Qualitative Research 1 PDF

**[Core Principles in mental health Research]** **[Qualitative Research 1]** [Different ways of thinking:] Ontology Epistemology Methodology [Ontological Positions:] - Realism: A fixed reality independent of our study: A Single Truth - Critical Realism: A pre-existing reality, but our know...

**[Core Principles in mental health Research]** **[Qualitative Research 1]** [Different ways of thinking:] Ontology Epistemology Methodology [Ontological Positions:] - Realism: A fixed reality independent of our study: A Single Truth - Critical Realism: A pre-existing reality, but our knowledge of it depends on our perspective - Relativism: Reality depends entirely on human interpretation: Multiple Truths [Epistemological Frameworks:] - Phenomenology: - Subjective experience; uniqueness - Interpretative phenomenological Analysis (IPA) - Social Constructionism: - Anti-essentialism/rejection of universal knowledge - Instead, situated understandings of people as social beings - Role of language - Critical - Discourse Analysis [Methodological Approaches:] - Meaning and Complexity - Content, forms of expression - Subjectivities - Defining things from research participants' point of view - Social Contexts - Paying attention to the social setting of the research [Inductive Approach:] - Exploratory - Broad Research questions rather than specific hypotheses - Generating new insights/understanding not testing a hypothesis [Models of research & researched"] - The perspective or positioning of the researcher is important - Research output is jointly constructed - Role of researcher reflexivity - Research "with" not "on" participants [Key differences between quantitative and qualitative research:] Quantitative: - Starts with specific hypotheses - Theory testing/deductive - Data = numbers - Shallow but broad data; reduction of complexity; large Ns - Seeks to produce generalisable predictions or patterns - Aims for objectivity; values reduction of bias - Fixed analysis methods (stats tests) Qualitative: - Starts with broad research questions - Inductive/theory-generating - Data = words - In-depth, rich data; retention of complexity; small Ns - Seeks to understand context-dependent meanings - Recognises personal positioning/perspective, values reflexivity - 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) - Researchers 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), general acceptance now of value of qualitative approaches in mental health - Mixed methods actively encouraged by many funding bodies, publication in high impact journals [Why use qualitative approaches in mental health research:] - Experimental or lived experience focus: - In-depth verbal data -- interviews, blogs, social media data - Mental health conditions / psychological issues: (Keating 2021; Mawson et - More 'external' things: services / treatments / social issues (Chilman et al - Informing development: - Stakeholder perspectives on service/treatment needs - Iterative intervention design and development - Thematic analysis of data collected via interviews or focus groups - Evaluation: - Experiences of receiving/delivering a treatment or service - Feasibility, acceptability of an intervention (before or within a RCT) - RCT embedded process evaluations. Why and how does a treatment work/not work? - Usually interviews (or focus groups) with thematic analysis - Implementation/Service-based work: - What happens in services - How are policies/interventions implemented - Methods to capture what happens: verbal and/or observational; ethnography; language-based [Overview of Qualitative Methods:] - Verbal Methods: - Interviews, focus groups - Observational - Ethnography - Written data: - Generated - Open-ended survey or questionnaire data - Analysis of pre-existing material - Blogs/social media/internet-based data - Local or national policy documents; media material - Visual methods - E.g., participatory photography [A quick note on mixed methods research:] - Qualitative studies often part of larger mixed methods research programmed - Triangulation of forms of data and perspectives; answering broad research questions in different ways [Semi-structured or in-depth interviews:] - Studies with experiential or ideographic orientation - "what does it feel like..." - To access complexity of views - Tensions/Ambivalence - Reasons - Exploration of under-researched areas - Continuum of structure - Structured - Fixed format - In-depth: small N, experientially focused work - Open ended questions but clear topic guide - Potential for probing/follow up questions - 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:] - Interview schedule/topic guide should reflect: 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 [Sampling:] - Small samples -- in-depth focus - Not aiming for statistical representation - Sampling should be theoretically driven - Range of participants/stakeholders/settings [How many interviews?] - Depends on theoretical orientation, aims and resources - N ≤10: IPA, narrative analysis - 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 answrs about sample size - Rather some concepts to draw on in thinking about justifying your decisions - 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 - **Guest et al., 2006:** data saturation reaches 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 of data - Quality/depth of language - Analytic strategy [Sampling Strategies:] - Purposive - Not random or representative Forms of purposive sampling: - Maximal Variation Sampling: - Aim for broad range of participants on variables that may be relevant to the research - May include clinical variables; socio-demographics; other "positions" relevant to the topic - Other possibilities: - Extreme case sampling - Typical case sampling - Critical cases - Convenience sampling - Snowball sampling