Qualitative is in-depth data that is collected reflecting participants feelings, attitudes and thoughts i.e. data that cannot be directly observed it wouldusually consist of video or audio recording, or written notes on what the observers have witnessed. Analysis of this data is less straightforward but can still be done, usually qualitative data is seen as “of limited use and turned into quantitative data using content analysis to enable it to be interpreted easier. It includes Quality of research, quality of data, Subjectivity-richness of data, descriptivity of the data, can’t really be measured and depth of information. Some examples Thematic analysis (selecting themes beforehand and organising responses according to the themes) or grounded theory (developing themes from the data) or giving voice (using participants own words) (+) represents complexity of humans behaviour (+) Provides rich detail (-) More difficult to detect patterns and reach conclusions (-) Subjective, affected by personal expectations and beliefs 1.1b) Quantitative data Quantitative data is concerned with measuring “how much” it is data with numbers; the statistics are used to show, for example, the most common behaviours, thisdata can be obtained by categorising and rating behaviour. In using quantitativedata it becomes easier to see patterns in the data and easier to summarise and present it. Statisticalanalysis allows statements regarding significance to be made. Although care is needed to avoid bias in defining unitsor deciding which behaviours fit particular units. It includes Quantity of research, quantity of data, objectivity-measurement and numbers, breadth of information, comparison of information and cause and effect (symptoms). Some examples Frequency counts, measures of central tendency and dispersion, bar charts and histograms. (+) Easy to analyse (+) Produces neat conclusions (-) Oversimplifies reality (-) Experimenter bias, demand characteristics 1.1c)...