Empirical Research in Affective Computing: An Analysis of Research Practices and Recommendations

Abstract

In the last decade, empirical sciences have faced a tremendous change in the way of conducting research. As a broad interdisciplinary field, research in Affective Computing often employs empirical user studies. The current paper analyzes research practices in Affective Computing and deduces recommendations for improving the quality of methods and reporting. We extracted a total of k = 65 empirical studies from the two most recent International Conferences on Affective Computing & Intelligent Interaction (ACII) ’17 and ’19. Three raters summarized characteristics of studies (e.g., number of experimental studies) and how much methodological (e.g., participant characteristics) and statistical information (e.g., degrees of freedom) were missing. Also, we conducted a p-curve analysis to test the overall evidential value of findings. Results showed that 1. in at least half of the studies, one important information about statistical results was missing, and 2. those k = 31 studies that had reported all necessary information to be included into the p-curve showed evidential value. In general, all criteria were never met in one single study. We provide concrete recommendations on how to implement open research practices for empirical studies in Affective Computing.