Maurer, FrankChen, Qing2018-05-112018-05-112018-05-10Chen, Q. (2018). Immersive Analytics Interaction: User Preferences and Agreements by Task Type (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/31914http://hdl.handle.net/1880/106633For immersive computing environments, multiple interaction modes (e.g. voice, gestures, handheld controller) have been proposed. In this thesis, I present the results of an elicitation study examining user preferences and measuring interaction agreements, based on two task types from an existing task taxonomy, in the context of data interaction in augmented reality (AR). The results indicate a non-statistically-significant association between a user’s input mode preference and the type of the performed task in most cases. However, agreements on interactions were found to be higher in one type of task. I reflect on the resulting implications and offer one practical guideline for UX designers creating AR-based analytics applications. This thesis also details an alternative way of quantifying user agreements in an elicitation study on interactions.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.speech inputgesture inputelicitationimmersive analyticsaugmented realityComputer Sciencemaster thesis10.11575/PRISM/31914