DOING RESEARCH IN APPLIED
LINGUISTICS
Bangkok, June 2013
The grounded analysis of qualitative data:
From coding to interpretation and
explanation
Roger Barnard
University of Waikato
rbarnard@waikato.ac.nz
Overview
• 1. The nature of grounded theory
– Positivist theory
– Grounded theory
• 2. Grounded analysis
– Open, axial and selective coding
– Qualitative analysis tools
The need for theory
• Most empirical research, including a PhD thesis, needs to make
a theoretical contribution to academic understanding of the
chosen topic.
• A theory needs to explain, not merely describe.
• In order to explain a phenomenon, to be:
– a critical review of existing scholarship and research to identify the
research spaces the study will occupy, the selection of an appropriate
methodological framework, and the collection and analysis of data
emerging from empirical research.
Positivist research
• Test a theory by identifying hypotheses
• Seek to (dis)confirm these hypotheses
• Control the variables, e.g.,
– Sample/population
– Experimental / control groups
– Pre-test, treatment, post-test, delayed post-test
• Collect quantitative data
• Statistically analyse the data
• Interpret the probability of significance
Interpretive research / Naturalistic inquiry
(Cohen et al, 2011, p.219)
• “The social and educational world is a messy place, full of
contradictions, richness, complexity, connectedness,
injunctions and disjunction is…
• … It is multilayered, and not easily susceptible to the
atomisation process inherent in much numerical research. It
has to be studied in total rather than in fragments if a true
understanding is to be reached”.
• The implication is that conducting interpretive research is
likely to be messy, full of contradictions, etc…
The discovery of grounded theory
(Glaser & Strauss, 1967)
• G&S advocated explaining phenomena by “developing theories
from research grounded in data rather than deducing testable
hypotheses from existing theories” (Charmaz, 2006, p. 4 – emphasis
in original)
• “ … the purpose of the constant comparative method of joint coding
and analysis is to generate theory by using explicit coding and
analytic procedures” (Glaser & Strauss, 1967, p. 103 - emphasis in
original).
• This is in sharp contrast to the positivistic assumptions of
objectivity, generalisation, replication, predictability, and the
falsification of competing theories and hypotheses.
Features of grounded theory
(Cohen et al, 2011, p.598)
• Theoretical explanations are not predefined
• Theory emerges from the data rather than vice versa
• Theory is generated as a consequence of, systematic data
collection (‘thick’ description) and analysis (‘rich’
interpretation)
• Patterns and theories are implicit in data, waiting to be
discovered.
But how does it work?
• Thick description of context, events & participants, derived from
• multiple methods of data collection, from which
• key data are collated, and subjected to an iterative process of
• grounded analysis, which enables
• rich interpretation, which leads to
• Grounded theory – situated explanation
2. Grounded analysis
Collation and management of data
• Multi-methods generate a vast amount of data
• The data must be stored or filed (in a computer)
• Datasets must be systematically collated
• Then systematically compared and contrasted.
• QDA software can be very helpful
Qualitative data analysis (QDA) tools
• ‘False hopes’ of QDA tools’ ability of analysing qualitative data
(Weitzman, 2000)
• The nature of qualitative data and the importance of capturing the
context meaning and participants’ experience are not well treated
through QDA tools (Roberts &Wilson, 2002)
• Technology has long been downplayed in qualitative data analysis
(Paulus, Lester, & Britt, 2013)
• Because 21st century has seen major developments – eg NVivo
• QSR-NVivo 10 assists the researcher to import, manage and analyse
both quantitative and qualitative data (print, audio, visual).
• It does not interpret the data – that is the task of the researcher
(Hutchinson et al, 2010)
Analysis and interpretation
• Analysis begins and continues when data are being collected and
transcribed (and translated)
• Therefore, the researcher him/herself should transcribe the data
because preliminary codes and themes will occur in the process
• The analysis says as much about the researcher as about the data
being analysed:
•
“It is naïve to suppose that the qualitative data analyst can separate analysis
from interpretation, because words themselves are interpretations and are to be
interpreted.” (Cohen et al, 2007, p. 495)
Coding
• Read the data several times to: get a general sense, note down
ideas, think about organising the data, check if more data are
needed.
• Then coding can start.
• “any researcher who wishes to become proficient at doing
qualitative analysis must learn to code well and easily”
(Strauss, 1982, p. 27)
• “Coding means that we attach labels to segments of data that
depict what each segment is about.” (Charmaz, 2006, p. 3)
• Three types of coding: open, axial, and selective.
• Open Coding
Coding the data
• Coding allows the researcher to deconstruct the data into
manageable chunks in order to understand the phenomena in
question
• Treating data bit by bit helps to categorise (Dey, 2003)
• examining each line to construct the meaning (Ikpeze,2007)
•
• “The early part of coding should be confusing, with a mass of
apparently unrelated material. However as coding progresses
and things emerge analysis becomes more organised and
structured” (Ezzy, 2002, p.94).
The coding process
• The researcher must interrogate the data to identify
units of analysis (categories). Thus:
•
•
•
•
•
•
Highlight key points in each dataset (e.g. an interview)
Give each key point a code to describe the data
The codes will reveal patterns across the data
Then group the patterns into categories
Give each category a title
New codes and subcategories will emerge.
Open codes: An example
(Saldana, 2013, p.5)
•
1.My
•
•
•
•
•
1. Middle
son, Barry, went through a really tough time about, probably started
the end of fifth-grade and went into sixth grade. 2.When he was growing up
in school he was a people pleaser and his teachers love him to death. 3.Two
boys in particular he chose to try to emulate, wouldn’t, were not very good
for him. 4.They were very critical of him, and put him down all the time and
he internalised that, I think, for a long time. 5.In that time, they really just
kind of shunned him altogether, and so his network as he knew it was gone
school hell
2. Teacher’s pet
3. Bad influences
4. Angst
5. Lost boy
Identifying open codes
• Read through the data and highlight key points.
•
•
•
•
•
•
The key points can be identified by, for example:
Repetition - of the same word/phrase
Synonyms and antonyms
‘Jargon’ - technical terminology words/phrases
Linguistic connectors - e.g. because, instead, similar…
Metaphors/analogies – comparing one thing in terms of another
• Consider what is not in the data (that you expected to find).
•
•
•
•
Axial coding
Categories
Themes
Interpretations
Axial coding
• After open coding one set of data, you start to make
interconnections between categories and codes between
datasets (e.g. interviews and observations)
• Examine each open code in a dataset and compare and contrast
with other datasets (Bloor & Wood, 2006)
• Common patterns (categories) will emerge from axial coding
• Codes categories and sub-categories need to be constantly
checked, rechecked and redefined (Seidel, 1998)
Axial coding: Example
• Analyse other data using the same codes:
•
1. Middle
school hell
2. Teacher’s
pet
3. Bad
influences
4. Angst
• Several/many of the same codes will be used repeatedly
• Look at other data for new codes
•
6. Importance
•
•
•
•
Review your first dataset to see if the new codes apply
Expand/refine/redefine the codes, as necessary.
Search for patterns among the codes.
Examples
•
C1.Affection C2. Negative
of friends 7.Happy days 8.Carefree, etc
experiences C3. Reactions
5. Lost
boy
Emerging categories: Examples
• Patterns may be exact, similar, or something in common;
• Examples
• C1.Affection C2. Negative experiences C3. Reactions
• There may be sub-categories – e.g., C1.Affection
•
C1T. from
teachers; C1P. from peers;
C1F .fromfamily
Selective coding: Core categories
(Strauss & Corbin, 1994)
• After completing open and axial coding, the researcher will
select core categories, which must:
• 1. be central to the category system and the phenomena, rather
than peripheral
• 2. appear frequently in the data
• 3. fit the data, comfortably and logically
• 4. enable variations to be explained
• 5. have the greatest explanatory power
Categories and themes
• Some researchers do not distinguish between core categories and
themes, but most do.
• A category is a word or phrase describing some explicit segment of
your data
• A theme is a phrase or sentence describing more subtle and implicit
processes (Rossman & Rallis, 2003, p. 282)
• Themes can be developed within conventional social coordinates –
who, what, when, where:
– “the intersection of one or more actors [participants] engaging in
one or more activities at a particular time and in particular
specific place (Lofland et al., 2006, p. 121 – emphasis in
original).
Interpreting the data
(Burns, 1999, p. 159)
• The move beyond describing, coding, categorising and
comparing to make sense of the data.
• “This stage demands a certain amount of creative thinking…
• As it is concerned with articulating underlying concepts and
developing theories …
• … about why particular patterns of behaviours, interactions or
attitudes have emerged”..
• … You may need to come back to the data several times to
pose questions, rethink the connections and develop
explanations of the bigger picture underpinning the research.”
Theoretical saturation
• Saturation is the point when the major themes are
fully developed, and no new information will add to
them (Cresswell, 2007, p. 244)
• the analysed data can provide no new theoretical
insights (Hutchinsoin et al, 2010, p.299)
• This is ultimately a subjective judgement
• The researcher is now ready to formulate a situated
explanation of the phenomenon that has been
investigated grounded in the data.
Grounded analysis: Summary
• Transcribe, collate and manage data – iteratively
• Select key data for preliminary exploration
• Coding: open – to identify categories
• Coding: axial – compare & contrast datasets
– Use a variety of memos
– Use a codebook to label, define and exemplify
– Keep a reflective research journal
• Coding: selective –to identify and layer themes
• Explore the themes to develop an explanation
• Saturation - from analysis to interpretation to theory
Thank you!
Do you have any questions or comments?
Using other QDA tools
• Using NVivo, researcher can risk losing “the context and meaning of
raw data by too much data manipulation”(Roberts & Wilson, 2002,
p. 11).
• Use different tools : for example
• Use a codebook
• Constantly write memos in NVivo
• Keep a reflective research journal
• Ask ‘critical friends’ for assistance
• Alternative software tools
• Create a matrix in Excel consisting of the main elements
• Use Mindjet to see everything in one space.
• Use Inspiration 8IE to capture the main story
Memo-writing
(Charmaz, 2006, p. 72)
• “Memo -writing is the pivotal intermediate step between data
collection and writing drafts of papers.
• When you write memos, you stop and analyze your ideas
about the codes in any - and every – way that occurs to you
during the moment.
• Memo-writing constitutes a crucial method in grounded theory
because it prompts you to analyse your data and codes
[throughout] the research process”
• (NVivo makes memo-writing easy because it links to the data
and codes)
Codebook
(Macqueen et al, 1998)
• It is very useful for researchers to use a code book,
which may consist of six components:
•
•
•
•
•
•
•
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1. code-name/label
2. brief definition
3. Full definition
4. inclusion criteria
5. exclusion criteria
6. example(s)
As the analysis proceeds, these will change.
The codebook can be inserted into NVivo
Reflective research journal
(Borg, 2001)
• This should be a personal record of your progress during the whole
research process
• This can be maintained in Nvivo, or stored separately
• The main purpose is to detail the ongoing theoretical development,
and all decisions regarding sampling and analysis. (Hutchinson et al,
2010, p.286)
• But many people find qualitative data analysis frustrating. Thus, it is
useful also to record:
–
–
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–
–
Daily achievements
Moments of success
Feelings of anxiety, frustration, depression
What action was taken
Etc.
Critical friends
•
•
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Ask academic colleagues to assist you by, for example:
Evaluating your open and / or axial codes
Applying codes to sample data and comparing with your own
Responding to your questions about process
Giving feedback on your interpretations
–
–
–
–
Example of data
Your interpretation
Feedback from CF
Your response/action
References
•
•
•
Barnard, R., & Burns, A. (Eds.). (2012). Researching language teacher
cognition and practice: International case studies Bristol, England:
Multilingual Matters.
Bloor, M., & Wood, F. (2006). Keywords in qualitative methods: A vocabulary
of research concepts. London, England: Sage .
Borg, S. (2001). The research journal: a tool for promoting and understanding researcher
development1 Language Teaching Research, 5(2), 156–177.
•
Burke, K. (1969).The grammar of motives. Berkeley, CA: University of California Press.
(Original work 1945)
•
Charmaz, K. (2006). Constructing grounded theory: A practical guide through
qualitative analysis. Los Angeles, CA: Sage.
Cohen, L., Manion, L., & Morrison, K. (2011) Research methods in education
(7th ed.) London, England: Routledge.
Creswell, J.W. (2005). Educational research: Planning, conducting, and
evaluating quantitative and qualitative research (2nd ed.). Upper Saddle River,
NJ: Pearson/Merrill Prentice Hall.
Creswell, J. W. (2007). Qualitative inquiry and research design: Choosing
among five approaches (2nd ed.). London and New York: Sage.
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References
Dey, I. (2003). Qualitative data analysis: A user friendly guide for social
scientists: London, England: Routledge.
Ezzy, D, (2002). Qualitative analysis: Practice and innovation. London,
England: Routledge.
Flick, U., von Kardoff, E., & Steinke, I. (Eds.) (2004). A companion to
qualitative research (Trans: B. Jenner). London, England: Sage.
Geertz, C. (1973). The thick description: Towards an interpretive theory of
culture. In C. Geertz (Ed.), The interpretation of cultures. New York, NY: Basic
Books.
Glaser, A., & Strauss, B. (1967). The discovery of grounded theory. Chicago,
ILL: Aldane.
Harding, J. (2013). Qualitative data analysis from start to finish. Thousand
Oaks, CA: Sage.
Hutchinson, A.J.., Halley Johnston, L., & Breckon, J.D. (2010) Using QSRNVivo to facilitate the develoopment of a grounded theory project: An account
of a worked example. International Journal of Social Research Methodology,
13(4), 283-302.
Lincoln, Y.S., & Guba, E. (1985). Naturalistic inquiry. Beverly Hills, CA:
Sage.
MacQueen, K., McLellan, KE., Kay, K., & Milstein, B. (1998). Code book
development for team-based qualitative analysis. Cultural Anthropology
Methods 10, 31-36.
References
• Paulus, T. M., Lester, J. N., & Britt, V. G. (2013). Constructing hopes and
fears around technology: A discourse analysis of introductory qualitative
research texts. Qualitative Inquiry, 19(9), 639-651.
• Saldana, J. (2013). The coding manual for qualitative researchers (2nd ed.)
Los Angeles, CA: Sage.
• Seidel, J. V. (1998). Qualitative data analysis. Retrieved from
ftp://ftp.qualisresearch.com/pub/qda.pdf
• Spindler, G., & Spindler, L. (1992). Cultural process and ethnography: An
anthropological perspective. In M. LeCompte, W.L. Millroy & J. Preissle
(Eds.), The handbook of qualitative research in education. London,
England: Academic Press.
• Strauss, A. L. (1987). Qualitative analysis for social scientists: Cambridge,
England: Cambridge University Press.
• Strauss, A.L., & Corbin, J. (1994).Grounded theory methodology: An
overview. In N. Denzin & Y. Lincoln (Eds.), Handbook of qualitative
research (pp. 273-285). Thousand Oaks, CA: Sage.
• Weitzman, E. A. (2000). Qualitative research. Thousand Oaks, California:
Sage.
3. Reporting interpretive research
Interpretive research: criteria
• Warrants
• Credibility
• Dependability
• Relatability (Transferability)
• Trustworthiness
Interpretive research: ‘Warrants’
• Validity and reliability are criteria that belong more
to reporting positivistic research than to interpretive
research.
• For interpretive inquiry, internal validity should be
replaced by credibility and internal reliability by
dependability.
• Generalisation should be replaced with relatability.
Interpretive research: Credibility
• Interpretive research is inevitably subjective.
• The researcher is a participant in the research.
• Interpretive researchers to identify their own
ideological and epistemological biases and
acknowledge their ethical implications (Janesick,
1994, p.212).
• They need to explain how data are categorised…
• … and how similarities and differences between
categories are judged.
Naturalistic research: Dependability
• The data collection and analysis procedures must be
rigorous…
• … and comprehensive and transparent in the
reporting.
• Contradictory findings need to be reported.
• Unexpected changes need to be explained.
• The observer needs to hold inferences in check and
use them ‘parsimoniously’ (Spindler & Spindler,
1992, p. 22).
Interpretive research: Relatability
• Thickness of description and richness of interpretation make
a particular case interesting and relevant to those in similar
situations (Lincoln and Guba, 1985)
• Readers can relate (Bassey, 1981) the findings to their own
contexts.
• “insights from a case study can inform, be adapted to, and
provide comparative information to a wide variety of other
cases” (Van Lier, 2005, p. 198).
• But “it is the reader’s decision whether or not the findings are
transferable to another context” (Graneheim & Lundman,
2004, p. 110).
Trustworthiness
(Lincoln & Guba, 1985, pp. 289 – 311)
Both the reporting of events and their interpretation
must be a credible version of what happened.
The changing sociocultural context of the study must be
fully documented (‘thick description’) so that the
researcher’s interpretations can be justified in their
context.
What eventuates, therefore, is judgment built upon
judgment (Edge & Richards, 1998; Hammersley, 1992)
Grounded analysis: stages
•
•
•
•
•
•
Collation and management of data
Interrogating data
Coding the data
Constantly comparing and contrasting the data
Identifying patterns, categories, sub-categories
Saturation – no new insights are reached
– even when new data are added
Selection of data
• The use of multiple methods of means that often the
researcher is overwhelmed by the sheer amount of data
collected.
• It may be necessary to (initally?) put on one side data
from some sources, and concentrate on key sources.
• It will also be likely that broad themes will emerge from
some data sources that will inform the analysis.
• It is important for researchers to tell the truth (as they
see it), but they do not have to tell the whole truth.
• But contradictory, or ‘difficult’ data should not be
eliminated.