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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?

Resources: http://jmgirard.com/wp-content/uploads/2018/05/Resources_2018-05-15.zip

 

Methods Articles:

  • McGraw, K. O., & Wong, S. P. (1996). Forming inferences about some intraclass correlation coefficients. Psychological Methods, 1(1), 30–46.
  • Cumming, G., & Finch, S. (2001). A primer on the understanding, use, and calculation of confidence intervals that are based on central and noncentral distributions. Educational and Psychological Measurement, 61(4), 532–574.
  • Cumming, G., & Finch, S. (2005). Inference by eye: Confidence intervals and how to read pictures of data. The American Psychologist, 60(2), 170–180.
  • Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79–82.
  • Zhao, X., Liu, J. S., & Deng, K. (2012). Assumptions behind inter-coder reliability indices. In C. T. Salmon (Ed.), Communication Yearbook (pp. 418–480). Routledge.
  • Cizek, G. J. (2016). Validating test score meaning and defending test score use: Different aims, different methods. Assessment in Education: Principles, Policy & Practice, 23(2), 212–225
  • Girard, J. M., & Cohn, J. F. (2016). A primer on observational measurement. Assessment, 23(4), 404–413.
  • Luo, W., Phung, D., Tran, T., Gupta, S., Rana, S., Karmakar, C., … Berk, M. (2016). Guidelines for developing and reporting machine learning predictive models in biomedical research: A multidisciplinary view. Journal of Medical Internet Research, 18(12), e323.
  • Bunnage, M. (2017). How do I know when a diagnostic test works? In S. C. Bowden (Ed.), Neuropsychological assessment in the age of evidence-based practice (pp. 223–237). New York, NY: Oxford University Press.

Applied Articles:

  • Girard, J. M., Cohn, J. F., Jeni, L. A., Lucey, S., & De la Torre, F. (2015). How much training data for facial action unit detection? In IEEE International Conference on Automatic Face & Gesture Recognition.
  • Girard, J. M., Cohn, J. F., Jeni, L. A., Sayette, M. A., & De la Torre, F. (2015). Spontaneous facial expression in unscripted social interactions can be measured automatically. Behavior Research Methods, 47(4), 1136–1147.
  • Girard, J. M., Cohn, J. F., & De La Torre, F. (2015). Estimating smile intensity: A better way. Pattern Recognition Letters, 66, 13–21.
  • Girard, J. M., Chu, W.-S., Jeni, L. A., Cohn, J. F., De La Torre, F., & Sayette, M. A. (2017). Sayette Group Formation Task (GFT) Spontaneous Facial Expression Database. In Proceedings of the 12th IEEE International Conference on Automatic Face and Gesture Recognition.
  • Girard, J. M., & McDuff, D. (2017). Historical Heterogeneity Predicts Smiling: Evidence from Large-Scale Observational Analyses. In Proceedings of the 12th IEEE International Conference on Automatic Face and Gesture Recognition.
  • McDuff, D., Girard, J. M., & El Kaliouby, R. (2017). Large-scale observational evidence of cross-cultural differences in facial behavior. Journal of Nonverbal Behavior, 41(1), 1–19.

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