Fichas
Qualitative data research
Fichas del conjunto (17)
- 1When QD research methods are appropriateTo understand an area where little is known, complex situations.
Learn the way participants understand.
Discover a new theory based on empirical results.
Understand a phenomenon in detail - 2What is QD research methods?Approach that is human focused.
Explores questions that ask why and the processes that occur in society.
Examines meanings individuals give to events/occurrences
Provides a way to understand why people do and think what they do and think. - 3Truths inherent in researchObjective: that which multiple people have primary access to (income, children, etc).
Subjective: that which is only available to the person(fear, remorse, etc).
Normative/evaluative: that which entails value judgements or judgements about what is ‘normal’ (adequate income, well behaved children). - 4Characteristics of QD research methodsConceptual: concerned with understanding human behaviour from informant’s perspective
Methodological: Data are collected through participant observation, interviews. Data are analysed by themes from descriptions by informants. Data are reported in the language of the informant. - 5Understanding QD researchEpistemological positions – positivist or constructionalist views
Methodological frameworks – ethnography
Methods – observation - 6SamplingTheoretical/purposive sampling – meet inclusion criteria for the study
Maximum variation – specific purposive sample reflecting diversity
Snowball – initial participants lead to new recruits - 7Approach – phenomenologyInterest in the subjective experience, how people experience their life and what it means to them, how people construct meaning and communicate, understand people’s experience and able to describe and communicate to others, and epoche (bracketing) or reflexivity.
- 8Approach – ethnographySocial interactions – in groups
Data collected taking a holistic approach (observations that are detailed and aim is to understand how the group perceives their world). - 9Approach – grounded theoryRather than testing theory, about ‘discovering’ theory by examining data closely. More than descriptive – looking to understand process (how something occurs).
- 10Design QD researchLook at the purpose of the research, how does it relate to other work in the field, what are the appropriate methods to answer questions, and ethical issues to be considered.
- 11Methods of QD research – interviewsIndividual (structured, semi-structured, unstructured).
Focus groups (homogenous groups 6-12 participants, efficient but depth and detail is limited that individual interviews) - 12Methods of QD research – observationsParticipant observations – researcher participates
Structured observations – researcher observes
Observational episodes – progressive focus informed by clarity through observations to research questions - 13Methods of QD research – document analysisMaterial produced prior to or during research, provides a context of a study, can be a basis for exploration, confidentiality to be considered.
- 14Reliability and validity of QD research methodsCriteria to judge the quality – credibility, transferability, dependability, authenticity.
Procedures – auditing, member checking, debriefing. - 15Analysis – preliminary data analysisInitial questioning of data to determine themes, issues, what is occurring.
Aim to identify gaps by critiquing data as it occurs – make connections between issues and research topic.
Analysis is ongoing during data collection
Critique data to determine whether data is broad enough in relation to research topic. - 16Analysis – Thematic analysisInterview, obs and document analysis
Process – engage data, discuss themes, think of context surrounding the themes.
Coding – to manage large databases. - 17Coding in grounded theory analysisRead material over to get a ‘feel’ for the data (overall understanding)
Open coding – meaningful units are identified and given a label/catergory
Substantive coding – interrogating the data
Initial coding is to gain sense of the data – detail, complexity and variation.
Coding shifts from descriptive to analytical, fewer categories emerge until data is saturated (nothing new).
Axial coding – categories from initial open coding are refined through finding r/ships between categories and examine for redundancy.
Memos – extremely useful (thoughts and thought processes throughout analysis).
Rigour – goodness of fit: how well do the categories apply to the data (but not forced). Seek external validation, use of reflexivity (researcher acknowledges their role).
Información
- Descripción
- Honours RMD Fichas sobre Qualitative data research, creado por Sarah Forster el 13/10/2015.
- Creado por
- Sarah Forster · oct 2015
- Actualizado
- 19 feb 2016
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