data analysis

• quant data - numbers
• qual data - words
• there must be adequete data sampling to ensure that it has been represenititive
• language must be used accuratly - operationalised definitions
• interviews - context must be considered - selection of data - aware of any interviewer bias
• questionniares - consider both types of data - distinguish research interpretations and statements
• qual data analyse - thematic analysis
• thematic analysos - involved subject decision - making summaries and identifying key themes
• pros thematic analysis - detail of data - hypothesis allow for new insights
• cons thematic analysis - how do you decide on categories - subjective decisions
• content analysis - way to quantify qual data - representiatitive sample of data collected - add units and operationalise them - analysed to see how often it occurs
• content analysis pros - clear summary - replicable
• content analysis cons - subjective - removes detail
• pros of quantifying data - easier to see patterns - statisitcal analysis can be carried out
• cons of quantifying data - hard to avoid bias - detail lost
• primary - collected by researcher
• secondary - collected by others
• meta analysis - summarising lots of studies - come up with general conclusions
• meta analysis pros - good way to bring data together
• meta analysis cons - reduce sample size - conflicting results