qualitative_research and data_analysis.ppt

Research & Project Design
Assoc. Prof. Chiwoza R Bandawe

What is the purpose of my
research?
 What is my research for?
 How will this contribute to the socio-
political and cultural context of Malawi?
 Who will benefit? How emancipatory or
participatory is it?

What topic or broad area is the
research concerned with?
 Health?
 Policy?
 Sociological?
 Historical?
 Multi disciplinary approach?

What puzzle am I trying to
unwind?
 Development puzzle? How and why did
x or y develop?
 Mechanical puzzles? How does x or y
work? Why does it work in this way?
 Comparative puzzles? What can we
learn from comparing x and y? How can
we explain the differences between
them?

What are my research
questions?
 What is the social reality I wish to
investigate?
 What explanations or arguments can I build
from my data?
 Can I generalise my findings?
 Are my RQs consistent & linked with each
other? Do they add to a sensible whole?
 Are they worth asking and grounded in an
understanding of the relevant background?

How is the social world
organised?
 What is my theory/ cosmology or world
view?
 What are my life values?
 How might my cosmology influence my
research?

Research
questions
Data sources
& methods
Justification Practicalities Ethics

Qualitative data
analysis
 Principles of data analysis (Patton,1990)
 1. No exact replication. Each study
unique
 2. Dependent on skills of researcher at
each stage of study
 3. No absolute rules, but guidelines for
analysis
 4. Report and monitor and report
analytical procedures in detail

Principles of qualitative
data analysis
 Important for researchers to recognise
and account for own perspective
 Respondent validation
 Seek alternative explanations
 Work closely with same-language key
informant familiar with the languages and
perspectives of both researchers and
participants

data analysis
 Context is critical
i.e. physical, historical, social, political,
organisational, individual context
Dependence/interdependence
 Identify convergence / divergence of views
and how contextual factors may influence
the differences

data analysis
 Role of theory guides approach to
analysis
 Established conceptual framework –
predetermined categories according to
research questions
 Grounded theory – interrogate the data for
emergent themes

data analysis
 Pay attention to deviant cases /
exceptions
 Gives a voice to minorities
 Yield new insights
 Lead to further inquiry

data analysis
 Data analysis is a non-linear / iterative
process
 Numerous rounds of questioning,
reflecting, rephrasing, analysing,
theorising, verifying after each observation,
interview, or Focus Group Discussion

Steps to Analysis
 Step 1: Familiarisation and immersion
 Step 2: Inducing themes/ Hypothesis
Formulation:
 Identifying
 Coding
 Categorisation
 Step 3: Discursive Elaboration (context)
 Step 4: Interpretation (telling the story)

Discourse (language)
 Realised in texts
 Is about objects
 Contains subjects
 Reflects its own way of speaking/
presentation
 Is historically located

Ideology
 A set of ideas that explains reality,
provides guidelines for behaviour and
expresses the interest of a group
 Elaborate: Christianity, capitalism,
Marxism.
 Consistent framework guiding action
 Narrowly aimed at one side of issue

Step 1: Familiarisation and
immersion
 Read the whole, read parts and see how
they fit into the whole picture.
 What are the contradictions?
 What are the taken for granted
statements?
 What vivid expressions, figures of
speech and metaphors emerge?
 What repetitions, gaps are noticed?

Step 1 …continued
 Why is this pattern like this?
 How are the sentences constructed?
Active or passive?
 How is the language being used? E.g.
police: “they did it, I keep law and
order” for protection.
 Comb the data and immerse yourself

Step 2: Inducing Themes
 Order the text into segment and solicit
themes
 - Way in which people categorise
 -Who is doing the categories?
 -Look for consistent patterns
 Coding
 Categorisation

Processes in qualitative
data analysis
2. Coding – Identifying emerging themes
 Code the themes that you have identified
 No standard rules of how to code
 Researchers differ on how to derive codes,
when to start and stop, and on the level of
detail required
 Record coding decisions
 Usually - insert codes / labels into the
margins
 Use words or parts of words to flag ideas
you find in the transcript
 Identify sub-themes and explore them in
greater depth

Coding – Identifying
emerging themes
 Codes / labels
 Emergent codes
 Closely match the language and ideas in the
textual data
 Insert notes during the coding process
 Explanatory notes, questions
 Give consideration to the words that you will
use as codes / labels – must capture meaning
and lead to explanations
 Flexible coding scheme – record codes,
definitions, and revisions

 Imposes a systematic approach
 Helps to identify gaps or
questions while it is possible to
return for more data
 Reveals early biases
 Helps to re-define concepts

Step 3: Discursive
Elaboration
 Texts work to reproduce status quo of
power relations OR disrupt, challenge,
deconstruct, show marginal voices.
 Explore function of texts in relation to:
 Power
 Ideology
 Institutions & domination

Developing hypotheses,
questioning and
verification
 Extract meaning from the data
 Do the categories developed make sense?
 What pieces of information contradict my
emerging ideas?
 What pieces of information are missing or
underdeveloped?
 What other opinions should be taken into
account?
 How do my own biases influence the data
collection and analysis process?

Step 3 Tools for
Analysis
 How are persons, situations named,
referred to linguistically?
 What traits, qualities, characteristics
attributed?
 What arguments are used to justify,
legitimise the status quo?

Step 4: Telling the
Story
 Bringing the whole analysis together
into a coherent whole. For a competent
and useful guideline, refer to the
article:
 Malterud, K. (2001). Qualitative
research: standards, challenges,
guidelines. The Lancet, 358, 483-488.

Interpretation
 Dependability
 Can findings be replicated?
 Confirmability
 Audit trail
 Permits external review of analysis
decisions
 Transferability
 Apply lessons learned in one context to
another
 Support, refine, limit the generalisability
of, or propose an alternative model or
theory

qualitative_research and data_analysis.ppt

More Related Content

Similar to qualitative_research and data_analysis.ppt (20)

More from AbanteeHarun (20)

Recently uploaded (20)

qualitative_research and data_analysis.ppt

Editor's Notes