Chapter 16: Analysing Qualitative Data
A. Qualitative Data Management
Step 1: Familiarize yourself with appropriate softwarePrograms worth exploring include:- NVIVO, MAXqda, The Ethnograph – used for indexing, searching, and theorizing- ATLAS.ti – can be used for images as well as words- CONCORDANCE, HAMLET, DICTION – popular for content analysis.- CLAN – popular for conversation analysisStep 2: Log in your dataIt is rare that qualitative comes in at the same time, or in the same form, and can end up being a lot messier than a pile of questionnaires, so it is wise to keep track of your qualitative data as it is collected.Step 3: Organize your data sourcesThis involves grouping like sources, making any necessary copies, and conducting an initial cull of any notes, observations, etc., not relevant to the analysis.Step 4: Read through and take overarching notesIt is extremely important to get a feel for qualitative data. This means reading through your data as it comes in and taking a variety of notes that will help you decide on the best way to sort and categorize the data you have collected.Step 5: Prepare data for analysis/transcriptionIf using a specialist QDA program, you will need to transcribe/scan your data so that it is ready to be entered into the relevant program.Step 6: Enter data/get analysis tools preparedIf you are using QDA software, you will need to enter your electronic data into the program. If you are manually handling your data, you won’t need to ‘enter’ your data, but you will need to arm yourself with qualitative analysis tools such as index cards, whiteboards, sticky notes, and highlighters.
B. Steps in Qualitative Analysis: Stepping Your Way through Effective Qualitative Data Analysis
Step 1: Identifying biases/ noting overall impressionsDoing this fully is extremely important. If you do not acknowledge preconceived notions and actively work to neutralize them, you are likely to find exactly what you expect to find!Step 2: Reducing and coding into themes your dataThis involves building both categories and subcategories that are likely to expand as you work your way through your data.Step 3: Searching for patterns and interconnectionsYou are likely to have overlapping themes across your data sources – so this step asks you to search for commonalities and divergences.Step 4: Mapping and building themesOne small section of a preliminary map might be as follow s (take music video analysis as an example). From here you would a) continue mapping all the main themes b) create even more sub categories as appropriate; and c) map various interconnections. Don’t forget to call on the literature in doing these tasks.Step 5: Building and verifying theoriesThis is your “hey you know what might be going on here” moment that will hopeful dawn on you as you go over your data for the 103rd time and play around with your maps for the 72nd time.Step 6: Drawing conclusionsYou are likely to find out much more through the processes than you could possible share, so you will need to decide what is most significant/ important and link this back to your project’s main questions, aims and objectives in the most compelling and credible way.