Data management and coding Flashcards
poor data management (interview)
.no systematic record of interviews taken
.Interview recordings on different locations e.g. different computers-
.recordings not stored securely
.file names are confusing and unclear
.consent forms are scattered in various places
.transcription is incomplete or inaccurate
Data management - transcription
recordings translated into written data
a necessary step to interpret contextual qualitative data
provided with something tangible to analyze
allows verification of our analytic claims (need to evidence our claims)
ppts quotes are used as evidence
Jefferson transcription (precise transcription)
verbatim
used for conversational analysis and discourse analysis
LINGUISTIC ANALYSIS
MAXIMUM EXACTNESS
uses symbols to denote types of speech, ways of speech, and aspects of speech
verbatim- writing everything said down
inductive coding meaning- EMERGENT
bottom up
developing categories primarily from the data
coding is a classic inductive approach
deducitve coding- A PRIORI
top down
start with a set of codes based on research questions and you take those codes and look at data to see which data fits those codes
looking at data to support something we already think
Bazely and Jackson - stages of coding
transcribe data/ read and reflect/ explore and play- notes and hunches/ code and connect/ review and refine
confidentiality in data management
ANTAKI-
Uk data protection legislation GDPR ANTAKI- names with same syllable length maintain gender preserve ethnicity keep authenticity of data- preserve conventions city names can be left unless they are identifiable institutional names should b changed
Thematic Analysis- what is it
what are the 3 main types
analysing, identifying, reporting themes within qual data
types- content analysis, template analysis, thematic analysis
Thematic analysis - strengths
Not bounded to any pre-existing framework- you can do thematic analysis for most qualitative data sets
very flexible
a pragmatic approach
Can also be used to examine the way in which events, realities and meanings can effect a range of discourses operating in society
Content analysis (conceptual analysis)
purely often deductive
determine presence of certain words, themes, or concepts within given qualitative data.
changing qual—quant so it can be statistically analysed
used to reduce large data sets/ identify important aspects of content
Content analysis (conceptual analysis)
determine presence of certain words, themes, or concepts within given qualitative data.
changing qual—quant so it can be statistically analysed
USED TO= reduce large data sets/ identify important aspects of content/ examine trends in data
content analysis - STRENGTHS
.unobtrusive- can be used for secondary data meaning fewer ethical issues than other methods of analysis
.data is analysed in a very systematic and transparent manner- clear what u have done
.researchers do not require a certain ‘skill’
.used not just in psychology
content analysis - DISADVANTAGES
.time consuming
.prone to increased error-especially sensitive to measurement error, because data generation relies on the “consensual reading” (Krippendorff, 2004a, p. 212) of semantically or visually ambiguous messages by different coders and/or at different occasions.
.critised for being to basic and lacking in theory
.reductionist
.ignores context
causality cannot be madr
Template analysis
TEMPLATE
used to thematically analyse and organise qualitative data.
data is usually form of interview transcripts but has to be TEXTUAL DATA
you develop coding ‘templates’
hierarchal coding is suggested
top down/ a priori
when/why use template analysis
it is epistemological flexible - doesn’t matter if we are ‘realist’ or ‘constructionist’
.procedural flexibility- can be used number of different studies
.good for larger data sets
.used for a priori themes (these are tentative)
stages of template analysis
- define a priori themes
- collect data
- transcribe interviews/familiarise data
- carry out initial coding
- produce initial coding template
- continue to refine template
- use final template to interpret and write up findings
Advantages of template analysis
- links to previous research- guided analysis
- builds on existing theory and findings
- flexible- can be refined/adjusted as analysis continues
- less time consuming analysis
- researcher captures important theoretical concepts
- offers a structured approach to organise data
- way of managing and structuring analysis with a large data set
disadvantages of template analysis
- lack of holistic understanding in relation to individual accounts
- research with lots of studies may leave a novice reader overwhelmed and unsure on which direction to go e.g. which a priori codes to use
- templates may be too simple to allow interpretation or too complex for effective management
thematic analysis in thematic analysis-
INDUCTIVE/DEDUCTIVE - THEMES
- not bounded to any pre-exisitng framework
- pragmatic approach- identifies experiences, meanings, reality of ppts (flexibile)
- examine ways in which events, realities, and meanings effect the ways which discourses operate within society
- requires involvement and interpretation from the researcher
- involves identifying and describing explicit (semantic) and implicit (latent) ideas in the data
- inductive thematic analysis has a huge breadth of scope
THEMES- how to identify them in qualitative
repeating words/ behaviours key words searching for missing info metaphors and analogies are there any deviations from these patterns
why is reflexivity important in qualitative
REFLEXIVITY= reflecting on ones own beliefs and judgements and practices- examining how ones own assumptions might affect the study
active research
themes don’t just emerge from the data they are generated by the researcher
researcher needs to reflect on assumptions they bring when analysing as this will have an effect
Thematic analysis- Thematic analysis (Braun and Clarke) 6 STAGES
- familiarisation data= reading and re reading actively and critically, noting what is interesting
- generating codes= iterative process (evolves). flexible process allows for change
- generating initial themes- important codes become themes/ clusters of codes of similar meaning become themes- very flexible (still rename etc)
- reviewing initial themes- check meaning, should some be combined?/ do they fit the theme/ does it address research question and reflect content of data set
5.defining and naming themes- e.g. odour, dress, diet may be named appearance.
when you get to data saturation u have enough themes
6.producing the report- analysis still continues in this stage
select evidence from data to support claims
provide a ‘thick’ description
thematic analysis codes
latent/semantic
semantic- capture surface meaning
latent- capture assumptions underpinning the surface meaning
thematic analysis
what is bracketing?
coping with our own assumptions affecting data
attempts to mitigate potential effects of our preconceptions or biases might have on analysis
one way is writing memos to yourself (important to be honest) -CUTCLIFFE 2003
another way is to engage in an interview with another researcher not involved in this analysis - ROLLS AND RELF 2006
thematic analysis - braun and clarke DISADVANTAGES
- novice readers find it hard to handle large data sets
- poorly branded method
- not a rigorous method due to the flexible nature
- can be conducted poorly
- not consistent
when carrying out thematic analysis what decisions do researchers have to make?
inductive coding/deductive coding how you are going to code- software? scribbles? cutting up pieces of paper? word? whats interesting whats a theme when to stop analysis final report what to include
