Sampling In-Library Use

Transcription

1 Sebastian Mundt Head of Acquisitions, University of the Federal Armed Forces Library, Germany Abstract In recent years, many libraries witness diminishing numbers of loans and physical visits; others face an increasing number of requests for learning facilities and study space inside the library. For practical reasons, however, the overall picture of these developments remains unclear as only few statistical data of library use can be collected in census form. The revised International Standard ISO 2789 International library statistics now allows libraries to sample certain statistical data for national reporting in order to monitor activities of in-library use. Through examples of real data, the presentation describes different methods of random and non-random sampling in the library environment, and it evaluates procedures to estimate annual totals from the sample count. Introduction Whenever a full count or census is practically impossible, too time-consuming or costly and/or too monotonous, libraries have traditionally applied sampling procedures to study specifics of the collection (Lancaster, 1993, p 51-75), interlibrary lending (Hasemann, 1977) and the use and revision of card catalogues (e.g. Lipetz, 1972; Bookstein, 1983). More recent applications focus on sampling for user surveys and on collecting data for performance indicators (e.g. Lancaster, 1993, p ). In general, sampling has been used to reduce complexity by selecting and analysing a subset of the population in question. It can be selective as regards time (reporting period) location (certain branches or service points) object (collection) subject (users, staff). Literature on sampling in libraries regularly provides thorough information and guidance on estimating percentages; examples mostly focus on user surveys. Statistics of library use, however, usually aim at total numbers. Selecting over time is the most widely applied form of sampling totals and will be the focus of this contribution. For the basics of sampling, especially for random and non-random sampling methods, sampling and measurement error and the calculation of sample sizes, the reader is referred to the contribution by Creaser in this volume and any standard textbook of statistics and performance measurement in libraries and information services. Non-random sampling To achieve the highest possible accuracy, official library statistics so far required that all statistical reporting should be based on a 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 most countries, important activities of use were therefore not reported on a national level. The new International Standard ISO 2789:2003 Information and documentation International library statistics now allows for the use of sampling procedures to estimate annual totals of library visits, in-house use and information requests. It notes that the annual total is to be established from a sample count and the sample should be taken in one or more normal weeks and grossed up. This Statistics in Practice Measuring & Managing

2 principle was regarded as the highest common factor for statistical reporting on the international level. It takes into consideration that this kind of purposive (judgement) sampling only requires basic statistical knowledge. Expanding upon this definition the NISO Z Draft Standard for Trial Use describes in its Data Dictionary Version 2002a a typical week as time that is neither unusually busy nor unusually slow and in which the library is open its regular hours. Holidays, vacation periods, days when unusual events are taking place in the community or in the library should be avoided. In the following example gate count data from Münster University Library are used to discuss the potentials and pitfalls of (1) weekly sampling and (2) sampling by judgement. Fig 1 displays the average number of gate counts per weekday between 1998 and Although the number of visits per weekday was not found to be normally distributed, visits to the library seem to follow a weekly pattern with relatively low standard deviation: note that the number of visits (gate counts) starts to decline on Tuesday, and due to the academic week Fridays and Saturdays (and Sundays if applicable) are generally less busy. Weeks can therefore be regarded as clusters which represent various activity levels in recurrent order. Besides, it is easier to organise data collection for one week than for a number of separate days, and many libraries therefore prefer to count in weekly intervals. In contrast to a random selection of the sample, the deliberate pre-selection of normal or typical weeks implies detailed knowledge about the variable in question. It is well known that, for example, daily use of academic libraries services is influenced by general factors like the academic year, events inside the library and the availability of competitive library services on the campus. It can be argued furthermore that a number of randomised factors like technical readiness of buildings and systems, local weather conditions or important cultural or other events in the vicinity will blur any set of in-library use data. Fig 1 Average gate counts per weekday (Münster UL, ) 62 Statistics in Practice Measuring & Managing 2002

3 Fig 2 Weekly gate counts in percent of deviation from yearly mean (Münster UL, ) Even if it is difficult, if not impossible, to take these fuzzy elements into consideration, the selection of normal weeks implies that data of previous years provide sufficiently reliable information on weeks representing an average level of activity, and that library staff are aware of these patterns. Fig 2 underlines the problem by displaying adjusted data of weekly gate counts at Münster University Library for the years 1998 to Hardly any week or even longer time frame can be identified as a reliable basis for purposive sampling, as many weeks show varying gate counts over the years in question, and periods of high use blend into periods of lower use. Furthermore, experienced members of staff in user services were asked to determine periods of average in-library use intensity. As seen in Fig 2, gate counts in the periods chosen by staff still vary between and percent from the mean. Staff in other libraries may even come to different results. Thus, the significantly smaller variation of values indicates that staff judgement can in fact improve the sample, but it is not a very solid foundation for statistical reporting and comparisons. Random sampling over time While non-random sampling cannot be accounted for precision, the accuracy of random samples can be measured in terms of error and confidence level. The following examples apply different methods of random sampling to reference and other use statistics. As the methods were applied to different library settings, the results and boundaries were generally not compared except where indicated. Louisiana State University Libraries A description of the purest sampling method, a simple random sample of opening hours throughout the year, can be found in Maxstadt (1988). For the fiscal year 1986/87, a sample size of 52 hours (out of 4,103 hours of service a year) was calculated setting a confidence level of 90% and error boundaries of ± 10%. With an increased sample size of 60 hours the actual overall error range was later determined as ± 11.23%. The yearly total of reference Statistics in Practice Measuring & Managing

4 questions was estimated by linear extrapolation of the sample count. To avoid any bias or service delays, additional library staff were assigned to collect the sample data. If no extra staff are available, this method may be criticised because the hourly count as practised here requires a great deal of coordination, especially in large libraries with several service points. A similar (daily) approach is described by Bauer (2000). New York University / Bobst Library Kesselman and Watstein (1987) describe the use of additional information to stratify the sample and thereby compared to a simple random sample reduce its variation: based on fully counted reference statistics from the year 1982/83, weekly reference counts were stratified in high, medium and low activity. Given a 95% confidence limit and an error of ± 400 ( 10%) a sample size of 15 weeks was calculated, which represented the number of weeks in each of the classes or strata. The yearly total was estimated by linear extrapolation of the weighted class means. It was recognised, however, that weekly reference activity may vary from year to year due to a number of reasons, academic or school holidays being the most obvious. Consequently library staff may find it difficult to qualify in advance if information from previous years is still reliable. In the Bobst Library case, the sample mean of medium weeks was higher than the one of high weeks. The problem was solved by merging both into one stratum, thereby losing some of the expected improvement. University of South Carolina / Thomas Cooper Library Starting from the Bobst Library procedure Lochstet and Lehman (2000) developed a correlation method that makes use of a highly significant, almost linear direct correlation (+.957) between weekly reference statistics values and door counts as found by staff at Thomas Cooper Library in In this case, the door count was used as a boundary distribution to extrapolate the reference sample values and estimate the yearly total. The correlated total and the total sampled from the same weeks differed by only 0.05%. The standard error with the correlation method, however, was considerably high. The authors recommend collecting and correlating data of two variables for one or even two years to provide a substantial set of comparable data before the correlation method could be regarded as a functional alternative. After an accurate correlation coefficient has been obtained, it is expected that the amount of time spent on recording reference statistics can be significantly reduced: only a small random sample of a few weeks will be needed to verify that the correlation has not changed. Münster University Library Staff at Münster University Library analysed if the correlation method used at Thomas Cooper Library could be extended to certain datasets from the library system: At first all in-library usage data were regarded as possible high correlates to the gate count because all these activities could only be initiated by persons who had previously entered the library. At second the data to be analysed should be collected automatically by the library system, i.e. available with only minimal staff input. Weekly gate counts (and reference questions) were then correlated with the selection of automated data shown in Table 1. The highest correlation values (> +.75) with gate counts and reference were found in (a) user-initiated reservations and (b) accesses to user accounts from PC workstations inside the library. In contrast, loans and reservations from workstations outside the library premises are obvious 64 Statistics in Practice Measuring & Managing 2002

5 Table 1 Correlation between weekly gate count and data from automated system (Münster UL, 1999/2000) Visits Reference (in library) (remote) Account information Renewals Short loans Normal loans Visits Reference.876** (in library).802**.751** (remote).437** ** Account information.800**.765**.796**.220** Renewals.523**.512*.568**.256**.759** Short loans.473** ** **.140* Normal loans.506** ** **.283**.483** ** The correlation is significant on the 0.01 level (2-sided). * The correlation is significant on the 0.05 level (2-sided). examples of unsuitable correlates: While loans differ in their seasonal patterns from library visits over a year, users frequenting the automated system from outside the library are unlikely to be included in the gate count on the same day yet it seems likely that remote use can also show high correlation values, e.g. online reference with virtual visits of the library website. Seemingly corresponding data may in fact be pure coincidence as the correlation coefficient only measures the nature and extent, but not the causal connection ( direction ) of a relationship between two variables. Before high correlation values can be used it is therefore important to pre-select possible correlates carefully and analyse them for logical consistency, and to monitor the correlation values over a longer period of time to ensure that the correspondence is not purely accidental. Conclusions Sampling procedures have always been widely applied in libraries because the full count of some data was impossible or too costly. The introduction of sampling in international statistical reporting reflects a general shift of focus from input to output measures many of which can only be counted in sample form. From the point of data collection management it seems useful to choose a week as the sampling unit. Normal weeks, when selected by judgement, may be difficult to anticipate even from data collected over several years, and the precision of judgement sampling cannot be calculated in terms of error and confidence level. It is likely that certain usage data show significant correlation and can provide useful information for estimating totals. Its significance, however, should be revised at regular intervals as correlation only indicates the extent, not any causal connection, of a relationship between variables. Due to the lack of comparable data, it seems unreasonable to recommend an overall best or most appropriate sampling method for international statistical reporting. Libraries are therefore asked to carefully apply sampling methods with respect to all possible sources of error, and their regional and national institutions will have to monitor and actively supervise the quality of data delivered to them. Statistics in Practice Measuring & Managing

6 References Bauer, K (2000) Gathering ARL reference data, URL: ( , last retrieved on ) Bookstein, A (1983) Sampling from card files, Library Quarterly 53, 3, Hasemann, C (1977) Stichprobenerhebungen fur die Bibliotheksstatistik [Sample surveys for library statistics], Bibliothek 1, 1, ISO 2789:1991 Information and documentation International library statistics. ISO 2789:2003 Information and documentation International library statistics. Lancaster, F W (1993) If you want to evaluate your library 2 nd ed., Champaign, IL. Lipetz, B A (1972) Catalog use in a large research library, Library Quarterly 42,1, Lochstet, G, Lehman, D H (1999) A correlation method for collecting reference statistics, College & Research Libraries 60,1, Kesselman, M, Watstein, S B (1987) The measurement of reference and information services, Journal of Academic Librarianship 13, 1, Maxstadt, J M (1988) A new approach to reference statistics, College & Research Libraries 49, February, NISO Z Draft Standard for Trial Use: Data Dictionary (Version 2002a), URL: ( , last retrieved on ). 66 Statistics in Practice Measuring & Managing 2002