Weft QDA is a tool for narrative/text analysis that can be used to organize a number ot text sources into something akin to a database structure. It provides the ability to categorize text into thematic segments and provides a powerful search/query functionality for these.
The best way to describe what Weft QDA (short for Qualitative Data Analysis) can do is to illustrate using an example. Imagine that you are writing an article on the differences and similarities between, say, the Democratic candidates for the US presidency, and that the material you are using for this are various transcripts of debates and public appearances that the candidates have made.
You can use Weft QDA to outline the different candidate’s positions on different issues, to quickly compare/contrast their stances, and to quickly find sections within the narrative that deal with a certain subject(s).
This is a rough outline of how you might work with Weft QDA to do this:
- First off, you would load all of the different transcripts and texts into your Weft QDA project. These can be .TXT or .PDF files
- After this, you would go create categories that make sense for your analysis. Assuming there are 7 candidates, for example, you would start off by creating a category for the name of each of the candidates. You can also create subcategories (e.g. “Foreign policy” might be a category, and “Iraq”, “Iran”, “Pakistan”, “Russia”, etc. would be subcategories). Creating categories will be an ongoing process that evolves with your analysis.
- Next, you would need to go over your texts and create categorized clippings, as follows: for, say, your “Hillary Clinton” category, you would go over the transcripts and highlight all statements made by her and associate them with her category (a process known as coding).
- Note that categories overlap (e.g. Hillary Clinton’s statements on Iraq also categorized under the “Iraq” category, and, say, a snippet mentioning Iran’s influence in Iraq would also be categorized under the “Iran” category. An entire transcript of a debate that took place in, say, July 2007 can be categorized under a “July 2007” category.
- What you get, once all of the coding is done, is the ability to do queries that produce all sorts of information in very useful ways: a search that will give you the various candidates subjects on different issues (say, Iraq) side by side, a search that can give you all the statements that, say, a candidate said about Iran in the context of their statements on Iraq, and so on.
More info on the software itself:
- The user interface: is split into 2 main screens, a documents and categories screen (which lists all of the text you are working with and all the categories/subcategories you created), and a screen for the text itself that you are working on. It works rather well, although I am not sure exactly why it is not possible to switch across categories from that screen even when there is a dropdown that seems like it should have been designed for that purpose.
- Search/query capabilities: are rather advanced. Search for individual terms (as in any application) or create complex queries containing multiple clauses and operators (clauses are “coded by” and “contains word”, while the operators are “And, Or, And Not”). Don’t worry about this terminology; suffice to say you have a good, solid search tool under your displosal.
- The Help file: is very thorough and really good. It took me literally about a half an hour to figure out how to work with this program.
The verdict: this is a unique and exciting (not to mention original) tool that can be really useful for writers, journalists, researchers, or anybody who work with text and/or qualitative data analysis. Its not for everyone, obviously, but it seems like something you can use I assure you that you will find it a good, solid tool that will give you as much as you put into it.
Version tested: 1.0.1
Compatibility: Windows, Linux.
Go to the program page to get the latest version (approx 2.66 megs).
