An Experimental Approach to the Construction of Binary Decision Classes from Card Sort Data

• Main Author: Almestadi, Emad Hamdan
• Other Authors: Hepting, Daryl, Mouhoub, Malek, Maguire, Brien
• Format: Thesis
• Language: English
• Published: Faculty of Graduate Studies and Research, University of Regina 2013
• Subjects: Face perception > Testing; Decision making > Mathematical models; Decision trees; First Nations
• Online Access: http://hdl.handle.net/10294/5305; http://ourspace.uregina.ca/bitstream/handle/10294/5305/Almestadi_Emad_200303164_MSC_CS_Fall2013.pdf

A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Science in Computer Science, University of Regina.x , 108 l. This thesis presents work done towards understanding the data collected from a card sorting study of facial photographs. In that study, 25 participants sorted 356 photos (178 Caucasian and 178 First Nations) into piles based on similarity. Photos placed in the same pile are deemed to be similar, photos in different piles are deemed to be dissimilar. Looking to establish binary decision classes is reasonable because the underlying question that participants answered was “Are these photos similar or not?”. There may also be more than two decision classes to describe all the behaviours. For example, an initial split into decision classes may be thought of as “doing something” and “not doing something”. The latter could be split into two, and the whole process repeated. Differences amongst the sorting behaviours of participants are evident, but the reason for these differences is difficult to determine. An early hypothesis was that perceived race was being used a criterion for some participants but not for others. An analysis that looked at the ratio of Caucasian and First Nations photos in each pile was used determine a pair of decision classes from which accurate classifiers could be built. Open questions from that earlier work include the basis for participants making those decisions and whether the behaviour supported by a small amount of carefully chosen data would be supported by all the data. There are several million possible decision class pairs that could be used to split those 25 participants into 2 groups. This work applies a knowledge discovery approach to find other candidate decision classes for this data, for which accurate classifiers can also be built. Each participant made a relatively small number of direct comparisons and a large number of indirect comparisons to determine whether each pair of ...