The Process of Qualitative Research Methods
description
Transcript of The Process of Qualitative Research Methods
Research & Project DesignAssoc. 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
Principles of qualitative data analysis Context is critical
i.e. physical, historical, social, political, organisational, individual contextDependence/interdependence
Identify convergence / divergence of views and how contextual factors may influence the differences
Principles of qualitative 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
Principles of qualitative data analysis Pay attention to deviant cases /
exceptions Gives a voice to minorities Yield new insights Lead to further inquiry
Principles of qualitative 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 analysis2. 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
Code continuously as data collection proceeds
Imposes a systematic approachHelps to identify gaps or questions while it is possible to return for more data
Reveals early biasesHelps 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