Emma Ho
Soci 217
Week 14
Qualitative Data Analysis
Qualitative data analysis: the process of organizing, analyzing, and interpreting qualitative data
- Making sense of the data can be difficult
- Iterative process
- Qualitative approaches are incredibly diverse, complex, and nuanced
Analytic Induction
- Seeks universal explanations of phenomena that does not have any exceptions
o Therefore, if there is an exception, the researcher excludes or redefines the
hypothesis
Grounded Theory
1. What is grounded theory?
2. How is grounded theory different from other research approaches?
3. When is it appropriate to use grounded theory?
4. What are the fundamental aspects of grounded theory?
5. How are data collected in grounded theory?
6. What are the steps in grounded theory?
Grounded Theory – Coding
Open – identify broad and distinct concepts and themes for categorization
Axial – refining and categorizing themes to create distinct thematic categories
Selective – select and intergrade categories of organized data from axial coding at a higher level
of abstract
Grounded Theory Outcomes
Concepts: discrete phenomena produced through open coding
Categories: higher level abstraction consisting of two or more concepts
Hypothesis: initial hunches about relationships between concepts
Theory: a set of well-developed categories that are systematically interrelated and form a
theoretical framework that explains a phenomena
Grounded Theory Coding Considerations
- Explore data with an open mind and without the pressure of coding
- Code data as they are collected and as soon as possible
- Avoid focusing on quantity of codes; especially in the beginning
- Review codes
- When generating theoretical ideas, start broad
- Coding isn’t the be all and end all of GT
Grounded Theory Coding Challenges
- Context may be lost
- Fragmentation can influence the narrative flow
- Impossible to be unbiased
o Interpretations and theorizing are necessary
Grounded Theory – Use of Memos
Memo: an analytic note researchers write for themselves or colleagues about a code, category,
or other ideas about the data
- Helpful for:
o Crystalizing ideas
o Keeping track of thoughts on various topics
o Shaping the researchers’ reflections on broader issues
Grounded Theory Challenges
- May be impossible not to think about existing theory and research
- Time consuming (transcribing, constant comparison, coding, etc.)
- Data broken down into chunks may dilute or remove context and flow
- May not result in a theory
Mixed Method Research
- Allows researchers to use a diversity of methods to combine inductive and deductive
approaches
- Draws on the strengths and offsets the limitations of exclusively quantitative and
qualitative research through a complementary approach to facilitate a more
comprehensive understanding of social phenomenon
Triangulation Design
- Obtain different but complementary data on the same phenomenon
- Both research methods are implemented at the same time and given equal weight
- Combines the differing strengths and nonoverlapping weaknesses of quantitative
methods with those of qualitative research
- Strengths
o Intuitive
o Efficient
o Team approach if desired
- Challenges
o Requires effort and expertise
o Potential contradictory results
Embedded Design
- One dataset provides a supportive, secondary role in a study based primarily on the
other data type
- Useful when researchers need to embed a qualitative component within a quantitative
design (eg. experimental design) and therefore one of the datasets plays a supplemental
role
- Strengths
o Efficient
o Logistically more manageable
o May be appealing to funding
- Challenges
o Identifying the primary and secondary purposes
o Can be difficult to integrate results
Explanatory (Sequential) Design
- Two-phase mixed method design
- Qualitative data is sued to explain/build on quantitative results
- Useful when researchers want to form groups based on qualitative results and them
follow up with those groups with qualitative research
- Strengths
o Straightforward to implement and report results
o Appeals to quantitative researchers
- Limitations
o Time consuming
o Same participants for both phases?
o REB approval can be difficult
Exploratory Design
- Two-phase process where the first method (qualitative) helps to develop/inform the
second method (quantitative)
- Based on the premise that an exploration is needed because:
o Measures or instruments are not available;
o Variables are unknown; or
o No guiding framework or theory
Exploratory (Sequential) Design
- Strengths
o Straightforward to implement and report results
o Appeals to qualitative researchers
- Limitations
o Time consuming
o Same participants for both phases?
o REB approval can be difficult