%0 Conference Proceedings
%T Did We Miss Something? Correspondence Analysis of Usability Data
%+ Department of informatics and Media [Upssala]
%+ Department of Computer Science and Engineering [York]
%A Zabramski, Stanislaw
%A Stuerzlinger, Wolfgang
%Z Part 1: Long and Short Papers (Continued)
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 14th International Conference on Human-Computer Interaction (INTERACT)
%C Cape Town, South Africa
%Y David Hutchison
%Y Takeo Kanade
%Y Madhu Sudan
%Y Demetri Terzopoulos
%Y Doug Tygar
%Y Moshe Y. Vardi
%Y Gerhard Weikum
%Y Paula Kotzé
%Y Gary Marsden
%Y Gitte Lindgaard
%Y Janet Wesson
%Y Marco Winckler
%Y Josef Kittler
%Y Jon M. Kleinberg
%Y Friedemann Mattern
%Y John C. Mitchell
%Y Moni Naor
%Y Oscar Nierstrasz
%Y C. Pandu Rangan
%Y Bernhard Steffen
%I Springer
%3 Human-Computer Interaction – INTERACT 2013
%V LNCS-8120
%N Part IV
%P 272-279
%8 2013-09-02
%D 2013
%R 10.1007/978-3-642-40498-6_20
%K shape
%K freehand
%K tracing
%K drawing
%K mouse
%K pen
%K stylus
%K touch
%K evaluation
%K comparison
%K error
%K measurement
%K subjective
%Z Computer Science [cs]Conference papers
%X We have applied a multivariate exploratory technique called Correspondence Analysis (CA) to create and analyze a model of the dataset of experiment results. The dataset originates from a comparative usability study of tracing with the use of mouse, pen, and touch input and contains both categorical and continuous data – i.e. results of questionnaires and task measurements. CA allowed to visually and numerically assess the main variables in the dataset and how they interact with each other. In our study, pen input had the best measured performance and was preferred by the users. Touch input was the least accurate of all input methods tested but it was preferred by users over mouse especially in the conditions lacking of visual feedback of drawing. CA helped to detect that secondary effect even though it cannot be explained by the performance results alone. The importance of the influence of user’s previous experience is also noted. We conclude that CA helped to identify all major phenomena known from previous studies but also was sensitive to minor and secondary effects, what makes it a well suited method to quickly evaluate usability data.
%G English
%2 https://inria.hal.science/hal-01510508/document
%2 https://inria.hal.science/hal-01510508/file/978-3-642-40498-6_20_Chapter.pdf
%L hal-01510508
%U https://inria.hal.science/hal-01510508
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC13
%~ IFIP-INTERACT
%~ IFIP-LNCS-8120