Statistics! What do They Mean? (iCon 2013)


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  • At ECU, there are three major reasons we keep statistics. Today, I’m going to talk about the first two.
  • This was our first venture into ‘connecting’ to the university. In other words, doing things that only academic departments did.We were the first support unit to undertake assessmentNow, all are in the process of beginningLibrarians are faculty – assessment further connects us to ‘teaching’ faculty
  • This is what our high quality resources looked like. After three years, we had to make some decision.
  • Who really cares that the library checked out 10,000+ items or had over 84,000 full-text uses.Even though we were doing university things, we still felt dis-connected.Our first UAC review proved that – they didn’t like anything we had done since there was no connection to SLOs.
  • University (Director of Assessment) agreed to pay for SAILS.Instructional Services Librarian maintains university account, along with distribution of tests and results.
  • Results are shared with academic departments to help market our instruction program.Teaching faculty have responded favorably to the shared results.Instructional Services continues to monitor trends in SAILS results and suggest curriculum to improve areas of weakness.
  • Dean approached me last summer to development guidelines.She was tired of cutting, pasting, and having to reformat for font and table size.Process took her more time than writing the actual report.
  • Our hopes were that now everything was connected to an academic department.Using Excel, it’s easy to group the departments into colleges/schools.We also report by classification and patron type, but I’m only going to talk about the Major code.
  • Each department and their code is listed.Each department given the color of their designated college/school.Easy to sort by college/school to group departments.
  • For example, the College of Education & Psychology make up 25.51% of ECU’s enrollment.24.04% of academic circulations22.03% of academic collections31.75% of electronic resource use (connections only – we’ll talk more about this in a minute)Only 13.39% of academic library instructions
  • Here are some examples of our graphs and charts.Top left example – other should be orange
  • WAM allows for access to resources from any internet connection.User does not have to setup the proxy.Based on IP.For now, only non-campus networked computers.Considering moving to all connectionsWould make patrons have to loginWould eliminate community patrons
  • Yet another spreadsheetMonthly gathering of statisticsWho else does this?
  • Just like in the master table, academics are compare against other academics. Faculty/staff not coded with academics – limitation of university systemGeneral Studies degree not connected to any academic departmentNon-student – special programs on campus (Upward Bound) with loginsNon-verified (all campus networked computer logins)
  • Only used full-text providing databasesDepartmental connections divided by total connections (for all academic departments) to get percentageMultiply percentage by total full-text for that particular databaseTotal calculations are sums of each row to ensure that they equal 100 and full-text total for that db
  • Goal: to increase number of verified connectionsPossibility: have login required from all computersCon: eliminates community patronsThe next handout is a collapsed version of handout 2 – quickly shows full-text percentages of connections by majorIntended to share with departmentsTotal numbers included in budget allocation formula
  • As you can see here, the School of Business’ (the smallest academic entity on campus) connections far exceed their enrollment.
  • There are 16 departmental annual reports, each with an appendix (raw data) fileInstead of having to remember which department does what at the spur of the moment (we can, but can your dean?), the numbers from the annual reports are transferred to the Master Table.Master Table is shared with the provost and all deans at the June Academic Affairs Council meetingAnother handout to share at departmental meetingsFor now, the Master Table is my College/SchoolCould easily be done by department, as wellEach liaison could adapt the Master Table to their own departmentsMaybe a Master Table for each College School showing the individual departmentsThe numbers are all in the annual reports
  • This is the collapse version of the Master Table.
  • Your next handout is the full version with defined items and ownership by library department.The numbers are also transferred to a 5 year spread table for strategic planningThe next handout is the same table but for all those OTHER numbers not attributable to an academic department.Goal: decrease the numbers in OTHER, increase the numbers for the main Master Table.
  • Statistics! What do They Mean? (iCon 2013)

    1. 1. LINSCHEID LIBRARY Statistics! Librarians Love Them, But What Do They Mean? Dana Belcher, Asst Library Director East Central University iCon 2013 August 2, 2013
    2. 2. Why Keep Statistics?LINSCHEIDLIBRARY Isn't that why we became librarians?
    3. 3. Assessment • Measurement of services & resources • Student Learning Outcomes!!!! Annual Reports • Checkouts • Purchases • Instructions • Use, etc. Surveys Types of StatisticsLINSCHEIDLIBRARY
    4. 4. Began working with the University Assessment Committee in 2006 • All about student learning outcomes • Struggle for libraries • We don’t see the end results from our instructions • We don’t grade papers • ECU focused on reference & instruction services, and high- quality resources. Assessment LINSCHEIDLIBRARY
    5. 5. High Quality ResourcesLINSCHEIDLIBRARY Access Numbers 2008 2009 2010 2011 Difference from last CY % Change Database Full-text 79,436 83,230 78,240 84,207 5,967 7.63% ILL Borrowing Articles 520 793 568 743 175 30.81% ILL Borrowing Books 407 413 308 527 219 71.1% ILL Lending Articles 439 384 313 305 -8 -2.56% ILL Lending Books 575 591 505 410 -95 -18.81% Circulation Checkouts 8,909 10,212 9,418 10,448 1,030 10.94% Circulation Renewals 1,101 883 872 765 -107 -12.27% Reserve Checkouts 2,113 1,301 1,463 3,140 1,677 114.63% E-reserves 102 282 1,352 1,676 324 23.96% In-house use monographs 2,281 4,299 6,158* 4,710 -1,448 -23.51%
    6. 6. • Numbers made no sense • No trend could be detected • Focus was on big picture • Entire collection numbers • How does that fit into the university? Shell GameLINSCHEIDLIBRARY
    7. 7. Moved to total Student Learning Outcomes (SLO) • SAILS – university buy in • UNIV 1001 Freshman Seminar • UNIV 3001 General Ed Seminar • In-house Assessment tools • SAILS 1.2 Developing appropriate search terms • SAILS 3.2 Articulating evaluation criteria Tools Assessment RefocusedLINSCHEIDLIBRARY
    8. 8. Freshman compared to Juniors: • Instruction numbers • Can now help in decision making • What SAILS criteria were weak – beef up instruction in those skills • What SAILS criteria improved – did instructions during those two years correlate? • Provide results by discipline to academic departments – lead-in to future information literacy sessions Results LINSCHEIDLIBRARY
    9. 9. • More numbers! • Still big picture with no correlation to academic departments or the university • No structure among multiple library departments • Refocus was needed, desperately Annual ReportsLINSCHEIDLIBRARY
    10. 10. First focus - to create a template • Provost’s requirements • Work plan items • Program data & accomplishments • Data in context • Departmental Projects • Personnel • Summation Template LINSCHEIDLIBRARY
    11. 11. No more big picture • Break down into academic departments • Group departments into colleges & schools ILS (Innovative) • PCODE2 = Classification • PCODE3 = Major (based on Admission’s code) • PTYPE = Type of Patron Data in ContextLINSCHEIDLIBRARY
    12. 12. Master file in Excel of P codes & colors: PCODE3LINSCHEIDLIBRARY
    13. 13. The big picture is now in smaller, more digestible chunks. No longer what the library has done, but who and how it is being used. • Provides the needed connection to the university & individual academic departments. Now easier to compare these numbers to external numbers, i.e., enrollment. ResultsLINSCHEIDLIBRARY
    14. 14. Web Access Management (WAM) • Tracks connections to databases from non-institutional networked computers • Laptops • Smart phones • Home computers Tracked since 2007-2008 • Never used data except to report number of connects • Does report by PCODE3 Unused DataLINSCHEIDLIBRARY
    15. 15. Vendor supplied COUNTER statistics • Proves resources are being used • Doesn’t tell you who uses them • Takes a lot of time gathering them Problem: How do connects (WAM) intersect with COUNTER statistics? Solution: Excel and percentages Aggregate Vendors LINSCHEIDLIBRARY
    16. 16. Number of database connections by major for AY12-13 • Majors listed with ‘total’ indicate more than one degree available • Connects not associated with a college/school are segregated out • Not included in totals/percentages used in calculations • Allows comparison of apples to apples Handout 1LINSCHEIDLIBRARY
    17. 17. Number of connections compared to full-text use • Eliminated any databases not providing full- text • Inserted two rows between databases • First: divided each major’s total connects by the total connects of all majors to come up with a percentage of connects • Second: took the percentage of connects and multiplied by the total full-text (COUNTER) for that database • The total for the percentage row for all majors = 100% Handout 2LINSCHEIDLIBRARY
    18. 18. At-a-glance, you can see what databases are being used by each academic department. Reminder: these statistics only track connections made by non-institutional networked computers. For AY1213, total connections = 1,115,228 with 249,849, or 22.40% from institution networked computers. For AY1213, total connections by major = 762,921, or 68.41%. Remaining connections were made by non-academic department entities.
    19. 19. Using the previously mentioned master Excel file of patron codes, I can quickly insert college/ school codes to group academic departments.
    20. 20. Connects all the pieces into one picture • Enrollment numbers provided by Academic Affairs • All other numbers provided in annual reports • Findings: • AY1213 – not all library statistics gathered based on PCODE3, or major • AY1314 – steps have been implemented to gather as much as Master TableLINSCHEIDLIBRARY
    21. 21. At-a-glance, the correlation between size of college/school and parts of the library. • It’s no longer that the library had 9,000+ checkouts, but that CEP had 24% of the checkouts and they are 25% of the total enrollment. • Easier to see where there are strengths and weaknesses.
    22. 22. Master Table Exploded: • Each item is coded to a library department. • All numbers entered come directly from departmental annual reports. • Highlighted areas weren’t counted by major for AY1213. • All areas are now being counted by major for AY1314 thru use of other ILS codes or Excel functions. Other Master Table – designed the same & includes all OTHER statistics
    23. 23. Libraries need to make internal statistics correlate more to the university • Refocused assessment to true SLOs, providing individual results to academic departments. • Refocused annual reports to also provide individual results to academic departments. Recap LINSCHEIDLIBRARY
    24. 24. Dana Belcher 580.559.5564 Any files shown or spoken about are available – just email me. Follow-upLINSCHEIDLIBRARY