How do you collect and organize your discourse data for analysis?

1 Choose your research question and approach

Before you start collecting data, you need to have a clear research question and a suitable approach for your discourse analysis. Your research question should be specific, relevant, and answerable by analyzing language use. Your approach should be based on a theoretical framework that guides your assumptions, concepts, and methods of analysis. For example, you can choose a critical discourse analysis approach if you want to examine how power, ideology, and inequality are expressed and reproduced in discourse. Or you can choose a conversation analysis approach if you want to explore how speakers accomplish social actions and manage interactional norms in talk.

2 Select and access your data sources

Next, you need to decide what type of data you want to analyze and how to access it. You can use primary or secondary data sources, depending on your research question and availability. Primary data sources are those that you collect yourself, such as audio or video recordings of natural or elicited speech events, or written texts that you produce or solicit from participants. Secondary data sources are those that are already available, such as transcripts, documents, or media texts that are relevant to your topic. You should consider the ethical and practical issues of obtaining consent, protecting anonymity, and ensuring quality when selecting and accessing your data sources.

3 Transcribe and annotate your data

If you are using spoken data, such as recordings or interviews, you need to transcribe them into written form for analysis. Transcription is a process of representing the features of speech in a systematic and consistent way, using symbols, conventions, and formats. You should follow a transcription style that suits your research question and approach, and that captures the relevant aspects of the spoken data, such as pauses, intonation, overlaps, or gestures. You can use software tools, such as Transana or ELAN, to help you transcribe and annotate your data. Annotation is a process of adding notes, labels, or codes to your data to mark specific elements, such as topics, speakers, or discourse markers.

4 Organize and store your data

Once you have transcribed and annotated your data, you need to organize and store them in a way that facilitates your analysis and protects your data security. You should create a system of naming, labeling, and categorizing your data files, such as by date, source, or theme. You should also backup your data regularly and use encryption or password protection to prevent unauthorized access or loss. You can use software tools, such as NVivo or ATLAS.ti, to help you organize and store your data. These tools can also help you manage, query, and visualize your data for analysis.

5 Prepare your data for analysis

Finally, you need to prepare your data for analysis by selecting, sampling, and sorting them according to your research question and approach. You should decide how much and which part of your data you want to analyze in detail, depending on your scope, purpose, and criteria. You should also decide how to sort your data into meaningful units or segments for analysis, such as by turn, utterance, sentence, or paragraph. You should use software tools, such as AntConc or WordSmith, to help you prepare your data for analysis. These tools can help you perform tasks, such as counting words or phrases, identifying patterns or collocations, or comparing texts.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?