The chapter focuses on the Bootstrap statistical technique for assigning measures of accuracy to sample estimates, here adopted for the first time to obtain an effective and efficient interaction evaluation. After introducing and discussing the classic debate on p value (i.e., the discovery detection rate) about estimation problems, the authors present the most used model for the estimation of the number of participants needed for an evaluation test, namely the Return On Investment model (ROI). Since the ROI model endorses a monodimensional and economical perspective in which an evaluation process, composed of only an expert technique, is sufficient to identify all the interaction problems-without distinguishing real problems (i.e., identified both experts and users) and false problems (i.e., identified only by experts)- they propose the new Bootstrap Discovery Behaviour (BDB) estimation model. Findings highlight the BDB as a functional technique favouring practitioners to optimize the number of participants needed for an interaction evaluation. Finally, three experiments show the application of the BDB model to create experimental sample sizes to test user experience of people with and without disabilities.
|Title of host publication||Cognitively Informed Intelligent Interfaces|
|Subtitle of host publication||Systems Design and Development|
|Number of pages||22|
|Publication status||Published - 1 Dec 2012|