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README.md
README.md
 
 
card_mat.py
card_mat.py
 
 
cards.txt
cards.txt
 
 
data.txt
data.txt
 
 
data_raw.txt
data_raw.txt
 
 
example.py
example.py
 
 
female.txt
female.txt
 
 
high_edu.txt
high_edu.txt
 
 
high_inc.txt
high_inc.txt
 
 
high_income.txt
high_income.txt
 
 
low_edu.txt
low_edu.txt
 
 
low_inc.txt
low_inc.txt
 
 
low_income.txt
low_income.txt
 
 
male.txt
male.txt
 
 
mat_per_group.py
mat_per_group.py
 
 
med_income.txt
med_income.txt
 
 
median_test.txt
median_test.txt
 
 
mid_age.txt
mid_age.txt
 
 
old_age.txt
old_age.txt
 
 
rank.py
rank.py
 
 
rank_age_group20.py
rank_age_group20.py
 
 
rank_high_ed.py
rank_high_ed.py
 
 
rank_high_inc.py
rank_high_inc.py
 
 
rank_low_ed.py
rank_low_ed.py
 
 
rank_low_inc.py
rank_low_inc.py
 
 
rank_median.py
rank_median.py
 
 
test.txt
test.txt
 
 
test_age_20.txt
test_age_20.txt
 
 
test_diff.txt
test_diff.txt
 
 
test_high_edu.txt
test_high_edu.txt
 
 
test_high_inc.txt
test_high_inc.txt
 
 
test_low_edu.txt
test_low_edu.txt
 
 
test_low_inc.txt
test_low_inc.txt
 
 

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card_sorting_analysis

Card Sorting Study to gather user information activities, task groupings, and task priorities. We computed a similarity matrix based on the distance between different cards. Analysis methodologies include hiarchical cluster analysis and computing ranks using a customized algorithm.

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