QUAL2- qualitative data anaylsis

what is qualitative analysis

• defined set of procedures used flexibly to identify themes & describes/interprets data

• aims to understand variation in experience & considers complexity/variation

• can be empathetic (or critical) → evaluating interviewees experiences

• needs active engagement with data from the researcher

what is transcription

converting audio into text

2 methods of transcription

0rthographic & the jefferson system

what is orthographic transcription

• speech transcribed verbatim using standard spelling conventions

• more common type

what is the jefferson system

• understanding the mechanics of communication

• prosody → phonemic aspects

• para-linguistics → non-phonemic aspects

• extra linguistics → non-linguistic aspects

aim of content analysis

• patterns in communication in a replicable/systematic way

• systematic labelling of data

is content analysis qualitative

no

aim of interpretive phenomenological analysis

• insights to how a person in a specific context makes sense of a phenomenon

• personal experiences, small homogenous samples, idiographic

aim of discourse/conversion analysis

• identify values of conversational organisation

• natural talking interactions to understand how people respond to eachother

aim of grounded theory

• generate theories of social phenomenons through systematic data analysis

• can be inductive or deductive

6 steps for thematic analysis

1. data familiarisation

2. generating codes: labelling relevant ideas in data

3. searching for themes (grouping)

4. reviewing themes

5. defining & naming themes

6. producing the report/paper

what should you do before carrying out a qualitative analysis

orient the analysis

3 components to orientating the analysis

• state theoretical framework

• inductive or deductive approach in the analysis

• semantic or latent meanings

inductive approach for thematic analysis

themes are derived from the data (no pre-defined concepts and assumptions)

how to avoid anecdotal/surface level themes

don’t take initial first impressions as a basis to create themes

what is coding

turns ideas about data into concise labels/tags that can be understood independently of the data

what are 1st order codes

• semantic/descriptive

• describes an idea/feature of the data in the researchers own words

what are 2nd order of codes

• latent/abstract

• captures underlying meaning of an idea/feature of the data

• summarising anecdotal stories/complex descriptions in data

what are themes

• captures something important about the data in relation to the research question

• represents patterns in data

• has a central organising concept that brings codes together

what type of process is searching for themes

active & constructive

post-it note approach

• write each code on a post-it note

• pick one code & see if they can group it with any other similar codes

• repeat until a set of candidate themes is found that can group all of the data

5 things need to be considered when reviewing themes

• is the theme evident across +1 data items or just describing the experience of a single interviewee

• does each theme have a centrally organised concept or is there overlap between themes

• do the themes answer the research question

• do themes capture most/all of the codes

• is there a clear fit between the themes & the data

how to overcome problem of candidate themes describing the experience of a single interviewee

think of a theme that explains the differences between the interviewees

3 things theme names need to be

• concise

• informative

• catchy

what is a theme definition

• expands on the centrally organising concept of a theme

• a paragraph explaining why the theme is what it is

5 things theme definition needs to demonstrate

• describe variation in experience

• how meaning is constructed

• context

• focus on process

• empathetic