This is the first of a three-part lecture on the foundations of qualitative research. This lecture provides an accessible introduction to qualitative research for those new to qualitative research. A key distinction is made between an understanding of qualitative research as comprising tools and techniques for collecting and analysing qualitative data and an understanding of qualitative research as involving both qualitative tools and techniques, and research values or philosophy. The lecture then considers some of the distinctive characteristics of a qualitative philosophy includes a focus on meaning in context. This lecture is followed by Foundations of Qualitative Research 2, also in three parts, which introduces some of the concepts (and more complex terminology) associated with qualitative research.

1. Foundations of Qualitative
Research 1
Part 1: What is qualitative research?
Presentation by
Victoria
Clarke
Associate Professor of
Qualitative and Critical
Psychology, UWE
May 2019

2. PowerPoint slides from the Braun, Clarke &
Hayfield Qualitative Methods Online Teaching &
Learning Resources Collaboration (QMOTLRC)
• Narration by Victoria Clarke

3. Topic overview
o This is the first of two lectures exploring the values, characteristics and theoretical
foundations of qualitative research.
o The aim of this first lecture is to provide an accessible introduction to qualitative
research, for those with some research training. We try to use minimal specialist
terminology (this will come in the second foundations lecture!).
o This lecture explores different understandings of qualitative research – as
providing researchers with tools and techniques and as providing both tools and
techniques, and research values.
o We then explore the central importance of meaning and meaning-making to
qualitative enquiry and some different orientations to meaning.
o There are some opportunities to pause the recording and reflect on your
knowledge and understanding of qualitative research.

4. Topic overview
o Part 1: What is qualitative research?
o Part 2: Meaning and meaning-making
o Part 3: Orientations to meaning

5. Pause for reflection
• Pause the recording and spend 10 minutes writing
down/typing all your associations for qualitative research
– anything that comes to mind (methods, approaches,
characteristics etc.), even if it’s judgemental or critical.
• You may like to turn this list of words into a word cloud.
• This will be your knowledge ‘baseline’ that you can come
back to throughout the lecture.
• Victoria’s 10 minute word cloud is on the next slide.

7. Let’s jump
straight in and
start with some
data…

8. Int: …what’s the worst thing about university do you think?
Asha: … it’s probably the uncertainty of the people that you meet […] just basically erm er, does he
have a gay friend? Yes or no, is he alright with a gay friend? Yes or no. This person is alright to go out
with- you know to come out with and basically if the answers are different the questions are
different and the outcomes would be different […] you’re just trying to you know answer all the
questions to see what the outcome is and it’s kinda a bit of a headache
Int: It sounds exhausting, and stressful
Asha: It is, very much so but it’s kinda something that I have in the back of my mind […] I find out
you know which box they tick, which box they don’t tick and if they tick the right ones or if they tick
the wrong ones I know what action to take from there […]
Int: Yep yep, god that sounds very hard
Asha: Well the thing is it’s almost kinda- I wouldn’t, I don’t know it’s something that just happens in
the background you know- I hardly notice it
Int: Yeah like this processing that going on and kinda churning away
Asha: Yeah all these things that you just happens that you’re not even completely aware of but it’s
building up and you know you look back at it you see all these point and you say to my- you say to
yourself right “I’m gonna tell this person I’m gay” “I’m gonna” you know and yeah

9. Pause for reflection
• You may want to pause the recording on the data slide
and reflect on the characteristics of the data.
• The data is an edited extract from an interview with
Asha, a young gay man of colour, from our research on
LGBT students’ experiences of university life.
• What do you notice about the characteristics of
qualitative interviewing from this brief extract?

10. Some characteristics of this qualitative
interview
• Open-ended questions (more or less open).
• Planned and spontaneous questions and comments.
• Participant-led (to a greater or lesser extent).
• Researcher is responsive to the participant’s account.
• Researcher’s interventions often intended to provoke more – depth,
detail, disclosure.
• Centres the participant’s sense-making, experiences, language.
• ‘Messy’ – little concern for standardisation.
• ‘Imperfect’ – a decidedly human interaction.

11. What is qualitative
research?
• A type of data – spoken and written words, text, images,
meaning?
• Tools and techniques for collecting and making sense of
such data?
• What about the values that underpin and guide the
research?
• Can our research values be qualitative too?
• Or does qualitative research have to adhere to the
values associated with quantitative (‘scientific’) research
to be worthwhile?
• Not one answer because qualitative research is not ‘one
thing’: small q versus Big Q qualitative.

12. Let’s consider ‘small q’ qualitative
• Small q – ‘mash-up’ of qualitative techniques and quantitative or ‘scientific’
values – concerns for accuracy, reliability, controlling for researcher bias or
subjectivity.
• Accuracy of the participant’s account: “Each participant received a copy of her
interview transcript by mail and was invited to correct, elaborate upon, or modify
her comments to make the transcript a more complete and accurate account of
her experience.” (Bond et al., 2008: 52)
• Implies that there is a singular truth of participant’s experiences that research
should seek to uncover.
• That accounts are not partial, situated, contextual, temporal, provisional,
multiple.
To read more about small q qualitative see Kidder & Fine (1987).

13. Sidebar - Qualitative data analysis: The basics
• Most commonly the outcome of analysing qualitative data is a number of
patterns, themes or categories identified across several interviews (or other data
items – such as group discussions, survey responses, diary entries).
• Simply put, these patterns capture significant meanings, that are often recurrent.
• The extract from Asha’s interviews illustrates a theme entitled ‘Managing
heterosexism’, which explores how the participants implicitly took responsibility
for managing heterosexism.
• Qualitative data coding is integral to theme development or discovery.
• Coding involves carefully reading data and assigning labels to particular excerpts
to capture what is of interest or relevance.
• It requires the researcher to make subjective judgements – is this a problem?
• No one answer – yes (small q); no (Big Q). Let’s return to small q qualitative…

14. Small q qualitative data analysis
• In small q qualitative, there is an overriding concern with accuracy and reliability of coding,
and minimising researcher bias or subjectivity.
• Accuracy of data coding: “All interviews were consensus-coded by two or three researchers,
two of whom by design were unfamiliar with, and therefore presumably uninfluenced by,
previous research examining community leadership. During the inductive thematic coding
process, none of the coders was aware of the intent to examine the qualitative data
ultimately in terms of generativity themes.” (Bond et al., 2008: 52)
• Common techniques include the use of:
• Structured codebooks or coding frames – which are then applied to the data
• Themes that are determined prior to, or early on in, data analysis (and often reflect data collection
questions – e.g. the worst aspect of university life)
• Coding understood as a process of allocating the data to the correct pre-determined theme
• Multiple coders working independently to code the data
• Coders often trained in the use of the codebook but ‘blind’ to some aspects of the research
• Measures of the level of ‘agreement’ (or inter-rater reliability) between coders
• Consensus coding – coders agreeing the final data coding

15. Assumptions of small q data coding
• Reliability and accuracy of coding is paramount.
• Researcher bias or subjectivity is a problem to be managed:
• ‘the need for full agreement among coders is designed to help minimise the effects
of experimenter bias because it reduces the influence of any one coder over the
assignment of codes.’ (Heath et al., 2011: 600)
• Critical questions:
• Will themes determined prior to analysis be relatively superficial or capture only the
more obvious data topics?
• To facilitate coding agreement and the use of multiple coders (some unfamiliar with
the research area) will codes be relatively superficial?
• Is depth and complexity of understanding sacrificed for reliability and accuracy?
• Is accuracy of interpretation even possible?

16. Assumptions of small q
data coding
• Researchers often discuss themes (or categories)
as if they are entities that pre-exist in the
analysis, as if they are ‘in’ the data waiting for the
researcher to uncover them – like buried
treasure.
• The researcher is an archaeologist digging in the
dirt to uncover the treasures.
• Research = a process of discovery.
• The researcher is relatively passive in the process.

17. Qualitative research values: Big Q
qualitative
• Qualitative research for many is more than collecting and analysing words (or
images) as data, it’s about embracing a philosophy or set of values about how we
do research, about the role of researcher in research and what counts as
meaningful knowledge.
• Qualitative research that uses qualitative techniques and is underpinned by
qualitative values = Big Q qualitative.
• Lots of different arguments for Big Q qualitative and lots of criticisms of
quantitative/‘scientific’ values including…
• Human beings’ subjective understandings of the world are meaningful and important, and
worth knowing about.
• Human beings don’t live in laboratories – they live in the (social) world, human sense-making
can’t and shouldn’t be divorced from the messy, complex world in which it is situated.
• We need to centre participants in our research, and allow them scope to shape what a
meaningful response looks like, rather than determine the response options ourselves.

18. Returning to the data
• Returning to Asha, if we view this interview through the lens of qualitative
values, we can say that:
• Asha’s sense-making is worth knowing about.
• Asha’s sense-making reflects his particular location and perspective – as a young gay
man, a British-Asian, living in a social context in which heterosexuality is ‘the norm’…
• Asha’s sense-making can tell us something about his personal experience and also
what’s going on in the (social) world, and the particular (social world) of the
university.
• Asha’s sense-making isn’t true or false - qualitative researchers aren’t typically
preoccupied with questions of honesty, lying, concealment and partial truths.
• They are more interested how and why – how does Asha make-sense of his
experiences and the world; why is he making sense in these ways?
• A qualitative interview gives Asha some scope to tell the researcher what he thinks is
important.

19. Assumptions of Big Q
qualitative
• The researcher is an artist or sculptor – chipping away at a
block of marble or chiseling away at a piece of wood.
• Research = a generative and creative process.
• The researcher is active in the process – subjectivity is a
resource for research not a problem to be managed.
• The outcome of research is delimited by the materials (data)
collected by the researcher and the tools (research training,
skills and experiences, perspectives, values) they bring to the
process.
• Understandings of the world generated by research are always
shaped by the researcher, and their tools, and situated in
particular contexts.
• Understandings are always partial – and that’s okay!

20. Is there always a clear distinction between small
q and Big Q? No – there is also ‘confused q’
• Often small q qualitative is ‘mashed-up’ with Big Q qualitative in published research.
• The researcher articulates both qualitative values and concerns for accuracy and
reliability.
• Sometimes this is deliberate or intentional – as in approaches such as consensual
qualitative research (e.g. Hill, 2012) or certain types of thematic analysis (e.g. Boyatzis,
1998).
• But often this ‘mashing-up’ seems unknowing – not deliberative or conscious – perhaps
reflecting the dominance of quantitative or ‘scientific’ values in some disciplines and
research areas.
• We and other qualitative researchers think it is important for researchers to strive to
‘own their perspective’ (Elliott et al., 1999) – to make deliberative choices, and
understand and reflect on the assumptions and values that inform their research.
• This is part of what counts as ‘quality control’ in Big Q qualitative – partial, subjective
understandings can’t be accurate or reliable but they can be knowingly generated, or
strive to be knowingly generated.

21. Optional activity
• Return to the word cloud you generated at the start – are there any
words you like to add and any you’d like to remove following this first
part of the lecture?

22. References
• Bond, L.A., Holmes, T.R., Byrne, C., Babchuck, L. & Kirton-Robbins, S. (2008). Movers and shakers:
How and why women become and remain engaged in community leadership. Psychology of
Women Quarterly, 32, 48–64.
• Boyatzis, R. E. (1998). Transforming qualitative information: Thematic analysis and code
development. Thousand Oaks, CA: Sage.
• Elliott, R., Fischer, C.T., & Rennie, D.L. (1999). Evolving guidelines for publication of qualitative
research studies in psychology and related fields. British Journal of Clinical Psychology, 38(3), 215-
229.
• Heath, N.M., Lynch, S.M., Fritch, A.M., McArthur, L.N. & Smith, S.L. (2011). Silent survivors: Rape
myth acceptance in incarcerated women’s narratives of disclosure and reporting of rape.
Psychology of Women Quarterly, 35(4), 596–610.
• Hill, C. E. (Ed.). (2012). Consensual qualitative research: A practical resource for investigating
social science phenomena. Washington, DC, US: American Psychological Association.
• Kidder, L. H., & Fine, M. (1987). Qualitative and quantitative methods: when stories converge. In
M.M. Mark & L. Shotland (Eds.), New directions in program evaluation (pp. 57-75). San Francisco:
Jossey-Bass.