antipsychotics in First Episode Psychosis Qualitative Data Collection: Verbal Methods Semi-structured Interviews Studies with experiential or ideographic orientation “What does it feel like...” To access complexity of views Tensions / ambivalences Reasons Exploration of under-researched areas Continuum of structure: structured / fixed-format → in-depth In-depth: small N, experientially focussed work (IPA, narrative analysis) respondent-led / defined within a broad topic Semi-structured =middle ground Most commonly used form of interview Untitled 6  Open ended questions but clear topic guide Potential for probing / follow up questions if necessary Aim to cover same broad topics with all respondents may follow different “routes” through topic guide may deviate to explore interesting / unexpected issues Designing a Topic Guide Topic guide / interview schedule should reflect / be compatible with: 1. theoretical orientation of your study 2. your specific research question and sub-questions 3. any specific issues you wish to explore It should also: 1. be carefully piloted and revised accordingly 2. be designed to create an interview that has a natural “flow” to it 3. allow possibilities for deviation / exploration Operationalising Aims in Interview Questions Aim 1: explore experiences of taking antipsychotic medications Sampling Small samples – in-depth focus Not aiming for statistical representation Instead sampling should be theoretically driven: range of participants / “stakeholders” / settings to reflect various relevant “positionings” in relation to research question How many interviews do I need? Depends on theoretical orientation, aims and resources N≤10: IPA, narrative analysis Untitled 7  N=12-25: typical in qual papers using thematic analysis N up to 60: comparison of groups / contexts Trade off of size and depth There are no definitive answers about sample size… Rather some concepts to draw on in thinking about and justifying your decisions It’s often better to specify a range / approx. N to allow some flexibility for what happens when you collect the data. Guidance / Principles to Guide Sample Size Decisions Data saturation = the point at which you cease to hear new things (?) Not very clear - you never truly know when you stop hearing something new Guest et al (2006): ‘data saturation’ reached around N=12 if interviews semi-structured and sample homogenous. Up to N=18 if sample more diverse Information Power (Malterud et al 2016) = the amount and depth of information a data sample holds that is relevant to the research question. May depend on: study aims sample specificity quality / depth of dialogue (eg focus on the topic; depth of ideas) Lower quality may require more data Quantity of data (eg length of each interview) analytic strategy. Sampling Strategies Untitled 8  Purposive Not random or ‘representative’ (in statistical sense) Forms of purposive sampling Maximal variation sampling Aim for broad range of participants on variables that may be relevant to the research qn. E.g. (1) pro-medication, (2) indifferent, (3) against medication E.g. (1) tablet medication, (2) depot medication May include clinical variables; socio-demographics (age, gender, ethnicity); other ‘positions’ relevant to the topic. Other possibilities (less common): Extreme case sampling Typical case sampling Critical cases Convenience sampling - last resort/deterred method Snowball sampling (e.g. for hard to reach participants / marginalised groups) Developing a Qualitative Research Study Experiences of Staff in EIP Services Regarding Implementation of Psychological Interventions as Per NICE Guidelines for FEP NHS EIP staff in England who deliver interventions. Specifically individuals who deliver the intervention → to gain a deeper insight into any discrepancies between guidelines and the realities of implementing them and the effect they feel that has on treatment Untitled 9  Methods: Focus Groups - mix from different services to get varied experiences/perspectives Learning outcomes for Teaching Session 12 Understand how qualitative data can be analysed using thematic analysis Be able to conduct the basic techniques of thematic analysis, including initial coding and theme development Be aware of other specific forms of qualitative data analysis and the key similarities and differences between these. Develop a broad critical understanding of the field of qualitative research in mental health Thematic Analysis Generic method; not strictly tied to any theory / epistemology Exploration of patterns of meaning in the data Popularised by Braun & Clark (eg 2006; 2013, 2021), now a mainstay of qualitative research Good basic method to learn and apply in mental health research Basic techniques of TA form basis of other pattern-based methods:– IPA; grounded theory; narrative approaches So what does TA involve? Six stage process described by Braun & Clarke (2006; 2021) - But better thought of as spiralling rather than linear process (overlapping processes esp in 2-5) 1.Familiarisation – Reading transcripts; noting ideas of interest 2.Data coding– Working through transcripts carefully and systematically; close, descriptive level 3.Generating initial themes – Collating codes into more abstract themes based on underlying concepts, similarities or meanings; more interpretative 4.Reviewing and developing themes– Hierarchical structure of themes / subthemes / codes– Exploring the content and patterning of themes across the Untitled 10  data se 5.Refining, defining and naming themes 6.Writing-up Beginning Thematic Analysis Researcher reflexivity = thinking critically about the assumptions, decisions or interpretations you make / bring into the research process A key tool in qualitative research [cf: underlying epistemological stance: recognising the researcher’s role in knowledge construction.] Key Characteristics of Thematic Analysis: PROCESSES Cyclical: – moving back and forward between data and ideas; between different stages of analysis – Analysis and writing up are blurred processes Not about following a fixed recipe – Every analysis is different; there are different forms of TA – Important to understand basic principles (and underlying epistemological assumptions) in order to conduct a form of TA that suits your study / aims / size of data set etc Time consuming! Can be done manually or within computer software (eg Nvivo) Key Characteristics of Thematic Analysis: OUTPUTS: What does the final product look like? Qualitative results sections are organised according to themes (though sometimes these are actually topic domains) Should do more than just describe what was said Analytic commentary should include interpretation – (how specific pieces of data are underpinned by abstract ideas or concepts) Untitled 11  Data extracts illustrate how themes are expressed by 9 research participants in their own words. Morant, Long et al (2023) Experiences of reduction and discontinuation of antipsychotics [Other thematic analysis example: Lawrence et al., 2023; Morant et al 2017; Zacharia et al 2020; Zerihun et al 2021] 2.1 Medication: Learning about the implications of reduction For many participants experiencing lower medication doses helped them understand the role, impacts, and value of medication for them personally or ascertain a personally optimum dose. Several thought that without the trial their psychiatrist would not have supported this. Some extended these insights into differentiating between medication effects, their illness, and their underlying self-concept. A sense of having more options about medication was significant or empowering for some, and many were highly engaged with reduction processes and detailed reflections about dose levels. “From 20 down to 15 or a lower dose wasn’t so difficult… anything below 10 was more difficult for me... I had a lot of anxiety when I went down to 5 and 2.5… I wasn’t feeling so good on those doses…It’s hard to explain how I felt, just uncomfortable in myself, lack of self-satisfaction, more nervous, more wary, more alert of the outside world. I’d say things become more real like my problems and my aspirations. Everything becomes a lot more real, a lot more serious… 7.5 is OK but sometimes I don’t feel as good on 7.5 as I would say with more of a dose.” (P12005) Grounded Theory: Focus on induction and theory development (Glaser & Strauss, 1967) Inductive model of research– theory development ‘bottom up’ from the data (contrast to hypothetico-deductive model) Characterised by iterative, cyclical, exploratory research processes – Initial broad research qns, refined and progressive focussed through the project; – Foci of data collection and analysis refined / may change through the project (not defined in specific term a priori) Untitled 12  – Data collection and analysis often overlap cyclically (insights from early data inform further data collection) 13 Example paper Lucksted et al (2008) Grounded theory: inductive techniques that inform thematic analysis Full grounded theory work rare (GT ‘lite’) but influence of GT in current analytic approaches, especially inductive, data-exploratory processes Many of basic processes of TA come from GT: Reflexive field notes that are incorporated into analysis – Researchers’ experiences as ‘valid data’; reflexivity Exploratory techniques for later stages of TA, eg: – negative case analysis; refining names of themes (often using respondents’ own terms) – conceptual model development (how themes relate to each other etc) ‘Saturation’: – when to stop data collection, when to stop analysis Interpretative Phenomenological Analysis (IPA) in-depth focus on inner worlds (example paper Mawson et al, 2011) Focus on subjective experiences and meanings (P) Analysis is researcher’s interpretation of participant’s account of experiences (I) Analysis is thematic / meaning-based but fine-grained and ideographic in orientation: – Detailed analysis of individual cases first, then look for common themes – Aim to develop ‘master themes’ to capture underlying phenomenological issues “Less is more”: In-depth analysis of small number of participants preferable to shallower / more descriptive analysis of larger N. Narrative Analysis dynamic focus on stories; often to make sense of life events / transitions Narrative theory Untitled 13  – focus on self and identity – Stories central to identity construction: identity as a life story – How narratives help us understand / find meaning / bring order Data collection. In-depth interviews designed to: – Allow respondent to tell their story of.. – Follow dynamic structure: before – during - after key event Analysis: – (Similar to IPA): In-depth, inner focus (small N), focus on details & uniqueness of each individual’s story – narrative tone and style, imagery, themes, using of culturally available narratives. – More focus on ordering of themes within structure of narrative 17 Narrative Analysis: example paper: Yuen, Billings and Morant (2019) Talking about sexual assault (MSc dissertation) Research Qn: What helps and hinders victims of sexual assault to start talking about the psychological impact? Interviews with 6 women survivors of sexual assault– Narrative interviews: temporally organised; minimal prompts; participant given freedom to decide what to prioritize in telling their story. – Asked to talk about experiences of first talking about the impact of the assault on their mental health: what led up to this; what helped and hindered. Language-based approaches Conversation analysis: (interpersonal, organisational and societal) (eg McCabe et al 2002; Anderson et al 2020; Kaminskiy & Finlay 2019) – Talk in social interactions – Often institutional (eg healthcare) settings Untitled 14  – Often uses naturally occurring (eg health consultations) rather than researcher-generated (eg interview) data Others: Discourse Analysis: (interpersonal focus / social psychology) – Role of language in constructing social identities; analysis of discursive processes, positionings, rhetorical devices etc (Potter & Wetherell,1987) Foucauldian Discourse Analysis: (societal focus) – How language shapes and regulates our social world and experiences – issues of broad social power, control and social practices. Key features of language-based analyses Detailed transcripts, ‘Jeffersonian’ style: – more detailed than standard ‘playscript’ transcriptions – aim to capture tiny nuances of expression, pauses, tone, how things said etc Analysis often very fine-grained; more selective Focus on what language does Less focus on charting patterns across the whole data set– Analysis and analytic arguments often based on selective data extracts that capture key interactional practices. Example paper: Conversation analysis (CA) Anderson, Stone, Low & Bloch 2020 Topic and rationale: Palliative care Clear, honest and sensitive communication by practitioners is important in helping families prepare for death. But there is prognostic uncertainty – How can / is this managed in end-of-life conversations with relatives? Data and Analysis: CA of 23 audio-recorded conversations between hospice clinicians and relatives about likely timing of death Untitled 15  Focus on how uncertainty is managed; how time is referenced; how prognosis (time till death) is referenced Implications: Research like this can be used in professional training to illustrate how prognostic uncertainty can be sensitively communicated. Untitled 16