Statistical Methods for Affective Computing
Tutorial presented at the IEEE International Conference on Automatic Face and Gesture Recognition
Statistical methods of data analysis emphasize inference and interpretability. As such, they are indispensable tools for enhancing scientific understanding, and they deserve a place alongside machine learning in the toolkits of scientists and engineers working in affective computing.
This tutorial will provide training on contemporary statistical methods with high relevance to conference attendees. Its emphasis will be on providing high-level intuitions and practical recommendations rather than exhaustive theoretical and technical details. Prior exposure to statistics, while helpful, will not be required of attendees. Applied examples, complete with syntax and write-ups, will be provided in both R (www.r-project.org) and MATLAB; tutorial attendees are encouraged to bring a laptop with one of these software packages installed.
Cross-cutting themes will include (A) measurement, (B) validity, and (C) uncertainty. Specific methods to be discussed include (1) measures of inter-rater reliability, (2) measures of criterion validity, (3) effect sizes, (4) confidence intervals, and (5) generalized linear modeling. These tools will help attendees answer such questions as: What are we measuring? How well are we measuring it? When are our measurements wrong? Do our measurements systematically vary across groups, times, etc.? How can we design our research studies to be maximally informative?
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