Qualitative data is predominantly in the form of words. For example, it can be notes of a discussion during an interview or focus group or a transcript from a recording of an interview or focus group. It is challenging to analyse qualitative data and the process is subjective so it's important to follow as rigorous an approach as possible.
If the data collected is in the form of notes it's important that these are written up as soon as possible so that what is recorded makes sense and that they can be elaborated on if need be. If the data is taken from audio or video recordings the transcriptions should match exactly what was recorded and not be corrected to be grammatically correct.
All data collected should be clearly labelled with the date and location the research took place, the interviewer/group facilitator and the participants' demographic details (such as male, under 25, Sheffield). This will help you draw out any potential themes at the analysis stage.
The most frequently used approach to analysing qualitative data is content analysis. It involves reading and re-reading the data looking for similarities and differences. These will form the themes/categories used in your analysis. As you repeatedly go through the data you may find that your themes develop slightly and you create subthemes. It is important that this is carried out in a systematic way, making sure that you don't leave any of the text out, to avoid a bias interpretation of the data.
It is helpful to have the data relating to one theme together, for example quotes that relate to attitudes about drink driving. Some people use computer software (e.g. Nvivo, Atlas.ti) to label themes/categories but it can also be done by creating a table in Excel or highlighting transcripts in different colours.
The presentation of your findings should, usually, follow the themes that have come out of your analysis. This should be factual based on what has been found. It is often useful to use quotes from your data to illustrate a point and bring the findings to life but they should also be used sparingly. A large number of quotes can suggest a lack of interpretation.
Quantitative data is in the form of numbers. For example, the percentage of people who ticked "strongly agree" in questionnaire or survey. Before quantitative data can be analysed it should be checked and entered into a database, such as an Excel spreadsheet or more specialised statistical software such as SPPS, Systat, or S-plus.
Descriptive statistics provide simple summaries about the data and illustrate what the data shows. For example, the percentage of male/female, ages of respondents, measure of road safety knowledge, attitude rating scales towards enforcement activity.