Recent advances in collecting and analyzing open-ended survey responses |
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| Coordinator 1 | Dr Ruben Bach (University of Mannheim) |
| Coordinator 2 | Dr Anna-Carolina Haensch (Ludwig Maximilian University of Munich) |
| Coordinator 3 | Professor Matthias Schonlau (University of Waterloo) |
Collecting and analyzing responses to open-ended survey questions used to be a tedious task. The steadily growing availability and popularity of web surveys and powerful machine learning and natural language processing techniques for data analysis in recent years have lowered the burden of collecting and analyzing open-ends by a lot, however. For example, respondents in a web survey may directly type in their response to an open-ended question or provide an audio record of their responses, which can later be transcribed automatically using speech-to-text algorithms. Natural language processing techniques and (un)supervised machine learning approaches allow, for example, to identify and analyze topics, sentiments, and stances in respondents' answers at unseen scale and speed. For unsupervised approaches, no manual coding of responses is necessary as, for example, clustering techniques are used to group answers by similarity. For supervised approaches, a sample of manually coded examples is used to train a model that may then be used to automatically code the remaining data, often with high levels of accuracy. With powerful large language models like GPT-4o, Llama 3, and the like, few-shot and zero-shot learning are now easily accessible for all researchers, including those with limited programming skills.
This session's goal is to bring together researchers from a variety of disciplines, such as survey research, statistics, and computer science. We will discuss recent advances in the collection and analysis of responses to open-ended survey questions using machine learning tools. We welcome submissions that address topics such as
- Statistical-learning / Machine-learning analysis of open-ends
- Speech-to-text algorithms for open-ends
- Large language models for analyzing open-ends
- Novel ways to collect open-ends
- Applied studies using novel analysis methods of open-ends