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 JustificationPracticalitiesEthics

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 context Dependence/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 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

Code continuously as data c o l l e c t i o n p r o c e e d s  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,

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