Discuss Qualitative Methods Final Project: Content/discourse analysis of text

Discuss Qualitative Methods Final Project: Content/discourse analysis of text

Guidelines

This coursework consists of an independent, hands-on activity.

Specifically, you are asked to develop a small-scale project involving either ‘content’ or ‘discourse’ analysis (or some combination of both).

The core of this project should involve the systematic analysis of a political or policy text, or set of texts that have been published already.

Examples of political or policy texts to be assessed are: policy documents, party manifestos, candidate election leaflets, major political speeches, parliamentary debates, news reports, interviews, open-ended surveys, focus group transcripts, tweets, cartoons…

You can choose a document (or documents) of various lengths. But the key point is to work on a “critical mass” of text. Whether that critical mass is 10, 50, or 100 pages depends on your data, your angle, and your approach. For example, if you are into the hand-coding of text in the content-analysis tradition you may need a longer document (or compare multiple documents). On the other hand, if you want to go deep into the framing, meaning of a particular discourse a shorter document would be enough. In any case, I am flexible about the length and nature of the text to be analysed. It is whatever works best for your project.

Two key issues to be taken into account:

1) the project should be framed around an interesting problem and a concrete research question.

For example, you can assess how the social construction of ‘austerity’ has changed, or whether it changed in the first place, by systematically comparing the 2010 & 2020 budget speeches.

Or you could analyse the fears of no and yes voters (and/or supporters) using the QESB focus group data (see web link on Scottish Parliament or Government).

Again, there are hundreds of plausible questions on hundreds of topics. It is up to you to find the topic and the angle. The core message is the text analysis should aim to answer a specific question which is relevant in the real world.

2) the project should be grounded in a specific approach (e.g. inductive content analysis, or deductive content analysis, or inductive and deductive content analysis, or sentiment analysis, or critical/political discourse analysis…). These approaches might also be informed and/or juxtaposed alongside other approaches such as feminist theory, or the narrative policy framework.

Why is this important? Because you need to know what to bring into the analysis. In other words, you are expected to deconstruct a text along certain analytical lines. For example, the narrative policy framework highlights some crucial dimensions: the setting, the plot, the actors etc. A gender-based approach to budget speeches might point to other dimensions such as power, and hierarchy. The point is: you need some sort of analytical anchor. Just pick up a framework you find most relevant and/or inspiring.

There is no fixed template to organise this work. That said, a possible structure:

• Title and Abstract (@250 words)
• Framing of the problem, including a clear research question (@500 words);
• Presentation of analytical/methodological approach (@800 words);
• Analysis & discussion (@1,000 words);
• Conclusion & limitations & implications for literature and society (@400 words).

Please note:

Computer-assisted text analysis using NVivo and/or R etc is not a requirement here.

If you fancy the challenge, you can do it; but it is optional. The project is basically about your own capacity to deconstruct a text using traditional discourse content analysis and/or discourse analysis tools. The aim is to be as analytic and systematic as possible in the deconstruction of the text(s).

Finally, you are allowed, even encouraged, to work on topics related to your dissertations. You can even use the project to pilot dissertation ideas.

Further detail and issues will be discussed in the next 3 seminars covering content analysis, NVivo, sentiment analysis, R, and discourse analysis.