RMHS - lecture 7 - analysing and presenting qualitative data

ALSO LOOK AT THE SLIDES FROM THE LECTURE FROM 26/9

• ESPECIALLY FOR EXPLANATION DEDUCTIVE VS INDUCTIVE

• ALL THE TERMS

• THE 6 DIFFERENT STEPS

• THE 3 DIFFERENT STEPS (OPEN CODING, AXIAL, SELECTIVE)

principles of qualitative research

• subjectivity

• transferability 

• iterative process

• inductive reasoning 

• reflexivity 

• data saturation 

• purposeful sampling 

subjectivity / contextual understaning

• Qualitative analysis values the unique perspectives of individuals and seeks to understand phenomena within their natural context.

purposeful sampling

• participants are selected based on their relevance to the research question, not for statistical representation 

transferability

findings aim to be transferable to similar contexts rather than universally generalizable.

• qualitative research often very little N so hard / not possible to generalize

  • no statistical power 

iterative process

• collect and analyzing goes hand in hand

  • important when you make a research plan 

• in quantitative first you collect and after you analyze 

inductive reasoning

• moving from specific to general

• develop theory → find patterns → observe and record phenomena

• the aim is to explore ideas

• it’s open ended (free)

deductive reasoning

• hypothesis testing study

• hypothesis → examine relationship among variables → prove / disprove hypothesis 

• moving from general to specific

• narrowed, defined

reflexivity

• researchers must reflect on their own role, assumptions and influence throughout the research

• this includes being aware of how the presence of the researcher and their interpretation may shape findings.

data saturation

• sampling continuous until no new insights are gained, ensuring comprehensive coverage of the topic.

• data saturation is the point in qualitative research when collecting more data yields no new insights, themes or patters.

ethical principles during data analysis

• data must be analysed honestly and transparently

• avoid cherry picking or misrepresenting participant voices

• include diverse perspectives and acknowledge contradictions or limitations

• findings should be shared in ways that benefit participants and communities

• avoid stigmatization or harm through careful contextualization and dissemination

• researchers should consider how their work might be interpreted or used by others. 

core principles as defined by David Gray 

• exam question; What are the general principles of qualitative analyses?

• inductive reasoning

  • Researchers build patterns and theories directly from data without relying on pre-existing hypotheses.

• contextual understanding

  • Data is interpreted within social, cultural, and situational contexts to reflect real experiences accurately.

• reflexivity and ethics

  • Researchers continuously reflect on biases and maintain ethical sensitivity to protect participants.

• iterative and transparant

  • Repeated cycles of coding and refining data ensure clarity and rigor in research documentation.

criticism on qualitative research

• Gray page 750

• no widely accepted rules how data should be analyzed

• lacking methodological rigour

• prone to researchers subjectivity

• small cases and limited evidence → this is why you need to explain the context of the research.

general principles of qualitative research

• always describe the data

• code the data (process of disaggregating the data into smaller parts)

• always integrate codes into meaning

• reflexivity of the researcher

why do you open code the data, and what are open codes

• open codes are → a word or short phrase or even a short story that represents the essence or key attribute of information in transcript

• used to codify or categorize data (breakdown complex data into manageable pieces) 

• coding is the process of organizing the data into chunks that are alike

step 1; open coding definition

• open coding is the naming and categorizing phenomena through close examination of data through a process of making constant comparison.

• you do not start with a hypothesis but only the raw data

how to do open coding

• go through data line by line identifying key ideas, actions or events.

• for each meaningful segment, you assign a code → a label that captures what is happening in that piece of data

• ask the data specific set of questions

  • compare text fragments on similarities / differences

  • what is the underlying concept 

  • label fragments with keywords, concepts and categories. include as many as possible.

content analysis

• follows deductive approach

• start with theory (hypothesis) → find your hypothesis

• Making inferences about data (usually text) by systematically and objectively identifying special characteristics (classes or categories)

• content analysis is the same as framework analysis

• useful for analyzing documents, media, open ended surveys.

• when open coding a content analysis the codes are always pre established on a theoretical model and a pre established set of criteria!!! (if it follows deductive approach)

facts about content analysis 

thematic analysis

• follows inductive approach 

• A method for identifying, analyzing and reporting patterns (themes) within data; a form of pattern recognition

• inductive thematic analysis has 6 phases → 1. familiarization 2. initial coding (fragment data) 3. theme searching 4. theme review 5. theme naming and definition 6. reporting

• aim is to find answers to your research question

content analysis and thematic analysis both start with open coding, what is the difference

grounded theory

• the discovery and construction of theory through the open analysis of data

• the aim is to build new theory

• procedure is the same as thematic analysis

facts about thematic analysis

steps in thematic analysis

• transcribe  the data 

• read and reflect → become familiar with the text

• explore and play → form hunches, experiment with themes

• code and connect → code for main / sub themes

• review and refine → create audit trial of codes

• develop an analytical account → describe, compare, defend, extent

• then → describe how individuals talked about a theme, compare and for similarities, relate and look for connections, contend a range of arguments / evidence that answer RQ, defend these arguments from data and coding, extend the rules to a broader context.

narrative analysis

• an ideal way of capturing the lived experiences of participants

conversational analysis

• formal analysis of everyday conversation.

• seeks to specify the formal principles and mechanisms with which participants express themselves.

three steps to code and connect 

1. open coding 

2. axial coding 

3. selective coding 

step 2; axial coding

• this the the next step after open coding

• the process of relating categories to their subcategories; linking categories at the level for their properties and dimensions

• gray says about axial coding → stage in which the fractured data are put back together again and make connection between categories and subcategories 

• this stage is interpretive, requiring the researcher to move back and forth between data and emerging categories to ensure coherence and depth. 

step 3; selective coding

• highest level of abstraction 

• it is the process of selecting core categories from the data in order to develop a single storyline or to form a theory. 

pitfalls of content and thematic analysis

• content → too much looking for evidence for relation (closed mind), can miss some information

• thematic → accepting too vague relations as the truth 

horizontal or vertical analysis

• you need to do both to grasp the meaning of your data

• Sometimes you are more content/horizontal focussed

• Sometimes you are more narrative/grounded/vertical focussed

horizontal analysis

• Focussed on aggregation and comparison of content of data across different interviews (or other transcripts). Pay attention to diversity (both majority and minority of views count (between respondents)

• goal → identify patterns, similarities and difference across participants, cases, texts 

vertical analysis

• Focused on understanding the essence of individual interviews: ‘the narrative’ (or other data/transcripts). You need the unique line of arguing of individuals and their priorities. (compare within a person)

• goal → to understand the internal logic, structure and meaning within each individual case, participant, text. 

when is saturation reached

• sampling redundancy

• no new themes emerge from the data

• check with deviant case (case very different from the others and see if they fit the data you already have) 

essentials qualitative analysis 

• starts with describing the data 

• disintegrate by coding and integrating 

• never do it alone 

• the first step always open coding

  • no matter it is is inductive / deductive approach

  • next steps depend on approach

  • use code book to describe categories.

credibility / internal validity

• truth value of evidence i.e. a strong link between data and theoretical ideas developed?

• the extent to which the study findings are trustworthy and believable to others

  how

  • Triangulation of data sources, researches triangulation

  • Prolonged data collection until saturation is researched

  • Member checking of summaries (send summaries of interviews to responders, how do they interprete it?).

transferability / external validity

• can not generalize the data!

• applicability of evidence outside context. Are findings generalizable to other social settings?

• The extent to which the findings can be transferred or applied in different setting

  how

  • Make findings meaningful by describing them and their context in detail

  • Explain the sample strategy (typical case sampling, maximum variation sampling)

  • Discuss the findings resonance with literature from different setting

dependability / internal reliability

• consistency of evidence through the use of audit trails

• The extent to which the findings are consistent in relation to the context in which they were generated.

  how?

  • Saturation: collection data until no new themes emerge

  • Iterative data collection and analysis; continuously analyze data for further data collection

  • Emergent research design (flexible and open toward the process and topics

confirmability / objectivity

• neutrality of evidence

• The extent to which the findings are based on the study’s participants and setÝngs instead of

  researchers bias.

  how

  • Search data and/or literature for evidence that disconforms

  • Discussion with peers/experts

  • Reflexivity; a diary to reflect on the process

  • Audit trial; document the staps and decisions take

reflexivity

• critically reflecting on yourself as a researcher

• be aware that you as a researcher are not a natural observer.

epistemological reflexivity

• Researchers reflect on their assumptions about the world and about the nature of knowledge

personal reflexivity

• Researcher reflects upon how their personal values, attitudes, beliefs and aims have served to shape the research