Research & Project Design Assoc. Prof. Chiwoza R Bandawe The Process of Qualitative Research Methods
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?
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 • 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 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