Program
9-11 - Workshop
11-12 - Help with analyzing your own material
12-13 - Lunch
Qualitative analysis is a cornerstone of social science research, used in fields as diverse as education research, sociology, history, and business. Traditional qualitative analysis, however, is time consuming, labor intensive, and difficult to scale and replicate.
To tackle these issues, we have devised a fast, replicable, and scalable technique for deductive, qualitative research on text-data, by utilizing novel advances in machine learning and artificial intelligence. The technique reduces many pain points of traditional qualitative analysis while maintaining the researcher's involvement in critical decision-making points. Moreover, it enhances the reproducibility and objectivity of the research, making it less dependent on the individual researcher’s subjective interpretations.
The method works by using text embedding models to transform textual data into high-dimensional vectors. The vectors are designed to capture the semantic content of the texts and is structured in a way where similar texts are spatially closer within that vector space. By utilizing the relative distances between different texts, this technique allows for analysis of a wide range of data types, from articles to interviews, surveys, and more.