NASIG 2010: Integrating Usage Statistics into Collection Development Decisions

Presenters: Dani Roach, University of St. Thomas and Linda Hulbert, University of St. Thomas

As with most libraries, they are faced with needing to downsize their purchases in order to fit within reduced budgets, so good tools must be employed to determine which stuff to remove or acquire.

The statistics for impact factor means little to librarians, since the “best” journals may not be appropriate for the programs the library supports. Quantitative data like cost per use, historical trends, and ILL data are more useful for libraries. Combine these with reviews, availability, features, user feedback, and the dust layer on the materials, and then you have some useful information for making decisions.

Usage statistics are just one component that we can use to analyze the value of resources. There are other variables than cost and other methods than cost per use, but these are what we most often apply.

Other variables can include funds/subjects, format, and identifiers like ISSN. Cost needs to be defined locally, as libraries manage them differently for annual subscriptions, multiple payments/funds, one-time archive fees, hosting fees, and single title databases or ebooks. Use is also tricky. A PDF download in a JR1 report is different from a session count in a DB1 report is different from a reshelve count for a bound journal. Local consistency with documentation is best practice for sorting this out.

Library-wide SharePoint service allows them to drop documents with subscription and analysis information into one location for liaisons to use. [We have a shared network folder that I do some of this with — I wonder if SharePoint would be better at managing all of the files?]

For print statistics, they track separately bound volume use versus new issue use, scanning barcodes into their ILS to keep a count. [I’m impressed that they have enough print journal use to do that rather than hash marks on a sheet of paper. We had 350 reshelved in last year, including ILL use, if I remember correctly.]

Once they have the data, they use what they call a “fairness factor” formula to normalize the various subject areas to determine if materials budgets are fairly allocated across all disciplines and programs. Applying this sort of thing now would likely shock budgets, so they decided to apply new money using the fairness factor, and gradually underfunded areas are being brought into balance without penalizing overfunded areas.

They have stopped trying to achieve a balance between books and periodicals. They’ve left that up to the liaisons to determine what is best for their disciplines and programs.

They don’t hide their cancellation list, and if any of the user community wants to keep something, they’ve been willing to retain it. However, they get few requests to retain content, and they think it is in part because the user community can see the cost, use, and other factors that indicate the value of the resource for the local community.

They have determined that it costs them around $52 a title to manage a print subscription, and over $200 a title to manage an online subscription, mainly because of the level of expertise involved. So, there really are no “free” subscriptions, and if you want to get into the cost of binding/reshelving, you need to factor in the managerial costs of electronic titles, as well.

Future trends and issues: more granularity, more integration of print and online usage, interoperability and migration options for data and systems, continued standards development, and continued development of tools and systems.

Anything worth doing is worth overdoing. You can gather Ulrich’s reports, Eigen factors, relative price indexes, and so much more, but at some point, you have to decide if the return is worth the investment of time and resources.

ER&L 2010: Comparison Complexities – the challenges of automating cost-per-use data management

Speakers: Jesse Koennecke & Bill Kara

We have the use reports, but it’s harder to pull in the acquisitions information because of the systems it lives in and the different subscription/purchase models. Cornell had a cut in staffing and an immediate need to assess their resources, so they began to triage statistics cost/use requests. They are not doing systematic or comprehensive reviews of all usage and cost per use.

In the past, they have tried doing manual preparation of reports (merging files, adding data), but that’s time-consuming. They’ve had to set up processes to consistently record data from year to year. Some vendor solutions have been partially successful, and they are looking to emerging options as well. Non-publisher data such as link resolver use data and proxy logs might be sufficient for some resources, or for adding a layer to the COUNTER information to possibly explain some use. All of this has required certain skill sets (databases, spreadsheets, etc.)

Currently, they are working on managing expectations. They need to define the product that their users (selectors, administrators) can expect on a regular basis, what they can handle on request, and what might need a cost/benefit decision. In order to get accurate time estimates for the work, they looked at 17 of their larger publisher-based accounts (not aggregated collections) to get an idea of patterns and unique issues. As an unfortunate side effect, every time they look at something, they get an idea of even more projects they will need to do.

The matrix they use includes: paid titles v. total titles, differences among publishers/accounts, license period, cancellations/swaps allowed, frontfile/backfile, payment data location (package, title, membership), and use data location and standard. Some of the challenges with usage data include non-COUNTER compliance or no data at all, multiple platforms for the same title, combined subscriptions and/or title changes, titles transferred between publishers, and subscribed content v. purchased content. Cost data depends on the nature of the account and the nature of the package.

For packages, you can divide the single line item by the total use, but that doesn’t help the selectors assess the individual subset of titles relevant to their areas/budgets. This gets more complicated when you have packages and individual titles from a single publisher.

Future possibilities: better automated matching of cost and use data, with some useful data elements such as multiple cost or price points, and formulas for various subscription models. They would also like to consolidate accounts within a single publisher to reduce confusion. Also, they need more documentation so that it’s not just in the minds of long-term staff. 

ER&L 2010: Beyond Log-ons and Downloads – meaningful measures of e-resource use

Speaker: Rachel A. Flemming-May

What is “use”? Is it an event? Something that can be measured (with numbers)? Why does it matter?

We spend a lot of money on these resources, and use is frequently treated as an objective for evaluating the value of the resource. But, we don’t really understand what use is.

A primitive concept is something that can’t be boiled down to anything smaller – we just know what it is. Use is frequently treated like a primitive concept – we know it when we see it. To measure use we focus on inputs and outputs, but what do those really say about the nature/value of the library?

This gets more complicated with electronic resources that can be accessed remotely. Patrons often don’t understand that they are using library resources when they use them. “I don’t use the library anymore, I get most of what I need from JSTOR.” D’oh.

Funds are based on assessments and outcomes – how do we show that? The money we spend on electronic resources is not going to get any smaller. ROI is focused more on funded research, but not electronic resources as a whole.

Use is not a primitive concept. When we talk about use, it can be an abstract concept that covers all use of library resources (physical and virtual). Our research often doesn’t specify what we are measuring as use.

Use as a process is the total experience of using the library, from asking reference questions to finding a quiet place to work to accessing resources from home. It is the application of library resources/materials to complete a complex/multi-stage process. We can do observational studies of the physical space, but it’s hard to do them for virtual resources.

Most of our research tends to focus on use as a transaction – things that can be recorded and quantified, but are removed from the user. When we look only at the transaction data, we don’t know anything about why the user viewed/downloaded/searched the resource. Because they are easy to quantify, we over-rely on vendor-supplied usage statistics. We think that COUNTER assures some consistency in measures, but there are still many grey areas (i.e. database time-outs equal more sessions).

We need to shift from focusing on isolated instances of downloads and ref desk questions, but focus on the aggregate of the process from the user perspective. Stats are only one component of this. This is where public services and technical services need to work together to gain a better understanding of the whole. This will require administrative support.

John Law’s study of undergraduate use of resources is a good example of how we need to approach this. Flemming-May thinks that the findings from that study have generated more progress than previous studies that were focused on more specific aspects of use.

How do we do all of this without invading on the privacy of the user? Make sure that your studies are thought-out and pass approval from your institution’s review board.

Transactional data needs to be combined with other information to make it valuable. We can see that a resource is being used or not used, but we need to look deeper to see why and what that means.

As a profession, are we prepared to do the kind of analysis we need to do? Some places are using anthropologists for this. A few LIS programs are requiring a research methods course, but it’s only one class and many don’t get it. This is a great continuing education opportunity for LIS programs.

ER&L 2010: We’ve Got Data – Now What Do We Do With It? Applying Standards to Assess Information Resources

Speakers: Mary Feeney, Ping Situ, and Jim Martin

They had a budget cut (surprise surprise), so they had to asses what to cut using the data they had. Complicating this was a change in organizational structure. In addition, they adopted the BYU project management model. Also, they had to sort out a common approach to assessment across all of the disciplines/resources.

They used their ILLs to gather stats about print resource use. They hired Scholarly Stats to gather their online resource stats, and for publishers/vendors not in Scholarly Stats, they gathered data directly from the vendors/publishers. Their process involved creating spreadsheets of resources by type, and then divided up the work of filling in the info. Potential cancellations were then provided to interested parties for feedback.

Quality standards:

  • 60% of monographs need to show at least one use in the last four years – this was used to apply cuts to the firm orders book budget, which impacts the flexibility for making one-time purchases with remaining funds and the book money was shifted to serial/subscription lines
  • 95% of individual journal titles need to show use in the last three years (both in-house and full-text downloads) – LJUR data was used to add to the data collected about print titles
  • dual format subscriptions required a hybrid approach, and they compared the costs with the online-only model – one might think that switching to online only would be a no-brainer, but licensing issues complicate the matter
  • cost per use of ejournal packages will not exceed twice the cost of ILL articles

One problem with their approach was with the existing procedures that resulted in not capturing data about all print journals. They also need to include local document delivery requests in future analysis. They need to better integrate the assessment of the use of materials in aggregator databases, particularly since users are inherently lazy and will go the easiest route to the content.

Aggregator databases are difficult to compare, and often the ISSN lists are incomplete. And, it’s difficult to compare based on title by title holdings coverage. It’s useful for long-term use comparison, but not this immediate project. Other problems with aggregator databases include duplication, embargos, and completeness of coverage of a title. They used SerSol’s overlap analysis tool to get an idea of duplication. It’s a time-consuming project, so they don’t plan to continue with it for all of their resources.

What if you don’t have any data or the data you have doesn’t have a quality standard? They relied on subject specialists and other members of the campus to assess the value of those resources.

ER&L 2010: Usage Statistics for E-resources – is all that data meaningful?

Speaker: Sally R. Krash, vendor

Three options: do it yourself, gather and format to upload to a vendor’s collection database, or have the vendor gather the data and send a report (Harrassowitz e-Stats). Surprisingly, the second solution was actually more time-consuming than the first because the library’s data didn’t always match the vendor’s data. The third is the easiest because it’s coming from their subscription agent.

Evaluation: review cost data; set cut-off point ($50, $75, $100, ILL/DocDel costs, whatever); generate list of all resources that fall beyond that point; use that list to determine cancellations. For citation databases, they want to see upward trends in use, not necessarily cyclical spikes that average out year-to-year.

Future: Need more turnaway reports from publishers, specifically journal publishers. COUNTER JR5 will give more detail about article requests by year of publication. COUNTER JR1 & BR1 combined report – don’t care about format, just want download data. Need to have download information for full-text subscriptions, not just searches/sessions.

Speaker: Benjamin Heet, librarian

He is speaking about University of Notre Dame’s statistics philosophy. They collect JR1 full text downloads – they’re not into database statistics, mostly because fed search messes them up. Impact factor and Eigen factors are hard to evaluate. He asks, “can you make questionable numbers meaningful by adding even more questionable numbers?”

At first, he was downloading the spreadsheets monthly and making them available on the library website. He started looking for a better way, whether that was to pay someone else to build a tool or do it himself. He went with the DIY route because he wanted to make the numbers more meaningful.

Avoid junk in junk out: HTML vs. PDF downloads depends on the platform setup. Pay attention to outliers to watch for spikes that might indicate unusual use by an individual. The reports often have bad data or duplicate data on the same report.

CORAL Usage Statistics – local program gives them a central location to store user names & passwords. He downloads reports quarterly now, and the public interface allows other librarians to view the stats in readable reports.

Speaker: Justin Clarke, vendor

Harvesting reports takes a lot of time and requires some administrative costs. SUSHI is a vehicle for automating the transfer of statistics from one source to another. However, you still need to look at the data. Your subscription agent has a lot more data about the resources than just use, and can combine the two together to create a broader picture of the resource use.

Harrassowitz starts with acquisitions data and matches the use statistics to that. They also capture things like publisher changes and title changes. Cost per use is not as easy as simple division – packages confuse the matter.

High use could be the result of class assignments or hackers/hoarders. Low use might be for political purchases or new department support. You need a reference point of cost. Pricing from publishers seems to have no rhyme or reason, and your price is not necessarily the list price. Multi-year analysis and subject-based analysis look at local trends.

Rather than usage statistics, we need useful statistics.