Qualitative Research Methods

Recording Interviews

• The lesson will be divided into two parts: Recording of interviews and data analysis (before morning tea).

  • Critical appraisal (after morning tea).

• The most common ways to documentation of what happened in the interview is to audio record, but over time, technology have started to use video recording as well to complement any note taking that they may take to supplement that.

• Purpose of recording:

  • Allow returning to the interview to clarify points or review non-verbal cues.

  • Create a transcript for detailed analysis.

  • Analyze data later to identify themes and patterns.

  • Give full attention to interviewees, fostering a more natural conversation.

• Problem with taking notes:

  • Losing eye contact, which can affect rapport.

  • Missing important details due to divided attention.

  • Reminds people they are being interviewed, potentially influencing their responses.

Recording Tips

• Use the best quality equipment available.

  • Access university equipment if possible to ensure professional-grade recording.

  • Consider project funding for equipment if high-quality tools are necessary.

  • Ensure the equipment is reliable to avoid data loss.

• Use a separate microphone.

  • Position it closer to the interviewee for better audio capture and clarity.

• Be careful with cables to avoid safety hazards.

• Pre-test the recorder before and at the interview location to ensure it functions correctly.

• Sit close to the person being interviewed.

  • Maintain comfortable proxemics; don't invade their personal space to make them uneasy.

• Check that everyone's voices can be heard clearly.

  • Adjust volume as needed to ensure balanced audio levels.

• Avoid bright lights that may cause discomfort.

• Strive for a comfortable environment to encourage open communication.

• Make a copy of the recording and store it separately to prevent loss of data.

• Choose a private setting unless a public space is unavoidable to reduce distractions.

• Minimize distractions to ensure a relaxed atmosphere.

• Ensure quietness for recording while retaining relevant background sounds.

Soundscapes

• Background sounds provide contextual information, enriching the understanding of the interview.

• Example given of a recording with dogs barking, water gurgling, and plane droning to contextualize the speaker's words.

• Three main types of soundscapes:

  • Human-generated sounds:

    • Electromechanical (traffic, trains, planes).

    • Physiological (coughing, sneezing).

    • Controlled (recorded music).

    • Incidental sounds.

  • Animal sounds:

    • Birds, dogs, insects.

  • Non-biological sounds:

    • Wind, rain, thunder, lightning.

• Soundscapes can be used as data to contextualize interviews, providing a richer understanding of the context.

Transcriptions

• A transcript is a verbatim, word-for-word written record of what was said, capturing every detail.

• Who should produce it?

  • The interviewer (for familiarity with data and nuances).

  • A hired person (e.g., support staff) to save time.

  • Technology (e.g., YouTube transcripts) for efficiency.

• Different types of transcripts:

  • Gisted transcript (summary), providing a condensed overview.

  • Basic level transcription (what was said), capturing the main content.

  • Advanced transcription (including ums, ahs, pauses, and symbols for intonation and volume), providing comprehensive detail.

• Elements to include in a transcript:

  • Spoken words, accurately transcribed.

  • Non-verbal behavior (in brackets) to provide context.

  • Other sounds to capture the complete environment.

  • Comments on group interactions to understand dynamics.

• Benefits of transcribing:

  • Good recording, ensuring accuracy.

  • Common practice in qualitative research.

  • Reasonable accuracy, especially with careful review.

  • Deep understanding of data, fostering insights.

  • Full attention to the interviewee, enhancing data quality.

• Potential errors:

  • Misheard words, leading to inaccuracies.

  • Similar-sounding words with different meanings ('adopt' vs. 'adapt').

  • Jargon or unfamiliar terms, example: milieu, postieu.

Using Transcripts

• Import data into software for organization and analysis, streamlining the process.

• Remove identifiers to maintain privacy. Separate file with person's name and assigned number.

• Clean the data, but avoid over-cleaning to retain potentially relevant information.

• Create summary tables to organize data, facilitating analysis.

Qualitative Data Analysis

• Goal: Identify, explore, and give meaning to patterns in the data to address study questions, enriching the research.

• Process: Ongoing, adaptable, and emergent, allowing flexibility.

• Four main types (modes) of qualitative analysis:

  • Iterative.

  • Enumerative.

  • Investigative.

  • Subjective.

Iterative Data Analysis

• Repetitive process to advance understanding, refining insights.

• Model (1998):

  • Notice things, focusing on key observations.

  • Gather information, collecting relevant details.

  • Sort into groups, organizing data thematically.

  • Reflect, critically evaluating the analysis.

  • The reflection informs future observations, guiding further research.

• Types of iterative analysis:

  • Analytic induction (exception to the no-hypothesis rule in qualitative research):

    • Start with a question, initiating the investigation.

    • Generate a hypothesis, proposing an explanation.

    • Examine data, testing the hypothesis.

    • If data supports the hypothesis, confirm it; if not, revise the question or hypothesis.

  • General inductive approach (conventional thematic analysis):

    • Identify core meanings of what people are saying to address the study question, extracting essence.

General Inductive Approach (Assignment Focus) - Seven Stages

*   Reading, coding, and grouping steps.

• Reading:

  • Read the transcript slowly, carefully, and multiple times.

  • Peruse to truly know the data, gaining familiarity.

• Coding:

  • Label meaningful text segments (sentences, phrases, paragraphs).

  • Summarize what participants are saying, capturing essence.

  • Iterative process, refining codes as understanding deepens.

  • Coding is fracturing the data into parts, breaking it down for analysis.

  • Example: patient says, "My heart starts to beat hard and irregularly, my limbs are shaking and my mouth gets dry, I think I'm going to die."

    • Codes: irregular heartbeat, shaking limbs, dry mouth.

  • In vivo coding: Using the participants' own words for coding to maintain authenticity.

  • Coding independently or with others (compare codes) to foster collaboration.

  • Coding manually or using a computer (for data management), streamlining the process.

• Grouping:

  • Categorize coded segments into groups, identifying themes.

  • Groups are themes that emerge from the data.

  • Theme: An abstract unit that combines codes into a group, providing insight.

  • Distinction between pattern (descriptive regularity) and theme (interpretation of the pattern).

Good Themes

• Reflect the purpose of the research, aligning with goals.

• Be specific and clear, avoiding ambiguity.

• Be sensitive to nuances in the data.

• Fit conceptually, being coherent and relevant.

• Distinguish between levels of themes (major, minor) to highlight hierarchy.

Tips for Identifying Codes and Themes

• Use their own words (in vivo), preserving authenticity.

• Look for metaphors that add depth.

• Note repetitions for emphasis.

• Analyze relationships (connectors like 'and' and 'but') to understand connections.

• Be mindful of the literature to stay informed.

• Consider what is present and what is absent (marked vs. unmarked text), noting what's emphasized and what's omitted.

• Analyze connections and transitions between ideas, sentences, and paragraphs to understand flow.

• Cutting and pasting on paper with colour coding also helps if doing things manually, aiding organization.

Example

• Example: "The doctor smiled at me and with no false pride for her selfless concern for my well-being she thanked me for my gift."

  • Doctor smiled: friendliness, warmth.

  • No false pride: humility.

  • Selfless concern: altruism.

  • Thanked me for my gift: gratitude.

  • Theme: virtue - stable trait of good character.

Stages of General Inductive Approach - Continued

*   Reading, coding, and grouping steps.

• Describe the theme:

  • Summarize the themes in a few sentences, providing a brief overview.

• Connecting:

  • Look for how themes connect with each other, understanding relationships.

  • Diagram with arrows or flowcharts to represent relationships visually.

  • Link back to the study question, staying focused.

• Interrogate:

  • Looking at how each of your themes might vary across different groups based on age, gender, ethnicity, what have you.

• Interpret the themes in the context of the literature:

  • Analyze and make sense of everything, enhancing understanding.

Levels of Theme Interpretation

• Simple restatement of the content, providing a basic summary.

• Gentle interpretation with a focus on description, adding context.

• Deeper interpretation (use of language, metaphors, intuition), enriching understanding.

• Potential for over- or under-interpretation, requiring caution.

• Be able to justify what you come up with, supporting claims.

Apophenia

• Perceiving patterns that don't exist, which can lead to false conclusions.

• Risk of seeing something in the data that's not there.

• Under-interpreting: Failing to pick up on meanings that are there.

• Deeper interoperation is important but it has some risks.

Reflexive Thematic Analysis

• Conventional means the general inductive approach, where the meaning comes out of the data directly. Conventional thematic approach equated to general inductive approach.

• Different kind of thematic analysis: Directed by pre-existing understanding in Iterature and theory.

Differences between Thematic Analysis and a Content Analysis.

• Content Analysis:

  • What was said mainly-describing.

• Thematic Analysis:

  • More interpretation, interpretation of the meanings and significance of the content and context of the data.

Salient Theme Framework

• Frequent is to do with recurrent, but if it's infrequent but more important, we need to consider it as a key aspect to the topic.

Conventional vs Reflexive Thematic Analysis

• Conventional: Researcher- neutral position, objective.

• Reflexive: researcher engages more actively with their own emotions and biases. This is more constructionist approach.

Other Approaches in Qualitative analysis

• Enumerative Data Analysis approach based on a content analysis. Enumerative, which is numerate, count counting investigating and very subjective types.
  
  

  * Semi-Quantitative is to be counting frequency.
  
  

  * Doesn't mean more important if repeated. Although could do.
  
  

  * Frequency doesn't mean important.

• Grounded Theory:
  
  

  * Highly inductive- open and to data emerge.
  
  

  * There is different versions of theory as that can influence a theory with the data that is grounded in the data.

• Framework approach:
  
  

  * Inductive and Deductive.
  
  

  * Maps themes from predetermined coders. deductive and emergent codes and then you map the themes against individual cases and I'll show you how that looks.

• Phenomenology- be reflexive:
  
  

  * Pure phenomonlogoy Essential Nature. How do we do this, you have to be reflexive, like reflect on yourself and how other people are looking at things, suspend your biases, so try to be self aware of our biases that