Data analysis is the process of breaking down, conceptualizing, and interpreting your data to help you clearly address the initial research questions. There are numerous methods of data analysis, and which one is appropriate for your project will depend on the type of data you have and what you want to learn.

For example, if you are trying to understand why judges decide cases involving damage to property in a certain way and you have collected the transcripts of those cases, you might use a method of analysis called content coding to systematically analyze the reasoning of court cases. You would then have an empirical basis for arguing that one mode of reasoning was particularly common. Alternatively, if you want to understand a possible association between personality type and propensity to begin litigation in a survey, a statistical analysis called regression that checks for a relationship between these variables will be useful. In general, content coding and discourse analysis are useful for understanding the reasoning and nuances of a sample of texts; historical interpretation is useful for dealing qualitatively with archival documents; legal content analysis may be appropriate for empirically addressing questions about the nature of doctrine; and statistical analysis is useful for most quantitative or very large datasets.

Feel free to use different methods of analysis on the Portal, or to skip to a particular method if you already have a sense of what you need!