Transcript 5. lecture_ Analysing qualitative data_2012s

Analysing qualitative data
What is the input?
- non-numeric data
- not quantified
- can be a product of all research strategies
-> Procedures for analysis can be BOTH
deductive and inductive
- computer aided qualitative data analysis
software (CAQDAS)
Computer aided qualitative data analysis
software (CAQDAS)
…used in psychology, marketing research, ethnography etc
+
- efficient means to manage and organize data
- rigorous data analysis
- no manual and clerical tasks
- saves time
- manages huge amounts of qualitative data
- increases flexibility
- improves validity and auditability of qualitative research
- increasingly deterministic/ rigid processes
- privileging of coding
- reification of data
- increased pressure to focus on volume/breadth rather than on
depth/meaning
- time/ energy spent learning to use computer packages
- increased commercialism
- distraction from the real work of analysis
Differences between qualitative and
quantitative data
• Quantitative data:
- Based on meanings
derived from numbers
- Collection result in
numerical and
standardised data
- Analysis conducted
through the use of
diagrams and statistics
• Qualitative data:
- Based on meanings
expressed through words
- Collection results in nonstandardised data
requiring classification
into categories
-Analysis conducted
through the use of
conceptualisation
Preparing your data for analysis: transcribing
qualitative data
• non-verbal information may be relevant (pauses, laugh, sighs,
coughs, the tone of the voice, the speed of talk) – not only, what
they say but how they say it
• awfully time-consuming – 6-10 h to transcribe every hour of audiorecording
• Accurateness of transcription – data cleaning
• Save each interview as separate file; use filename that maintains
confidentiality/ anonymity; helps recognize the the person
• Distinguish between interviewer and participant(s) visually; use
other identifiers – questions in italics, topic headings in bold etc; be
consistent across all transcriptions
• Having the full question in transcript may be of importance if you
want to understand later what they are talking about :P
• Plan in advance, how the analysis will follow – e.g., if you use some
CAQDAS, remember, that they may require sometimes .txt file so all
your highlights, capitals and italics will be gone :P
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Aga nüüd vaatasime seda lõiku ja sa nägid seda enne ka ja sa ütlesid, et ta võttis selle telefoni ära. Mis sa
arvad, miks ta selle ära võttis?
V:Ta tahtis endale saada.
K:Vist küll. A siin oli üks teine tegelane veel. See mees. Kas tema ka midagi valesti tegi sinu arvates?
V:Jah, et ta ei hoiatand teda.
K:Aga mis sa arvad, miks ta ei hoiatanud?
V:Ei tea
K:Mis siin valesti tehti?
V:[ei saa aru] et siin nad võtsid selle koti ära ja viskasid ära, et ta ei saaks seda kätte. Too teine, kes seda
pealt nägi, nemad ei hoiatand seda poissi.
K:Täpselt. Mis sa arvad, miks need kaks poissi seda väiksemat siis niimoodi kiusasid?
V:Et neile vist meeldis.
K:Aga miks see tädi, kes seal juures oli, miks ta appi ei läinud? Mis sa arvad?
V:Ta tegeles parajasti millegi muuga ja tal polnd tahtmist appi minna.
K:Jah, ma arvan, et sul on õigus.
Mis siin siis valesti tehti?
V:Et [ei sa aru] aga ta tegelt oskas seda ise ka teha.
K:A sa arvad, et oskas ise ka. A mis sa arvad, miks see tädi ei aidanud?
V: Ta ei tahtnud vist.
K:Vist jah.
Nonii, mis siin valesti tehti?
V:Et nagu üks nagu midagi ütles, mingi suvaline inimene, lihtsalt, mis kell on. Et ta küsis lihtsalt, mis kell on.
Et nagu vabandada, seda ta ei ütlendki
K:Ahah. Mis sa arvad, miks ta ei tahtnud öelda?
V: Sellepärast et ta seal mõtles, et mingi suvaline inimene ja pole pole üldse lahke, et ta ei tahtnud talle
ütelda.
An overview of qualitative anaysis: four
main categories of strategies
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Understanding the characteristics of language
Discovering regulatities
Comprehending the meaning of text or action
Reflection
Dimensions to differentiate the approaches to
qualitative analysis:
Less structured ------- More structured
Interpretivist ------ Procedural
Inductive ------- Deductive
Basic procedures common to different
approaches of qualitative data analysis: 1)
categorisation
Helps you:
- Comprehend and manage your data;
- Integrate related data drawn from different
transcripts and notes;
- Identify key themes or patterns from data for
further exploration;
- Develop and/or test theories based on these
apparent patterns and relatioships;
- Draw and verify conclusions
• Categories:
- may be derived from these data or from your theoretical
framework
- Have to „fit“ with what you have revealed – with data
- Codes/ labels, giving a structure for the data
- Identification of the categories -> purpose of your research
- it is possible to interprete the same qualitative data very
differently
- Internal aspect of category – meaningful in relation to the
data
- External aspect of category – meaningful in relation to
other categories
2) „Unitising“ data
- unit - chunk or bit of textual data that fits the
category and carries discrete meaning
3) Recognising relationships and developing
categories
- search for key themes /patterns /relationships
- revise your categories
- keep an up-to-date definition of all the
categories
4) Developing and testing hypotheses or
propositions
- testing relationships between variables
- seeking alternative explanations/ negative
examples
- considering possible intervening variables
Analytical aids: a record of additional
contextual information
• Summaries – after every data collection set -> a
summary of the key points that have arised; think on
alternative ideas to explore your question; identify
apparent relationships between themes -> check their
validity; contextual notes – setting, changes, persons
etc.
• Self-memos – to record ideas about any aspect of your
research. Omitting to record an idea -> it will be lost –
it is proved!
• Researcher’s diary - recording ideas -> you can later
follow the development of them because of the
choronogical form
Approaches to qualitative analysis
• Deductive – using a theoretical or descriptive framework - use of
existing theory to formulate research question -> theoretical
propositions may devise a framework to organise/ direct the data
analysis.
- Advantage – link your research into the existing body of knowledge
in your subject area
• Inductive – exploring without a predetermined theoretical or
descriptive framework - to start collecting data/ exploring them ->
finding themes to concentrate on.
- Analyse the data during collecting it, developing a conceptual
framework to guide the subsequent work
In practice -> combining the elements from both approaches as at
certain points you may need to develop some theoretical position to
test its applicability; and at some moment you notice that the
theoretical framework you choosed does not yield a good answer to
your research question
Deductively-based analytical
procedures
• Pattern matching - predicting a pattern of
outcomes based on theoretical propositions to
explain what you expect to find. Two
variations
• Explanation building – an attempt to build an
explanation while collecting data and
analysing them. process of explanation
building - iterative
Inductively-based analytical
procedures
Reasons for adopting an inductive approach for the analysis of data:
1)
2)
3)
need for an exploratory project seeking to generate a direction for
further work
the scope of your research -> constrained by theoretical
propositions not reflecting participant’s views / experience. The
use of inductive approach should allow a good „fit“ between the
theory you develop and the social reality of the participants
the theory may be used to suggest subsequent action to be taken
because it is specifically derived from the events and
circumstances of the setting in which the research was conducted
You should NOT USE inductive approach to avoid a proper level of
preparation!
Data display and analysis
1) summarise and simplify the data; selectively
focus on some parts of it; the aim is to
transform and condense the data
2) organise and assemble your reduced and
selected data into some diagrammatic or
visual displays (matrix or network);
recognizing the relationships and patterns/
drawing conclusions and verifying these is
easier by the use of data displays
Template analysis
- list of the codes or categories that represent themes revealed from
the data.
- The codes will be predetermined and then amended/ added if the
data requires it
- Data are coded and analysed to identify and explore themes,
patterns and relationships.
- codes and categories can be shown hierarchically
- The codes at different level of analysis may change their position
during the process
- What’s the point of all this?
- …analytical technique through which to devise an initial conceptual
framework that will represent and explore key themes and
relationships in the data; help you to identify new, emergent issues
that arise through the process of data collection and analysis
Analytic induction
…inductive version of the explanation-building
procedure – „intensive examination of a
strategically selected number of cases so as to
empirically establish the causes of a specific
phenomenon“.
Grounded theory
…to build an explanation or to generate a theory around the central theme
that emerges from your data. The process may be more or less structured and
systematic. There are different stages of grounded theory procedures:
• Open coding – the data will be disaggregated into conceptual units and
provided with a label. In that way you may find a multitude of labels, that
you need to place into broader, related groupings or categories. This will
produce a more manageable and focused data set
• Axial coding – process of looking for relationships between the categories
of data that have emerged from open coding. As relationships between
categories are recognised, they are re-arranged into a hierarchical form,
with the emergence of subcategories. The aim is to explore and explain
the phenomenon by identifying what is happening and why; to find out
what environmental factors affect this; how it is being managed within the
context being examined, and what the outcomes are of the action that has
been taken.
• Selective coding – during data collection, it is likely that you will find the
principal categories and related subcategories – core categories will be
base of your grounded theory
Quantifying qualitative data
- to count the frequency of certain events,
particular reasons that have been given, or in
relation to specific references to a phenomenon
- frequencies can be displayed as a table or
diagram
- can be produced using CAQDAS programs;
exported to statistical analysis software
- considered as method of limited value -> do not
demonstrate the nature and value of your
qualitative data, being a simplified form of it