CBR 302
A Hands-On Approach to Qualitative Methods
and Analysis
Introduction
About making the world a better place via Community
Based Research (CBR) :
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We’re all part of solution
No one way
Use dialogue
Deep learning to get at root of problem (Aha)
Learning Objectives
Upon completion of this workshop, you will be able to:
Describe qualitative research concepts
Explain some of the important terms associated with qualitative research
List misconceptions and limitations of ‘qualitative’ methods
Follow the steps to develop a research project
Differentiate between Methods, Methodology and Research
Develop strategies and skill for gathering and managing 'good' data (e.g.
recording, transcribing, note-taking, qualitative software, etc)
Participate in the analysis of data and develop strategies for coding data
Learn effective approaches for presenting qualitative data
Qualitative Research: Prevalent Concepts
“seek … how social experience is created and given
meaning” (Denzin & Lincoln, 2000, p. 8)
Produce findings without statistical procedures or
quantification
Accept different ways of making sense of the world
Seek participant interpretation
Emphasize participant more than researcher view
Qualitative Research: Prevalent Concepts
Researcher primary instrument
Focus on subjective (or lived) experience
Obtain descriptive, rich, in-depth accounts
Allow ‘from the ground up’ theory building
Can use open, exploratory, ‘unstructured’ means
One Definition of Qualitative Research
Understanding actions & meanings in their
social context (or ‘natural settings’)
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Focus on subjective (or lived) experience
Descriptive, rich, in-depth
Allows for ‘from the ground up’ theory building
Open ended, exploratory, ‘unstructured’
What is Qualitative Data?
Data in the form of words
Language in the form of texts such as : interviews,
questionnaire responses & long text
Observation of settings & events (researcher’s notes)
Personal document such as letter, diaries, etc
Aspects of QR Methods
Increases understanding of context, content
Increases understanding of what is affected & how
Analyzes why particular impacts are occurring
Assesses how policy can be improved
Qualitative Research:
Common Misconceptions
Easier than quantitative research
Not methodologically rigorous
Doesn’t reveal anything about ‘populations’
Data more manageable
Only experts can do it
Only a first step (for survey development)
Limitations of Qualitative Methods
Can be difficult to focus (holistic)
Researcher may have difficulty reconciling
differences in the data & assessing how
representative they are
Cannot make predictions
Cannot be objective since objectivity rejected on
principle
Cannot be replicated
Qualitative Research Stages
State Research Purpose and Problem
Design Research Project - Method
Conduct Research Project
Data Collection & Analysis
Disseminate Results
Exercise 1:
Define Qualitative Research in your own
words & discuss its application in the
context of your work?
Methods, Methodology & Research
Method: The practice of research
The specific techniques that are employed in collecting,
analyzing and disseminating the research
Methodology: The experience of research
The situations, contexts and social factors that informs the
researcher’s and participants’ involvement in the project
Method
A set of procedures and techniques for gathering and analyzing
data (Strauss & Corbin, 1998, p. 3)
• Observation
- Participant
- Nonparticipant
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Interview
Focus Group
Case study
Short-answer Questionnaire
Case study
Life history, Narrative,
Methods, Methodology & Research
Methodology – Process issues that can significantly
impact on the quality and validity of the research data
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Relationships to the community
Power and research
Nature of the questions
Role of prior knowledge in developing the project and
analyzing the data
Research Design and Methods
… there is no one design or method appropriate for
all
Instead, each partnership has to decide what works
best for its research question and intervention goal
in its particular community context. P. 70
Reference: Minkler & Wallerstein, 2003
Data
Rich Description
Captures sufficient data (e.g., quotations) so reader
can follow process, results, themes, conclusions
Captures sufficient detail to capture themes or
patterns to summarize essence of data
Method Exercise 1: Continued
Working with your table mates please discuss:
What factors should you consider when making the
decision about which method to use to collect the
data?
Be prepared to discuss your recommendations with the class.
Stages of Research
Identifying the Research Project –
Collaborative Venture with community partners
Clearly identify the focus and objectives of the research
project
Designing the Research Project – Method
Conducting the Research Project
Data Collection & Analysis
Dissemination of the Data
Develop Interview Guide Using Standard
Open–Ended Questions
Sub-questions should help answer main research question
Ask only what you need to know
Ask easier questions first
Do not ask more than one question within a sentence
Seeking more than a yes or no answer
Use exploratory words e.g., “describe,” “tell me”, “how,”
“where,” ‘imagine,” “think back”
Develop Interview Guide Using Standard
Open–Ended Questions
Create dialogue, encouraging participant to talk
Ask factual before controversial ones
Ask demographical questions as required
Last question for additional comments, interview
impressions
No leading questions
Develop Interview Guide
Unstructured Open-Ended Interview
Ask one question and allow questions to emerge
Good for initial explorations of topic when not much known
Develop Interview Guide
Closed fixed questions
Participants choose from possible answers
Useful for non-practiced interviewers
Group Exercise 2:
Using the scenarios that you have been given, please identify
the following:
1. A specific focus of the research project
2. Two primary goals of the research
project
3. The method that you would use to conduct
the
research
4. Four research / interview questions
Be prepared to present your group’s results to the class
Considerations for Sampling
Exclusion:
Decide if, why, how will exclude certain members who
want to be in the study
Collaboration:
Decide norms, decision process, how to make change,
procedures to encourage follow principles (e.g., member
checks; data collection/analysis personnel)
Data Analysis
The process through which data are organized so
that comparisons can be made and conclusions
drawn
Analyzing the Data
Manifest Content
– the actual ideas that are present and observable
in the data
Latent Content
– the ideas and themes that emerge as you
analyze and interpret the data
Data Analysis: Levels of Exploration
Need
to look
below
the
surface
(Described from
Interview)
Events
Trends,Patterns
Themes/Structure
(Forces/pressures at play)
Reference: Hagland, (1997), see also Argyris, C. & SchÖn (1974)
Group Exercise: Data Collection
Using a copy of the newspaper provided, please conduct a
research analysis of the document and explore the following:
What messages does the media provide you about issues of
gender?
In what way are those messages evidenced?
What are the potential benefits and drawbacks of providing
those messages?
Record your findings and make notes of your observations and
responses – verbal, non-verbal and emotional.
Data Analysis
Coding
 a word or short phrase that serves to organize
or categorize your data (community;
equitable coverage; stereotyping)
Themes
 clusters or patterns of codes that thematically
‘hang together’ (positive portrayal of women;
equitable portrayal;)
Coding
Coding means going through your data systematically (i.e.,
read each transcript carefully)
Decide ahead of time how you are going to approach
analyzing the data
You get to make up the names for the codes
Remember – not everything is important
Choose what is important to study by going back to your
goals & objectives.
Categories
• Codes are grouped to see where patterns occur (observations
over time causing event)
• From your exercise, examine both sets of data to see if there
are any codes that could be grouped together to form a
category
• Tip: look at definitions of codes as well
E.g., some codes might be all about “process”
• Try to see if any other categories could be formed
Themes or Structure
After categories are formed ……….. look for
themes or structure
(reasons why you might be getting the
particular pattern or trend)
Exercise 3: Data Analysis
Using the data you have collected, try to develop a simple
‘coding scheme’ of main ideas (maximum 10 codes)
Pick 1 code to apply:
Look for all the times that this idea is mentioned in your
data.
Analyze your code:
What are the range of experiences here? What
patterns/themes do you see here? What does this mean?
Discussion Questions
What are the themes that emerged?
What are the factors that determined the choice of the
particular themes?
How did your prior knowledge about the issue impact on the
themes that you identified?
How would that knowledge impact on the analysis of the
data?
Collaborative Data Analysis
Seeing the world through different eyes –
 Staff, clients & researchers all come with different
perspectives
 The more people you involve in developing your
coding scheme – the better.
Different eyes lends credibility to data analysis
Collaborative Data Analysis
Members of the team can examine specific parts of the data
E.g., stratified data: executive interviews by some members;
low-income interviews coded by other team members
Some members may code only for a certain period of time
due to their availability
Code because of knowledge and expertise of certain member
Code because of lack of knowledge and expertise of certain
members
Advocacy
Look for those persistent patterns that block social change
Once you identify the patterns, you can start to do something
about them
Analysis
QR and CBR seeks “authenticity” or ‘truthfulness”
Because of research rigor, results are credible,
demonstrates authenticity, truthfulness, provides
voice
Reference: Lincoln & Guba, 2000
Strategies for Optimizing Validity
Triangulation:
- the process of using a variety of data sources and approaches to
understanding a problem.
Thick description:
Rich, thick descriptions of the participants allow readers to assess
‘transferability’ to other research settings and context.
Member checks:
Asking participants to reflect on preliminary analyses and read rough
drafts of chapters/reports to ensure accuracy. These “checks” can take
place individually or in a group setting.
Clear audit trail:
Keeping a complete and accurate record of the steps taken to come to
the conclusions. This includes keeping organized notes, a clear and
consistent coding scheme and a ‘transparent’ data analysis process in a
reflective journal.
What about Software?
PROs:
 manages large amounts of
data, gives an ‘air’ of
legitimacy, makes
organization and retrieval
easy, provides structure
especially for teams not colocated
 Helps easily look at various
slices of the data
 Helps track decisions over
time
CONs
 steep learning curve (may
not be worth it), too much
structure, expense
Remember...software
manages ...YOU DO
ANALYSIS!
Types of Software
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NUDIST- N6
NVivo
Atlas.ti
SPSS
Dissemination
Approaches for presenting qualitative data effectively
 Predefine who your audience will be
 How do they receive their communications now?
 What will help them hear the message?
 Write in a language that is easily understood
 Can depict environment in which research took place
Critical Questions to Examine
Who owns the data?
What commitment have you made to share the
findings/results with the community?
How do you ensure that the results are accessible to
the community?
What role will the community play in dissemination,
and any resulting action outcomes?
Presenting Your Data
Describe your methods – tell your audience exactly what it is
that you did to come up with your results.
Be descriptive & analytical (so conclusions are accepted)
Use lots of quotes (as evidence, let people see them)
Consider tables, charts, figures, models & diagrams
Contextualize – where does this data come from, who does it
apply to (particularity, applicability)...
Speak with confidence about your data
Final Phase
What are the future indications of your research?
Are there other areas that have been highlighted for
future research?
Are there any other steps to be taken?
Workshop Reflections
Any surprises?
What are the most important learnings/messages
that you will take away from today?
Is there anything that you will plan to do differently?
Learning Objectives
Having completed this workshop you should now be able to:
Describe qualitative research concepts
Explain some of the important terms associated with qualitative research
List misconceptions and limitations of ‘qualitative’ methods
Follow the steps to develop a research project
Differentiate between Methods, Methodology and Research
Develop strategies and skill for gathering and managing 'good' data (e.g.
recording, transcribing, note-taking, qualitative software, etc)
Participate in the analysis of data and develop strategies for coding data
Learn effective approaches for presenting qualitative data
CBR 302
A Hands-On Approach to Qualitative Methods
and Analysis