preparing qualitative data for analysis

• qualitative data analysis is a range of processes and procedures that help move from qualitative data that have been collected, into some form of explanation, understanding or interpretation of the people and situations we are investigating
• when analysing qualitative data, we are looking at depth, detail and complexity
• what is qualitative data analysis:

  • the mass of words generated by interviews or observational data needs to be described and summarised
  • the question may require the researchers to seek relationships between various themes that have been identified, or to relate behaviour or ideas to biographical characteristics of respondents such as age or gender
• analysis of qualitative data usally goes through some or all of the following stages (though the order may vary) PART 1:

  • familiarisation with the data through review, reading, listening etc
  • transcription of tape recorded material
  • organisation and indexing of data for easy retrieval and identification
  • anonymising of sensitive data
  • coding (may be called indexing)
  • identification of themes
  • re-coding
• analysis of qualitative data usally goes through some or all of the following stages (though the order may vary) PART 2:

  • development of provisional categories
  • exploration of relationships between categories
  • refinement of themes and categories
  • development of theory and incorporation of pre-existing knowledge
  • testing of theory against the data
  • report writing
• types of qualitative data sets:

  • semiotic analysis
  • IPA, thematic analysis
  • conversational analysis
  • narrative analysis
• semiotic analysis:

  • analysing signs and symbols (e.g. a participant captures an image and is filmed whilst talking through what the image means to them - it is important to note that the analysis is of the picture and its meaning in a particular social context)
• Ipa, thematic analysis

  • an account from the participant is captured by an interviewer driving the conversation through asking questions
• conversational analysis:

  capturing a conversation between two people without any prompts
• narrative analysis:

  • a deep analysis of the dimensions of social life. this is one person giving an account of an issue, similar to 'story telling'
• a range of ways approaching TA

  • a more inductive way - coding and theme development are directed by the content of the data
• A range of ways of approaching TA:

  • a more deductive way: coding and theme development are directed by existing concepts or ideas
• A range of ways of approaching TA

  • a more semantic way: coding and theme development reflect the explicit content of the data
• A range of ways of approaching TA

  • a more latent way: coding and theme development report concepts and assumptions underpinning the overt content of the data
• A range of ways of approaching TA

  • a more realist or essentialist way: analysis focuses on reporting an assumed reality evident in the data
• A range of ways of approaching TA:

  • a more constructionist way: analysis focuses on exploring the realities produced within the data
• deductive or a priori approaches to data analysis

  • involves applying theory to the data to test the theory
  • a 'top-down' approach to data analysis
  • applying predetermined codes to the data
  • the codes can be developed as strictly organisational tools, or they can be created from concepts drawn from the literature, from theory, or from propositions that the researcher has developed
• applications of the deductive analysis

  • organise data into categories to maintain alignment with research questions
  • deductive analysis can help maintain focus on the purpose of the research
  • during the first read-through of the data, create broad topical categories of interest based on research questions and then sort the data into those categories
  • data will then be sorted into those categories, which allows you to focus on relevant data in subsequent rounds of analysis
• inductive analysis

  • inductive analysis is a more emergent strategy, where the researcher reads through the data and allows codes to develop/names concepts as they develop
  • a more 'bottom up' approach
• inductive analysis

  • there are many forms of inductive analysis, but some common practices are open coding , in vivo coding, and constant comparative analysis
• inductive analysis

  • reflexivity plays a key role for the researcher to keep track of the analysis process and the decisions made and to make sense of the data
• inductive analysis

  • keep a running memo of the themes and findings, keep interesting or generative participant quotes or excerpts from field notes, and any evidence relevant for your themes and findings as they develop
• applications of the inductive analysis

  develop themes and findings

  • the key purpose of inductive analysis is to really dig into what is happening in the data, to understand the themes present in the data and to produce findings to answer your research questions
  • identify themes from the pattern codes through memoing and further condensing the pattern codes where possible. then try to capture the themes in short phrases
• Applications of the inductive analysis

  • Identify representative data to support findings.
  • -Throughout the analysis process, you will often develop codes from participants’ own words to point to data that is representative of particular findings.
  • -You will also keep a running memo of participant quotes, as well as amemo of emerging findings, where you will note representative evidence and write deeper descriptions and explanations.
  • -This process helps to keep track of important evidence and gives aplace to free write about findings.
• organising your data

  • individuals will need to be given pseudonyms or referred to by a code number
  • secure file needed to link pseudonyms to participants. file is confidential and will be destroyed after completion of project
  • names and other identifiable material should be removed from the transcript. narrative data needs to be numbered using line or paragraph numbers
• • data anonymization (pseudonyms) should not alter participants characteristics, and subsequently the data analysis
  • names can unconsciously bias peoples and researchers interpretation of quote being read
• naturalised transcription corresponds to the thorough transcription of what is said and how it is said

  preserves the different elements of the interview other than the verbal content, such as non-verbal language, contextual aspects, and the interaction between interviewer and interveiwee
• denaturalised transcription prioritises the verbal speech and focuses on the omission of the idiosyncratic speech elements, such as stutters, pauses, involuntary vocalisations, and non-verbal language
• what is a code? a short phrase that symbolically assigns a summative, salient, essence-capturing, and or attribute for a portion of language based or visual data
• first cycle coding: the portion of data to be coded can range in magnitude from a single word to a full paragraph to an entire page of text to a stream of moving images
• second cycle coding: the portions coded can be the exact same units, longer passages of text, analytic memos about the data and even a reconfiguration of the codes themselves developed this far
• coding for patterns

  a pattern can be characterised by:

  • similarity
  • difference
  • frequency
  • sequence
  • correspondence (they happen in relation to other activities or events)
  • causation (one appears to cause another)
• a code: captures a single idea associated with a segment of data, consists of labels identifying what is of interest in the data
• subtheme :

  • it exists underneath the umbrella of a theme
  • it shares the same central organising concept as the theme, but focuses on one notable specific element
• a theme

  • captures a common, recurring pattern across a dataset, clustered around a central organising concept
  • describes the different facets of that singular idea
• a category: a word or phrase describing some segment of your data that is explicit

  a theme: a phrase or sentence describing more 'subtle and tacit processes
• what gets coded?

  • cultural practicies
  • episodes
  • encounters
  • roles
  • social and personal relationships
  • groups and cliques
  • settlements
  • lifestles
• what gets coded:

  1. cognitive aspects or meanings
  2. emotional aspects or feelings
  3. hierarchical aspects or inequalities
• reflexivity = the interviewer effect
• reflexivity

  • subjective reality is coconstructed
  • participants respond differently to questions, depending on how they perceive the person asking questions (sex, age) an the language used
  • interviewers and interviewees have their own belief system, preferences, prejudices on all these impact on rapport during encounter
  • sensitive topics may bring these issues on the surface