Qualitative Research

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Qualitative Research

• Liu Jianda

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What is Qualitative Research?

• Collecting, analyzing, and interpreting data by
  observing what people do and say
• Examples
• Focus groups
• Depth interviews
• Projective techniques
• Observation (human or mechanical)

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What is Qualitative Data?

• Verbal
• interviews
• focus groups
• Speeches
• Written
• diaries
• Letters
• Books

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Data gathering strategies

• Taking notes, either as a participant-observer or
  an observer.
• Conducting unstructured or semi-structured
  interviews (a type of survey) or focus groups and
  transcribing them.
• Obtaining relevant documents or audiovisual
  images.
• The researcher may combine the use of two or more
  data gathering techniques, and may also keep a
  journal to record his or her own reflections and
  tentative interpretations as the study
  progresses.

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Differences in Research Approaches

• QUALITATIVE
• Objective to gain a deeper understanding of
  behaviors, inner thoughts feelings, and
  meanings
• Nature of Data soft data such as words or
  text (verbatims), audio, pictures, film

• QUANTITATIVE
• Objective to quantify sample findings and
  generalize these results to the population of
  interest
• Nature of Data mostly numerical

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Differences in Research Approaches

• QUANTITATIVE
• Sample large statistical samples
• Data Collection highly structured,
  standardized
• Data Analysis statistical
• Outcome recommend a final course of action

• QUALITATIVE
• Sample small representative samples, often
  judgment sampling
• Data Collection unstructured or semi-structured
  
• Data Analysis data synthesis, interpretation,
  themes
• Outcome develop an initial or deeper
  understanding

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Different Approaches to Research4 Rs versus
4 Ps
Research Task 4 Rs (Quantitative) 4 Ps (Qualitative)
How do I select research subjects? Representativeness Purposefulness
How do I work with subjects to get data? (non-)Reactivity Participation
How do I avoid arbitrary findings? Reliability Process
How do I convince others of my findings? Replicability Particularity
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Tasks by Research Activity
Research Task Research Activity
How do I select research subjects? Data Collection
How do I work with subjects to get data? Data Collection
How do I avoid arbitrary findings? Data Analysis
How do I convince others of my findings? Data Analysis
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Quantitative and Qualitative Research Activities
Research Activity Quantitative Approaches Qualitative Approaches
Data Collection Representativeness Random samples of pre-determined groups Reactivity Fixed data collection instruments Purposefulness Sites and subjects sampled according to needs Participation Flexible data collection strategies
Data Analysis -Reliability Hypothesis testing via statistical inference -Replicability Standard reporting formats (tables, etc.) -Process Iterative coding and memoing to refine results -Particularity Narrative reports of findings in context
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Stages in analysing qualitative data

• Transcription
• Reading and Familiarisation
• Coding
• Themes/sub themes
• Develop into networks, concepts, models

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The Data Analysis Process
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(No Transcript)
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Methods of Analysis

• Content analysis
• Discourse Analysis
• Narrative analysis
• Grounded theory

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Grounded Theory

• Developed by Glaser Strauss (1967) during
  investigations of institutional care of
  terminally ill patients
• Involves identification and integration of
  categories of meaning from data
• Provides researcher with an explanatory
  framework to understand the phenomenon being
  studied

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Developing a Research Question

• Researchers require an initial question upon
  which to focus their attention in the
  area/phenomenon they select
• Question should identify but not make assumptions
  about the phenomenon of interest. How are the
  articles acquired by the students? Rather than
  How do the students acquire the articles by
  reading grammar books?
• Q should be open-ended and should not employ
  constructs derived from existing theories/models
• Initial question can change radically during the
  GT process

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Steps in Grounded Theory

• 1. Data collection
• 2. Data Storage
• 3. Coding
• 4. Memo writing
• 5. Outcomes
• Stages are not mutually exclusive (ie analysis
  carried out alongside data collection)

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Coding

• Three kinds of coding used in GT
• 1. Open coding to find categories
• 2. Axial coding to find links
• 3. Selective coding to find core category

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Dis/Advantages of using GT

• Pros
• accounts for data
• holistic grasp of data
• Cons
• research could potentially be never ending
• hypothesis/research questions generated only
  applicable to your data

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Computer Software

• NUDIST large datasets, structured coding, mimic
  quant analysis
• NVivo less data, unstructured coding, find
  patterns/relationships in codes
• Hyperresearch
• Winmax Pro