eDom Output

Two files are generated for each participant.  

The first file, which has the same name as the participant number (e.g., 101) contains the dominance ratings for each item.  Each row contains several columns of data, divided under the following headers:

ssIdent, trialNum, known, endTime, defn.word, dominance, ...

signTest, biggest, orderedP(1), orderedP(2), orderedP(3), ...

orderedP(4), orderedP(5), orderedP(6)

• known indicates whether the participant rated the word (1) or pressed 'don't know' (0)
• endTime is the time, in milliseconds, between the presentation of a word and pressing 'done rating'.  The precision of this number has not been examined in detail.
• dominance = [(biggestPercentage - secondBiggestPercentage) / biggestPercentage], which yields a dominance score ranging between 0 (perfectly balanced first and second most frequenty meanings) and 1 (first meaning completely dominant).
• signTest indicates if meaning #1 was given the biggest percentage rating (1) or not (0).  This is suitable for running a sign test which evalutes whether the a dictionary correctly lists the most dominant meaning first.
• biggest is the largest percentage associated with a meaning for a given word.
• orderedP(n) lists the pecentages associated with each meaning of the word.  For definitions that were provided by the experimenter, these definitions are re-ordered to conform to the meaning # 1... n ordering they were input in.  For any additional meanings provided by the participant, these meanings are listed in the order in which they were provided in the boxes (left-to-right, top-to-bottom).  

For instance, a row in this file might look like:

101    0    1    3881    pupil    0.333333    0    (...) 
60    40    60    0    0    0    0

If a participant presses 'don't know', the non-applicable values will be set to 'NaN'.

The second file, named <participantNumber>.newDefs.txt, contains a list of the new definitions for a word that were provided by the participant.  Each row contains:

ssIdent, trailNum, word, percentage, definition|

For example, if a participant listed two new definitions for the word BANK, they would be stored as:

101    0    bank    60    The first new definition|

101    0    bank    40    The second new definition|

It may be useful to combine these output files for analyses (e.g., via ls | more > newFile).