Upgrade to remove ads
Qualitative Data Analysis& Critical Evaluation
Terms in this set (21)
According to Silverman (2000),
'transcription is... data analysis'
No universal system
. Jefferson system (Landridge & Hagger-Johnson, 2009) take account of different aspects:
Prosody (phenomenological aspects of spoken language e.g. intonation, stress)
Paralinguistics (non-phonemic aspects of language e.g. serious or jocular)
Extralinguistic (non-linguistic aspects e.g. gesture)
(non-phonemic aspects of language e.g. serious or jocular)
non-linguistic aspects e.g. gesture)
(phenomenological aspects of spoken language e.g. intonation, stress)
Some approaches to analysing qualitative data:
Framework Analysis - policy work, clear, mechanism for demonstrating in a visual way
Grounded Theory/constant comparison - based on well known and well respected theory
Discourse or Conversation Analysis - Jefferson system
Interpretative Phenomenological Analysis (IPA) - meaning and metaphors not just themes
Six Phases of Analysis (Braun and Clarke 2006)
1. Familiarising yourself with the data and identifying items of interest
2. Generating codes
3. Searching for themes
4. Reviewing themes
5. Defining and naming themes
6. Producing the report / paper
Codes identify a feature of the data (semantic content or latent) that appears interesting to the analyst, and refers to 'the most basic segment, or element, of the raw data or information that can be assessed in a meaningful way regarding the phenomenon
Thematic Analysis Process:
1st order or descriptive coding
Most basic level
Second order coding or interpretative coding
Super-ordinate in relation to 1st order codes
Third order pattern/theoretical coding
Super-ordinate in relation to descriptive and interpretative
Begin to apply relevant psychological or other relevant theories
Thematic analysis - overarching themes
Thematic Analysis (TA)
is a method for identifying, analysing and reporting patterns (themes) within data. It minimally organises and describes your data in (rich) detail. However, frequently it goes further than this, and interprets various aspects of the research topic
What is a theme
captures something important about the data in relation to the research question, and represents some level of patterned response or meaning within the data set.
What are the strengths of TA?
Can be used across a range of research questions and epistemologies
2. Relatively easy and quick to learn and do.
less labour intensive than other methods (e.g. DA/CA - technically detailed analyses of each instance).
3. Accessible to researchers with little or no experience of qualitative research.
4. Results accessible to educated general public.
5. Can usefully summarise key features of a large body of data.
- Provide a 'thick description' of a data set
6. Can highlight similarities and differences across the data set.
7. Can generate unanticipated insights.
Reveal new phenomena
8. Allows for social and psychological interpretations of data.
9. Useful for producing qualitative analyses suited to informing policy development.
(Braun and Clarke 2006)
What are the weaknesses of TA?
1. Flexibility of analytic options/foci can be paralysing!
No specific guidelines for higher level analysis
How to decide which aspect/s of data to focus on can be overwhelming.
2. Limited interpretative power if not used within an existing theoretical framework that anchors analytic claims.
3. Cannot retain sense of continuity/contradiction through an individual account (unlike IPA, NA, DP).
4. Cannot make claims about the fine-grained functionality of talk (unlike DP/CA).
5. Little kudos compared to 'branded' methods like IPA, GT, DP, CA.
Failure to actually analyse the data
Analytic comment simply paraphrases extract content (no analytic narrative)
Uses data collection (e.g. interview) questions as themes
Analysis should go beyond specific content to interpret/make sense of the data
Weak or unconvincing analysis
Too many themes
Too much overlap between themes
Vague thematic descriptors
Data examples within a theme not internally consistent or coherent
Poor theoretical application
Mismatch between data and analytic claims
Mismatch between theory/approach and analytic claims
Mismatch between research questions and type of TA/level of analysis used
Failure to consider other (obvious) alternative readings of the data
Failure to explain analytic process
Failure to provide adequate examples from the data
E.g. only one or two extracts per theme
Extracts are not compelling/convincing to someone who has not read entire data-set.
Who assesses quality of research
Why assess quality? researcher perspective
Provide accounts that reflects:
Participants' different subjective
'subtle realism' vs 'truth' claims
Reduce errors and biase
Why assess quality? From the 'consumer'/commissioner perspective:
To distinguish between 'good' and 'poor' quality research to determine whether:
Fundable 'value for money'
How to assess quality?
Does it add to existing knowledge?
Aims & methods
Methodology and methods
How to improve quality in your research
Negative or 'deviant' cases
YOU MIGHT ALSO LIKE...
Research - Final
Research Methods: Test 3
Chapter 15: Qualitative Data Analysis
Research Methods Chapter 6
OTHER SETS BY THIS CREATOR
WEEK 10)cognitive and the bilingual brain
WEEK 9) Language and thought
week 7) judgements and decision making
OTHER QUIZLET SETS
world history final historical event dates
RELI 101: chs 9-17.
Just 212 exam M
Human Gross Anatomy Test 4