1. Sampling in-library use Statistics in practice: Measuring and managing Sebastian Mundt sebastian.mundt@unibw-hamburg.de Sebastian Mundt sebastian.mundt@unibw-hamburg.de

2. Selection Examples Conclusions Framework

3. “Official“ library statistics so far only allowed the full count: “Data referring to a period should cover the specified period in question, not the interval between two successive surveys.“ (ISO 2789:1991) In libraries sampling has traditionally been used... - in performance measurement (e.g. correct shelving). - in user surveys - for catalogue evaluation Sampling in library statistics

4. Consequence: Important activities of use have previously not been reported in most countries. - practically impossible - too monotone. - too time consuming (costly), The full count of some measures would be... The revised International Standard ISO 2789:2001 now recommends sampling methods for... - in-house use - visits (gate count). - information requests Libraries Library use (lending) Collections Expenditure 7 7 4 4 19 35 41 Library staff Library use (other) 4 4 Category # datasets (ISO 2789:1991) Sampling in library statistics

5. Selection Examples Conclusions Framework

6. - time (reporting period) Sampling can be “selective“ as regards... Sampling is selecting a subset of the population in question. - location (branch, service point) - objects (media) - persons (satisfaction, user behaviour) A sample can be drawn randomly or not. The “accuracy“ of random samples can be measured in terms of error and confidence level. It depends on the sample size and the variance of the sample. Sampling

7. NISO Z39.7-2002 Draft Standard for Trial Use, Data Dictionary Version 2002a “A “typical week“ is a time that is neither unusually busy nor unusually slow. Avoid holidays, vacation periods, days when unusual events are taking place in the community or in the library. Choose a week in which the library is open its regular hours.“ ISO/FDIS 2789:2001 “The annual total is to be established from a sample count. The sample should be taken in one or more normal weeks and grossed up.” purposive (judgement) sampling highly dependent on staff experience requires mimimum statistical knowledge Selection procedure

8. Visits per weekday (Münster UL) 100% 99,5% 96,8% 91,8% 85,1% 24,8% mon tuewedthufrisat Administration: weekwise count is easier to organize. Cluster: weeks comprise days of different activity level. “Typical week“

9. Periods of average activity as estimated by reference staff % deviation of visits from annual mean (Münster UL) “Typical“ weeks can hardly be anticipated even from data collected over several years. “Typical week”

10. -17,4% +16,9% -17,8% +21,7% -23,2% +22,9% +12.4% -20.5% 1998 +15,3% -11,6% +15,8% -15,1% min (staff) max (staff) min (all) max (all) Minimum/maximum values (Münster UL) 19992000 Data collected by purposive (judgement) sampling are a weak foundation for comparisons. “Typical week“

11. Selection Examples Conclusions Framework

12. Maxstadt, J.M. (1988): A new approach to reference statistics, C&RL (Feb. 1988), p. 85-88 Louisiana State University Libraries (reference statistics) A sample size of 52 hours (of 4,103 hours of service a year) was calculated given a confidence level of 90% and an error of +/- 11.23% Randomly and individually selected hours of the year (simple random sample) Hourwise count is difficult to administer. Total estimated by linear extrapolation Similar (daywise): Bauer, K. (2000): Gathering ARL reference data, http://info.med.yale.edu/assessment/methods.html Selection method: case 1

13. Kesselman, M.; Watstein, S.B.: The measurement of reference and information services, JAL (1987, 1), p. 24-30 New York University / Bobst Library (reference statistics) Based on reference data of previous year, weeks were “classified“ in high, medium and low usage (stratified random sample). Sample size of 15 weeks was calculated given a confidence level of 95% and error of +/- 400 [ 10%]. Additional information (past data) is used to improve the sample. Linear extrapolation of weighted class means. Separation of high and medium weeks difficult. Selection method: case 2

14. University of South Carolina / Thomas Cooper Library (reference statistics) Extrapolation relative to boundary distribution (gate count) Lochstet, G.; Lehman, D.H.: A correlation method for collecting reference statistics, C&RL (Jan, 1999), p. 45-53 Found extremely high correlation (.957) between reference activity and gate count. Deals with “missing“ days. Selection method: case 3 Allows small random sample of a few weeks once high correlation is confirmed.

15. Münster University Library visits reference reserv. inside reserv. remote account info renewals short loans normal loans visits refe- rence reser- vations inside reser- vations inside reser- vations remote reser- vations remote account info rene- wals short loans 1.000.876** 1.000.802**.751** 1.000.437**.347.269** 1.000.800**.765**.796**.220** 1.000.523**.512*.568**.156**.759** 1.000.473**.383.558**.117.312**.140* 1.000.506**.057.656** -.019.508**.283**.483** Which data from the library system can be used as boundary distribution? In branch libraries the same datasets are collected. These can be used to extrapolate the sample count for visits and information requests. Selection method: case 4

16. University of the FAF / University Library, Hamburg Over the first half of 2002 no relationship between branches was found: Does reference activity in different branches correlate significantly? Branch 1 Branch 2 Branch 3 Branch 1 Branch 2 Branch 3 Branch 4 1.000 -.064 1.000.022 -.041 1.000.122.058.031 1.000 - 4 branch libraries (3 interconnected) with separate service points and entrances in one building Sampling locations Münster University Library Does reference activity in different branches correlate significantly? main reading Branch A main reading Branch A 1.000.437* 1.000.593*.559* 1.000

17. Selection Examples Conclusions Framework

18. It is, however, likely that certain usage data show significant correlation and provide useful information for estimating totals. If data from automated systems are used for correlation the workload of sampling can be reduced. From the point of data collection management it seems useful to choose a week as sampling unit. In-library use activities correlate with in-library use of automated systems. Significant remote use should be correlated separately (e.g. frequent e-mail reference). “Normal“ weeks can hardly be anticipated even from data collected over several years. Conclusions Sampling locations might reduce the workload of data collection further. Results, however, are ambivalent.