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