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Update: The comment period for Version 1 closed on August 22, 2016. Version 2 will be released for comments in January 2017. Archivists and special collections librarians should direct further comments to Task Force co-chairs Amy Schindler amycschindler@gmail.com (SAA) and Christian Dupont christian.dupont@bc.edu (ACRL/RBMS).
Count the number of Reading Room Visits made by Registered Users during a 24-hour period, beginning and ending at midnight. Count each Registered User once and only once during the 24-hour period regardless of how many Visits they make during the period and regardless of the visit length.
Guidelines for collection:
Reader Days can be tallied manually by creating a daily list of individual Users who enter the Reading Room, and then counting up the number of unique Users who were admitted to the reading room that day.
Visits can be tallied upon entrance or exit from the Reading Room (in a properly managed and secure environment, the number of entrances and exits should, of course, be the same).
Application and examples:
If a User is admitted to the reading room at 10:00am and works until noon, then signs out to take a lunch break, and returns at 1:30pm and works for another hour, count one Visit only.
If a User is admitted to the reading room at 10:00am, briefly consults one item, and then leaves at 10:15am for the rest of the day, count one Visit.
If a User is admitted to the reading room on one day and returns the next day to consult the same or new material, count two Visits.
Calculate the cumulative time that a User spends in the Reading Room during a 24-hour period, beginning and ending at midnight. Record the measure in hours or minutes.
Guidelines for collection:
This measure can be obtained by manually recording and tabulating values, but is more effectively obtained by entering Reading Room sign-in and sign-out times in a spreadsheet or an automated system that can calculate and report the total amounts of time that individual Users spend in the Reading Room each day.
Application and examples:
If a User is admitted to the Reading Room at 10:00am and works until 12:00pm, then signs out to take a lunch break, and returns at 1:30pm working until 3:15pm, record a total Visit length of 3.75 hours or 225 minutes.
If a User is admitted to the Reading Room at 10:00am, quickly consults one item, and then leaves at 10:15am for the rest of the day, record a total visit length of 0.25 hours or 15 minutes.
If a User is admitted to the Reading Room at 9:00am on the first day and leaves at 11:00am, and then returns the next day at 10:00am and leaves at 12:30pm, record a Visit length of 2.0 hours or 120 minutes for the first Visit and a Visit length of 2.5 hours or 150 minutes for the second Visit.
Total Visits per day
Graphing the total number of Visits per day over a given period of time can reveal usage patterns. For instance, at academic institutions, total daily Visits might increase towards the end of the semester, when research papers are due. For all institutions, total daily usage may increase for specific projects, during specific business cycles, or certain times of the year.
Comparing the total number of Visits per day/week/month for multiple years in succession can reveal fluctuations in usage levels and trends.
Average number of Visits per day
Calculating the average number of Visits per day for a given period can provide a good baseline metric for comparing activity levels at different reading rooms or repositories. Reading Room size and staffing needs would naturally be different at an institution that receives an average of 0.8 Visits per day than one that receives 18 Visits per day.
Average Visit length
Comparing the average visit length for a given period can reveal usage patterns and trends.
Comparing the average visit length by User type for multiple years can reveal fluctuations in usage trends.
Unique Registered Users
Comparing the number of Registered Users from year to year can reveal usage patterns and trends.
Repositories may be able to attribute differences in Registered Users year to year to effective outreach programs, publicity, improved facilities, or other Repository changes.
Newly Registered Users
Comparing the number of newly Registered Users for a given time period can reveal usage patterns. Comparing data may reveal the effectiveness of outreach and marketing programs.
Compare the return rate of Users; how many Users return to use the Repository multiple times in a given year or year to year.
Ratio of newly Registered Users to total Users
Compare the number of newly Registered Users as a percentage of total Users.