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X. Triple paper

At the IATUL follow-up workshop on library statistics in Africa, on April 19, 2013, Tord Høivik prepared three sessions - with background papers. The sessions covered three different aspects of the subject:

  • the first, on statistical literacy, looks inward. It has to do with the skills and practices of librarians
  • the second, on statistical advocacy, looks outward. It has to do with the defence, marketing and promotion of libraries
  • the third, on statistical trends, looks forward. It has to do with the role of libraries in a knowledge based economy. 

All materials are also published, with a CC licence, on the web site


This document includes all three papers. 



Change is the key to knowledge

My three axioms are:
  • Statistics is a tool for change. 
  • To work with statistics is to argue with numbers.
  • Change must come from below
This implies
  • If you don't want change, stay away from statistics. 
  • If you don't want discussions, stay away from statistics. 
  • If your staff wants stability, you have a problem.
Evidence-based practice

Many people speak about EBLIP, or evidence-based librarianship and information practice. But EBLIP and library statistics are only different names for the same animal.

The "evidence movement" developed in medicine about thirty years ago. Since then, the principles have expanded into many practical professions: nursing, social work, teaching, librarianship.

Evidence-based practice means that decisions should be based on systematic evidence
  • rather than power
  • rather than tradition
  • rather than personal interest
The best evidence is usually quantitative - and based on statistical methods.

Change from below

For many years we have tried to change the statistical practices of librarians through committees, concepts and proposals from the top. This approach does not work.

Librarians are not willing to change their routines just because committees, without power or money to impose their views, say so. We have to start at the bottom. That means to improve existing data and current practices, year by year, in cooperation with the libraries that do the actual work of collecting, interpreting and applying statistical data.

We have to change from a top-down to a bottom-up approach.

Top-down work is easy to organize. You gather ten people around a table and ask them to make proposals. After one or two years the committee is finished. Implementation is left to the libraries. If they do not want to do what the committee proposes, the process stops here. The committee draws up a plan of work, but leaves the work itself to the library community.

Bottom-up work is hard to organize. Organizations don’t enjoy change. They resist change. The change agent has to find libraries and library organizations that are willing to cooperate. We have to form networks and production teams rather than committees. We have to test, to train and to argue our case. We have to struggle with the intellectual and material difficulties of statistics production. This is not for the faint of heart. It takes years of commitment and thousands of hours of work.

Change can be encouraged at the top, but must be realized at the bottom. That’s the way the world works.


The world has about two hundred countries. Less than twenty collect good library statistics at the national level.

This does not mean a total lack of statistics. Nearly all countries have universities. These universities have libraries. Today, most of the libraries operate digital systems. The systems can be used to generate a variety of statistical reports.

But potential access to statistical does not, by itself, lead to data-driven decision making. Decisions combine political and rational elements. Evidence-based practice requires an evidence-based culture. Managers must respect, promote and integrate statistical data into their daily work. Staff must accept and share statistical arguments.

Statistical literacy is the ability of organizations and individuals to understand and to argue with statistics - without losing sight of other important considerations.

Statistical literacy is not widespread. More important: practical statistics is seldom recognized as a professional skill. The first step towards statistical literacy is to accept that amateurs differ from experts.

Typical amateur practices are:
  • If you need a questionnaire - make your own. 
  • If you want a sample survey - put a pop-up form on the library web site.
  • If you want to present your data - use lots of pie and bar charts in glorious colours. 
People who are statistically literate will
  • Re-use, or adapt, tested and established questionnaires - to safequard quality and to achieve comparability
  • Avoid web forms because of their low response rates, which lead to biases that are hard or impossible to correct.
  • Simplify graphs and tables so that the main message is clear. 

  • Statistics is like drawing. Any librarian can draw a face. Very few can paint a portrait. 
  • Statistics is like haircuts. Any person with a pair of scissors can cut your hair, but most people prefer a real hairdresser. 
  • Statistics is like Russian. You can learn the Cyrillic alphabet in a day and two hundred words in a week, but proficiency takes years.


Elisha Chiware has given a good overview of the literacy problem in African library statistics
  1. There is no standard on the type of library statistics to be collected
  2. There is no shared position on how data must be collected, analyzed, presented and applied
  3. There is no national or regional African databases of comparative library statistics available
  4. There is a wide gap in the type and frequency of statistics between technologically advanced libraries and those less fortunate
Before I go further, I'd like to give a quick personal response to Elisha.

I am not saying that this is the answer. But statistics is about change. The intention is to look at possible actions- to help the discussion along.

Possible actions
  1. Test and develop regional standards for Africa 
    • by adapting the two main ISO standards (variables; indicators) to local conditions
    • by working with IFLA (Statistics and Evaluation section; IFLA's committee on library standards)
    • by working with SCECSAL and eLearning Africa
  2. Create a network of like-minded librarians to explore, in their own libraries:
    • data collection
    • data analysis
    • data presentation
    • data applications 
  3. Work in parallell A:
    • use library networks to initiate and develop databases with comparative library statistics
    • work with national statistical agencies to include such databases in the system of national statistics
  4. Work in parallell B: 
    • improve statistical reporting in the more advanced libraries
    • introduce some basic statistical routines in the less advanced libraries

Advanced skills

Last year the University of South Africa advertised a new position for its library.

The job title was information reporter, but the job description shows that the university wanted a "library statistician". Academic libraries in the United States would probably use the title assessment librarian.

Statistical reporting

The main purpose was statistical reporting. The candidate would
  • support operational activities and decision making
  • provide management information on library performance.
  • receiving requests for reports on information resources from Library business units
  • providing reports on information resources 
  • generating own reports
He or she would also do development work
  • setting up new reporting request structures
Required skills

The candidate should be able to
  • to interpret statistics from library systems
  • to substantiate findings
  • to express technical concepts in language that is coherent, clear and unambiguous
He or she should, in other words: be able
  • to interpret data
  • to argue with statistics
  • to present data in a convincing way
What the University wanted was advanced statistical literacy.


When I prepared for this lecture I was happy to discover that Wikipedia had quite a good article on library assessment.

Since I believe in the re-use of good stuff, I have copied the introduction - but added my own sub-headings.
  • Learn about the user. Library assessment is a process undertaken by libraries to learn about the needs of users (and non-users) and to evaluate how well they support these needs, in order to improve library facilities, services and resources.
  • Culture of assessment. In many libraries successful library assessment is dependent on the existence of a 'culture of assessment' in the library whose goal is to involve the entire library staff in the assessment process and to improve customer service.
  • Fifteen years. Although most academic libraries have collected data on the size and use of their collections for decades, it is only since the late 1990s that many have embarked on a systematic process of assessment (see sample workplans) by surveying their users as well as their collections.
  • Library Assessment Manager. Today, many academic libraries have created the position of Library Assessment Manager in order to coordinate and oversee their assessment activities.
  • Accountability. In addition, many libraries publish on their web sites the improvements that were implemented following their surveys as a way of demonstrating accountability to survey participants.
  • Tool for change. Several libraries have undertaken renovation or expansion projects as a result of their assessment activities as well as enhance resource discovery tools, improve web site usability and stop redundant services.

Indicator sets

Different library indicators measure different aspects of library services.

Students are not staff. Visits are different from loans. The number of seats, the number of volumes and the actual size of the building show size from different perspectives.

Single indicator values are hard to judge.

To get a balanced picture of a library we must combine several indicators into groups or sets of indicators.
It is easy for a single person to construct a nice set of indicators. It is harder to achieve consensus within a committee. It is nearly impossible to get hundreds and thousands of librarians to actually use the well-intentioned proposals.

The reasons are pretty obvious.
  • collecting data is hard work
  • processing data is hard work
  • statistical skills are scarce
Statistical indicators are similar to exams. Only the best students enjoy them. The rest would rather do something else.

Most librarians dislike numbers. Their culture is partly literary and partly control oriented, but always qualitative. They tend to be great talkers, but not so great calculators. They avoid numerical arguments, since these reveal their lack of skills and interest in quantitative reasoning.

It is also the case that dfferent libraries have different interests. Indicators are similar to sports. We prefer to compete in our favorite event. We want indicator sets that focus on our best qualities - and avoid the dark secrets that lie behind them. Doing well with a balanced set of indicators requires a broad focus. It becomes a decathlon struggle rather than a long jump or throwing the javelin.

But let us take a look at two indicator sets: the ISO standard 11620 and the German library index BIX.

ISO in Italy

The newest version of ISO 11620, from 2008, comprises forty-five indicators. I have listed them here.

The total is rather overwhelming. The ISO committee does not insist that libraries should use all the indicators, or that public and academic libraries should use the same subset. Librarians are asked to use their own judgement.

But this standard has a top-down character. It is not supported by a network of practitioners who are actually using the indicators - and reporting on their use.

Let me illustrate by a case from Italy. This is one of the few reports I've seen that describes actual use.

In 2002, Paolo Bellini published an article on performance measurement as a marketing support for libraries. He wrote:

At the Library of the University of Trento, we have been applying performance indicators since 1998 as a planning and evaluation tool and to indicate the working of the library. The ISO standard 11620 was chosen from the outset for various reasons: it was specifically designed for libraries and is the most recently developed method. ...

The main phases of the project have been:
  • a preliminary study, 
  • a pilot scheme, 
  • the choice of the indicators to be applied, 
  • internal and external communication, 
  • the gathering of data, 
  • the drafting and distribution of the final report. 
The initial results of the project were distributed amongst the librarian community through articles in professional journals and speeches at national conferences. ...

The major difficulties encountered in carrying out the project were:
  • the limited quantity of data directly available from the automated management system, 
  • the inconsistency of these data, 
  • the lack of data related to the use of electronic resources, 
  • the difficulty in carrying out sample surveys systematically and permanently, 
  • a certain isolation in the local and national context, 
  • sometimes uncertainty in interpreting the texts of the standards used within ISO 11620. 
After a few years’ into the trial, at this stage one can outline that 
  • the implementation of the standard ISO 11620 is onerous and extremely time consuming. 
  • It also requires a strong will to overcome the opposition and resistance from within and from outside the library. 
  • The librarians themselves are rarely conscious of its necessity and its benefits. 
On the other hand, the performance measurement has proved to be a very useful and versatile tool from the viewpoint of the university management.


This seems to be a realistic assessment of the situation. It confirms a study I did last year, on the use of indicators in Norwegian libraries.

That paper explored the great discrepancy between recommended library indicators, on the one hand, and the actual use of statistics by libraries and librarians, on the other.

Norway has three official indicator sets: a thirteen indicator set for public libraries, another thirty indicator set for public libraries and finally a set of twenty-four indicators for academic libraries. But the recommended sets are hardly used at all. The proposals are not taken seriously by the intended users.

BIX in Germany

The German library index BIX is, in my view, the most successful library indicator system in the world.

There are several reasons for this
  • The system is voluntary. It was created by the libraries that wanted an indicator system to measure and evaluate their performance.
  • The system is grounded. The methodology was developed over several years, by the interested libraries, with good financial support
  • The system is differentiated. BIX uses very different indicator sets for public and for academic libraries. 
  • The system is promoted. BIX has an active web site, in German and in English. It publishes an attractive annual magazine, with interesting articles about libraries in addition to the statistical information.
  • The system is open. The methodology and the indicators are continually monitored and revised, supported by an advisory board consisting of members of public and academic libraries and experts.
I am not saying the system is perfect. Every single indicator could be discussed. Several could be improved. But BIX is still the best example of a library indicator system that works within a library community.


Indicators are technical tools.
  • To use them with understanding, we have to study the way they are produced.
  • Statistical indicators are created from statistical variables.
  • Statistical variables are produced by statistical procedures.
Let me use library visits as an example.


ISO 11620 includes two indicators based on the number of visits:
  • Library Visits per Capita. To assess the library’s success in attracting users of all its services (B.2.2.1)
  • Cost per Library Visit. To assess the cost of the library’s service related to the number of library visits (B.3.1.4)
The first one [LVPC] is widely used. The second is seldom used - and not very informative, I would add.

BIX proposes
  • Physical library visits per capita of the primary user group - in academic libraries
  • Library Visits per capita - in public libraries
Meaning and measurement

In either case we have to specify what we mean by a library visit.

Personally I would define a genuine library visitor as a person who enters the library premises in order to use the library's professional facilities as a user. This concept excludes:
  • the comings and goings of the library staff
  • visits by janitors, sales representatives, postmen and others who enter for other (work-related) purposes
  • visits by persons who enter for non-library purposes: coffee shopping, free toilets or convenient short-cuts through the building
When we set up a system to register visitors it may be impractical to distinguish customers from people who just happen to enter for other reasons. But that is not a reason to be vague about the concepts involved.

Do not confuse meaning with measurement.


In ISO 2782 a visit is simply defined (2.2.40) as a person (individual) entering the library premises.

But the procedure (6.2.10) adds: where necessary, the count should be adjusted to deduct entrances and exits of library staff, and of any persons visiting other institutions or departments situated within the library building.

This seems unclear. I would rather define a visit as

a person who enters the library premises in order to use the library's professional facilities as a user.

Measurement errors

If the actual measurement is carried out by turnstile count or electronic counter, there are various sources of errors. For instance:
  • persons who enter for other purposes
  • persons who walk closely together (so that two count as one)
  • children who are carried (two count as one)
  • persons who walk in and out several times, such as playful children
  • non-persons that break the electronic beam, such as dogs and suitcases
There may be additional errors due to the actual location of the turnstile or the counter. With manual counts, the errors mentioned can mostly be avoided.


If the library suspects substantial errors of measurement (more than +/- 5%, say) , the counter should be calibrated (by running a manual count in parallell for a suitable period of time).

Visits per user

The first indicator can be improved by dividing the population (target group, size N) into users and non-users.
  • The number of library visits per user (LVPU) shows the (average) intensity of use among the actual users
  • The percentage of users (P) and of non-users (Q) shows the degree of penetration (P + Q = 100%)
LVPC and LVPU are related by a simple formula:
  • LVPC = LVPU * P
In words:
  • The number of library visits per capita = 
    The number of library visits per user * The percentage of users in the population

The total number of visits = A * N = B * (P * N) + 0 * (Q * N) = (B* P) * N
Divide by N on both sides

This distinction is often fruitful in library planning. It helps us differentiate between users and non-users. Library managers would normally want
  • to turn non-users into users
  • to turn users into better users
These are different tasks and require different strategies.

Target population

To be statistically literate you have to be comfortable with fractions and percentages.

Indicators are ratios. When we calculate visits per capita, we divide the number of visits (the numerator) by the size of the target population (the denominator).

To understand the concept of visits per capita we need to understand the denominator as well as the numerator. What do we mean, or what should we mean, by target population?

I will not go into this discussion, but simply say: this is not an easy question. Libraries often serve many different groups, in different ways. Public libraries are used by non-residents as well as residents. Academic libraries are used by staff, on-site students, part-time students, distance students, and sometimes by the local community.

It is also clear that defining the target population tends to be more difficult in the South than North.


We have looked at some of the components of statistical literacy:
  • understanding the link between statistics and action - with reference to library assessment
  • understanding the role and use of indicators - with ISO and BIX as examples
  • understanding statistical concepts and variables - with visits per capita as an example
My final example is a bit more technical. I refer to the role of statistical sampling in evidence-based practice.

Sampling is a dangerous area. Sampling is not difficult - if you follow the rules. But people are constantly tempted to break the rules. Most researchers are lazy. It is much easier to work with the data at hand than to collect data systematically.

Convenience sampling

Convenience sampling is the best example. I'll simply cite Wikipedia:

Accidental sampling (sometimes known as grab, convenience sampling or opportunity sampling) is a type of non-probability sampling which involves the sample being drawn from that part of the population which is close to hand. That is, a sample population selected because it is readily available and convenient. The researcher using such a sample cannot scientifically make generalizations about the total population from this sample because it would not be representative enough.

Simple sampling

I want to measure what is happening at my library. I want to know, say, about:
  • the circulation of stock: which items are in heavy and which are in low demand
  • the number of people who visit the library
  • why they use it: for study, recreation, digital access, meeting friends ...
Librarians cannot monitor and register everything that happens on a continuous basis. The cost would be too high.

The methods below will work whether the library works on a digital or a manual basis: no automated catalogue and no electronic counter at the entrance. The idea of sampling is the same.

Masters of the universe

Data collection is work – hard, disciplined work – and should be kept to a minimum. Statistical sampling is a technique for collecting data that minimizes the work, while providing the answers we need. When we sample, we take a selection from a larger total – using a particular (and strictly enforced) technique – and treat the sample as if it were the total.

The total is is often called the universe or the population.

Perfect accuracy is seldom needed. It does not really matter whether the library had 13.415 or 13.615 visitors in 2006. But the difference between thirteen and fifteen thousand visitors matter.

As a rule of thumb, I would say:
  • do not bother about a difference of 1-2 percent
  • a difference of 3-4 percent is small, but may be meaningful
  • a difference of 5-15 percent is substantial and interesting
  • anything more is very interesting
We can usually get information that is good enough for practical decision-making, from a sample of a few hundred items. The greater the sample, the greater the accuracy.

A very basic, and also very surprising, statistical rule is: The size of the original population does not matter.Accuracy only depends of the size of the sample.

First example: selecting books

Let me apply the idea of sampling to the book collection.

My library has, say, ten thousand books. I want to know how up-to-date my collection is, by looking at the year of publication.

Checking ten thousand cards and writing down ten thousand numbers does not appeal to me. I appeal to statistics and take a sample of – say – two hundred cards instead. This could, for obvious reasons, be called a two percent sample.

The big idea in statistical sampling lies in the way you go about selecting the sample from the total.

You should not pull two hundred consecutive cards from the nearest drawer. Nor should you rummage around, taking one here and one there as the mood takes you. The sample should
  • come from the whole population
  • not depend on deliberate human choice
There are many ways of achieving this. The simplest is probably to take a look at every fiftieth card and write down the year of publication.

The distribution of these two hundred numbers will provide a good approximation to the true distribution baed on all ten thousand publication years.

Second example: selecting users

Concepts are important. If we want to study users, we must first decide the limits of the population.

For instance, do I mean:
  • The people that visit the library on a regular basis?
  • The people that have visited the library at least once during the last year?
  • The people that have visited the library at least once during the last five years?
  • The people that are registered as users?
  • The people that are registered as users and have borrowed materials during the last year?
  • The people that are registered as users and have borrowed materials during the last five years?
Public libraries

Let me define user as a person that is registered as a user. My population will then consist of a set of registration cards.

I want to understand the social impact of the library by looking at the demographic distribution of users in the local community.
  • How many registered users are male? female?
  • How many are children? teenagers? adults? seniors?
  • Where do these people live? 
Let us say the library has six thousand registered users. I want to choose two hundred at random.

The selection procedure is simple. Since 6.000/200 = 30, I may simply start with a number between 1 and 30 and look at every thirtieth card.
  • The distributions by age and sex among the registered users in the sample will be a good estimate of the actual distribution in the population.
  • To find the geographical distribution, I write down the addresses and plot them on a local map. 
  • To find the penetration (percentage of users in each area), the user data may be compared with census data.
Academic libraries

Let me define user as a person that has borrowed materials during the last year? My population will then consist of a subset of the registration cards (or database posts).

I want to understand the relative use of the library by different groups of students.
  • How many users are male? female?
  • How many are full time students? part-time?
  • Which subjects do they study? at which faculties?
Let us say the library has six thousand registered users. I want to choose two hundred at random.

The selection procedure is simple. Since 6.000/200 = 30, I may simply start with a number between 1 and 30 and look at every thirtieth card/post.

If the card/post belongs to a staff member, or to a student who did not borrow anything during the last year, take the next (or the third, fourth, ...)
  • The distributions by age and sex among the users in the sample will be a good estimate of the actual distribution in the population.
  • The distribution by categories (full-time/part-time) and by subjects/faculties in the sample will be a good estimate of the actual distribution in the population.
If the registration cards (or database entries) lack information about categories, this information must be gathered from other management systems.

Third example: selecting days

My library is open – say – six days a week. We open at 9 am, take a break from noon til 2 pm, and open again from 2 till 6 pm. On Saturday, there is no afternoon session. The library is also closed for a total of four weeks during holidays.

This means that the library is open 6 * (52 – 4) = 288 days a year.
  • It is open for three hours on 48 Saturdays – which gives a total of 144 “Saturday hours”.
  • It is open for seven hours on 240 weekdays – giving a total of 1680 “weekday hours”
The total number of hours is 1680 + 144 = 1724 hours per year.

I want to know the number of visitors we have in a year. I know, from expeiernce, that library use tends to vary systematically during the day, during the week and during the year.

If I want to know the true number of visitors,
  • I should not take my “best hour” – and multiply by 1724.
  • I should not take my best day – and multiply by 288
  • I should not take my best week- and multiply by 48
I have to choose my sample from the “whole population” and in a proper “mechanical” way.

There are, as before, many ways of achieving this. The easiest is probably to select a small number of “counting days” throughout the year. On these days all visitors are counted.

You may, for instance start with the first Monday in January – and continue with the first Tuesday in February, the first Wednesday in March and so on. This approach will give you 12 days, or two full weeks, covering the whole year. Since the library keeps open 48 weeks a year, you find the total number of visitors by multiplying the observed number with 24.


The author, Tord Høivik, is a former library teacher from Norway. He has a professional background in statistics and sociology and is active on the web.


Let us move from literacy to advocacy.

  • Statistical literacy is the ability of organizations and individuals to understand and to argue with statistics - without losing sight of other important considerations. 
  • Library advocacy is the ability to mobilize people, politicians, parties and governments at all levels to support libraries – based on their actual contributions to society.

Ray Lyons

My friend Ray Lyons is a specialist in library statistics. A real expert. A genuine, honest-to-goodness professional. In his blog, and other publications, he concentrates on the United States and American library statistics. He is a strong library advocate - and a severe critic of weak library statistics. We cannot defend our libraries with plastic swords.

Some good advice:

Only Trust Numbers

  • If you want to be a good quantitative thinker, you must learn to make decisions on the basis of [evidence, which typically takes the form of] numerical information, even when that information conflicts with your instincts and perceptions…
  • Try to raise your level of trust in careful quantitative analysis, and reduce your confidence in hunches, theories, and casual observations

In Kahneman's famous book, Thinking, fast and slow, hunches belong to system 1 (fast), while quantitative analysis belongs to system 2 (slow).

Never Trust Numbers

Before we reconcile our apparently inconsistent advice, first let us explain why numbers are not worthy of your trust:

  • It’s because numbers can be wrong, are frequently misleading, and all too often have an agenda…
  • Even when accurate, numbers can easily mislead.
  • Quantitative data are seductive; they invite us to engage in the risky behavior of reading more into data than is warranted.

Numbers are Answers

A number only gets to be useful when considered as the answer to a question. To be a good consumer of numbers, the reader must constantly ask himself:

  • To what question is the number (supposed to be) the answer?
  • Is it the correct answer to that question?
  • Is that the question to which I need an answer?
Inductive or descriptive

A final point.

In library schools, statistics have usually been taught as a tool for research. This means an emphasis on probabilistic models and hypothesis testing (inductive statistics). I believe in a different approach. Most librarians need management data rather than research. This requires a good understanding of the descriptive statistics that are produced on a regular and repetitive basis by ILS and other administrative systems.


Once again I'd like to refer to Elisha Chiware's notes on library statics in Africa.

I fully agree with his description of the situation with respect to advocacy:
  • There is no clear use of library statistics in Africa for advocacy – because of the limited importance that officials in governments and institutions place on libraries
  • Library support is based on other considerations rather [than] feedback from statistical data on libraries’ use and impact
  • Presenting statistics in a meaningful way is a challenge for many academic and public libraries in Africa
  • Library statistics in Africa should be used for development aspects e.g. to support literacy programme, agriculture, education and health.
I will also give a quick response
  • The value and importance of libraries must be supported by a wide range of arguments. Statistics is only part of the answer.
  • Officials will only listen to statistical arguments if they - as well as we - are statistically literate. Librarians should therefore support the trend towards evidence-based reasoning in general.
  • Presenting statistics in a meaningful way is a challenge in all sectors and in all countries. But the demand for infographics is growing rapidly. Librarians who master the art of presentation are gaining a marketable skill.
  • In the South, libraries (need to) play a much greater role in social and economic development than in the North. This should influence the way libraries in Africa develop and use statistics.


IFLA has recently developed an integrated series of training modules for competence building in library associations. The IFLA program is called Building Strong Library Associations. In 2009, IFLA asked the Statistics and Evaluation Section to develop a learning module on the collection and use of statistics within that framework.
  • We called the development project Global statistics for advocacy.
  • The course itself is named Statistics for Advocacy.
The first SFA course in Africa was organized by Makerere University Library in Uganda, in July 2012.

BSLA activities in Africa include
  • A country project in Cameroon, to develop the Cameroon Association of Librarians, Archivists, Documentalists and Museum Curators, with Jacinta Were (Kenya) as core trainer.
  • A country project in Botswana, to develop the Botswana Library Association, with Winnie Vitzansky (Denmark) as core trainer.
  • A train the trainer workshop for trainers from Arab-Speaking countries, in cooperation with the IFLA Centre for Arabic Speaking Libraries
  • A case study of the Uganda Library Association
Local conditions

Such a course can not be fully standardized, however. The levels of library development, and of official statistics, differ too much. Each course will take place in a particular setting and must be aimed at participants from a particular working environment. Africa is different from Europe. Northern Europe is different from Southern Europe. 

Adapting to local conditions means, concretely, to adapt the course materials to:

A. Relevant conditions in the national or regional library environments, such as:
  • the current level of statistical practice: how are statistics collected and used by practitioners
  • the working language(s) in the library field
  • the digital infrastructure in the library field
B. The actual course environment
  • the language skills of the participants
  • the numerical skills of the participants
  • the digital skills of the participants
  • the digital infrastructure at the workshop venue

Based on our experience, advocacy training for library staff should build knowledge and skills in the following areas to be most effective.
  • Impact. Library staff should understand and be able to demonstrate the impact libraries have on individuals and their community as well as libraries’ contribution to government priorities to convince government and the community to support their library
  • Communication and Messaging. Library staff should be able to develop messages relevant to their target audiences and learn how to communicate those messages to the public, media, and government officials.
  • Networking. Library staff should be able to identify the key stakeholders in their community and to build alliances with community members, community leaders, and government
  • Attitude and Self-Confidence. Often library staff have some or all of these skills, but lack the self-confidence or attitude to put them into practice. Building confidence and improving attitudes is an important element to the training
  • Sawaya (2009)

We recommend that advocacy training for library staff follow these principles to have the greatest impact on library staff behavior.
  • Training Methodology. Adult learning methodology that is interactive and allows participants to learn from each other is most effective
  • Trainers. Trainers who represent diverse professional backgrounds bring expertise from their disciplines to the library field thereby broadening library staff perspective on different approaches to sustaining their library
  • Training Participants. Including other members of the community gains support for library staff in implementing advocacy initiatives and builds skills of other members of the community to be advocates for the library
  • Output. Participants should develop concrete outputs so they are able to put their newly acquired knowledge and skills into practice during the training and are prepared to act once they return to their communities


The principles of high quality graphical data presentation have been articulated by William Cleveland, Edward Tufte, Howard Wainer and others. Good graphing practice is based on these three rules:
  • Be clear. (Strive for clarity. – William Cleveland)
  • Be fair and accurate. (Tell the truth about the data. – Edward Tufte)
  • Be thorough. (‘You can see a lot just by looking.’ – Yogi Berra)

The actual graphics are in colour and will be shown on screens.


  • No matter how many users one talks to in designing a system, there will be a gulf between what a user wants and what a system can do.
  • The belief that users even know what they want, or that somehow a library can correctly interpret the needs of users is at best presumptuous.
  • To truly build systems that met the needs of users, we must let them build these systems directly.
  • How can you tell a pioneer in a crowd?
  • He’s the one with all the arrows in his backside
  • Change is like Heaven
  • Everybody thinks it is a good idea.
  • But nobody wants to go there first.


This page is an example of the actual teaching material for IFLA Statistics for Advocacy training course.

Through this module the participants should gain a basic understanding of:
  • The value of systematic measurement
  • Some basic measurement techniques (using manual or automated systems)
  • Some typical errors and mistakes
Ten well-established methods for systematic data collection are listed below.

1. Mail questionnaires

Questionnaires are sent by mail to a sample of persons.

2. Interviews by phone or mail

The persons in the sample are contacted for interviews. Where phones are widespread, most interviews can be conducted by phone.

3. Exit interviews

Quick interviews with users on their way out from the library. Often used to register current activities – what people did during their visit – as well as their evaluation of facilities and service (satisfaction). Do include a question about frequency of use.

4. Visitor questionnaires

All visitors during a period are asked or encouraged to fill in a questionnaire. May be used to collect data on their library use in general – or on what they did and experienced today. Short questionnaires that can be completed then and there are recommended. Do include a question about frequency of use.

5. Digital surveys

Two possibilities are
  • All visitors to the library web site during a period are asked to fill in a questionnaire that "pops up" on the web. 
  • If a complete list of e-mail addresses is available, an e-mail with a link to a web form can be sent to a representative sample from the population 
Do include a question about the frequency of library use. Short questionnaires are recommended. Response rates may be low, especially for "pop-up questionnaires"

6. Focus groups

Small groups of people from the community, typically five to eight, are “interviewed” as a group about their views of the library.

7. Informant interviews

People with a special position in – or exceptional understanding of – the local community are interviewed. Such interviews may focus more on the informant’s perception and interpretation of the total situation than on her or his personal views and experiences.

8. Lending patterns

Since loans are registered, all lending transactions between users and libraries are logged by the systems, whether they are manual or electronic. These transaction logs contain lots of useful data that can be analyzed.

9. Web logs

Libraries with web pages can analyze the traffic their web sites generate, with free digital tools.

10. Observation

Direct observation is well known as a way of finding the number of visitors to libraries that lack electronic counters. Observation at the entrance can also be used to find out who the visitors are (sex, age group) and how long they tend to stay. Observation based on repeated walks through the library can be used to gather data about user activities.


This page is another example of the actual teaching materials for IFLA Statistics for Advocacy training course.

In statistics, the group we want to study is often called the universe or the (statistical) population.

Public libraries

For most public libraries, the population (of the community) is so large that we cannot contact every single individual. Instead we have to take a population sample. 

A sample should represent the whole population. It should therefore be as similar to the population as possible. The sample should have about the same percentage of men and women, of young and old, of workers and non-workers, of literates and illiterates, as the population as a whole. Such a sample would be a representative sample.

It is not easy to select representative samples. Unless you follow some rather strict rules, your sample is likely to be biased. The values that you find in a biased sample can not be generalized to the population. Biased samples give skewed results.
  • If just pick the persons you happen to meet on the street around noon, say, the sample will certainly be biased. School children and full-time workers are busy elsewhere. Night-shift workers may be sleeping. The sick and the very old stay at home.
  • If you just select the persons you find in the library, the sample will also be biased. Some people visit libraries every week. Others only come in once or twice a year. Inside libraries we are much more likely to encounter the regular users. 
If regular users are overrepresented, we will probably get answers that are too positive. People who use the library often, probably like the library a lot. The occasional user may be more critical. Many non-users are not interested in libraries at all. 

Biased samples are very common. It is very tempting to select people that are easily available. Such samples are sometimes called convenience samples. Data from convenience samples tell very little about the population as such. 

They are convenient for the "researcher", since they reduce the amount of work. They are inconvenient for the reader, who is presented with false, misleading or irrelevant pieces of information.

Define your target group

There are many different methods that may be used to gather new data about users in single libraries. 

The first thing you have to decide, however, is which group of persons you want to study.  The target group may for instance be be: 

Public libraries
  1. Everybody who lives in the community: potential users 
  2. People that have a library card: registered users 
  3. People that actually use the library: active users 
Academic libraries
  1. Everybody who attend or work at the institution 
  2. People that have a library card 
  3. People that actually use the library
 School libraries
  1. Everybody who studies or work at the institution 
  2. People that have a library card 
  3. People that actually use the library 
 Special libraries
  1. Everybody who work at the institution 
  2. People that have a library card 
  3. People that actually use the library Note 
In library statistics, we usually define the third group (active users) as people that have used the library during the last twelve months.

Control your sample

To avoid bias, your sampling procedure should be based on statistical guidelines.

  • In case 1 (potential users), you should select your respondents - the people you contact - from a list of all residents (people that live in the community). Taking every 10th, or 30th, or 100th person from such a list is quite OK. The total number could be a few hundred persons.
  • In case 2 (registered members), you can do the same thing, from a list of all library members
In case 3 (active users), you have two possibilities.
  • You can contact a sample of visitors as they leave the library - and ask them how many times they used the library during the last twelve months - approximately - in addition to all the other questions you may have. Once you know the frequency of use, you can - with a bit of statistical help - correct the bias.
  • You can select every 10th (or 30th, or 100th) name from a list of all people who borrowed one or more items last year and try to contact them by phone or mail.
Academic libraries

In academic libraries the relevant populations tend to be much smaller than in community libraries. 

In small institutions, it may be possible to send questionnaires to all students, or at least to all staff members. In larger universities, sampling is still a very useful tool.


Librarians have, in general, very little systematic information about activities inside their libraries. CounT The Traffic (TTT) is a cheap and simple method to gather such data. It gives a good numerical picture of how library users actually use the various parts of the library.  

TTT reveals both the quality  - or the kinds of activity - and the quantity of use. Combined with data on the number of visitors it will also indicate the average length of stay.

Tours of observation

TTT is based on regular  “tours of observation” through the public areas of the library, normally once an hour, during one or two weekly cycles.  The actual observation days - Monday, Tuesday, Wednesday, aso.  - should preferably be distributed over several weeks. Data gathering and analysis can be carried out by the library’s own staff rather than by  hired observers and consultants. To carry out a CTT, you need:
  • A time plan, with dates and times for all observation rounds (”sweeps”)
  • A floor plan covering all the public areas of the library
    • The plan should be divided into functional zones: reception area, newspaper section, PC area, etc.
  • A list of activities to be observed
    • We recommend using - or adapting - the standardized TTT list of Activities.

Two hours

You can test out the method for yourself in a couple of hours by
  1. sketching a floor plan with zones
  2. making one copy of the list of activities for each zone
  3. doing a single tour of observation, noting the number of people engaged in different observable activities as you pass.
  4. putting the numbers in a spreadsheet
  5. finding the distribution of users by zone
  6. finding the distribution of users by activity

Three days

If you decide to gather data about one typical day at the library, this will require two to three days of full time work:
  1. read more about the method and how it can be used
  2. adjust the zones - based on the test run
  3. walk through the library at regular intervals throughout the day
    • choose the distance between rounds so that you end up with several hundred observations - at the least
    • in smaller libraries - less than thirty users present on the average - rounds may be done every twenty or thirty minutes
    • in medium-size libraries, with thirty to one hundred simultaneous users, rounds could done every hour
    • in larger libraries, every second hour should be sufficient
  4. put the numbers in a spreadsheet
  5. calculate the distribution of users by hours
  6. calculate the distribution of users by zones
  7. calculate the distribution of users by activity
  8. explore the bivariate distributions
If you want to go deeper, see TTT method.

7. Development

Public libraries are organizations that produce and deliver media-based services to their local communities.

From a production point of view our statistical data can be roughly divided into four main categories:

  1. Statistics about input - such as the number of staff, the number of books, the number of seats and the total budget
  2. Statistics that reflect the production process - such as the number of registered users, the time spent on cataloguing or the opening hours per week
  3. Statistics that show the output or the services that are actually delivered - such as the number of visits, the number of loans and the number of computer sessions realized in a year
  4. Statistics that (may) reflect the outcome of the services - such as changes in reading patterns, in school results and in community activities

Everybody is interested in the budget. Budgets are treated as a zero-sum games.  

  • Librarians are concerned about the library itself: inputs, processes and outputs. 
  • Politicians are interested in outcomes. 
  • But the relationship between library outputs and socio-economic outcomes is seldom investigated.

 Six areas

The Global Libraries program of the Gates Foundation have pointed to six areas in which libraries can make a difference.


  • People acquire new or improved skills at the library

  • Libraries offer on-line reading programs


  • Libraries refer people to relevant on-line information and services

  • Users get improved health choices

Culture and Leisure

  • Library become social centers

  • Events and activities lead to community re-vitalization



Economic Development

  • Librarians direct farmers and local entrepreneurs to relevant information

  • Small businesses become more productive



  • Librarians teach people to use e-mail

  • Isolation of remote communities decreases


  • Librarians teach people to access e-government services

  • Improved citizen uptake of government services

 Any public library, in Africa or elsewhere, could, in principle,
  • choose one or more of these impact areas
  • develop (or continue) relevant library activities
  • observe what happens
  • try to measure the local impact
  • describe the results
  • tell the community
  • tell decision-makers


In a global perspective there are many positive signs of growth. 

Official statistics

A fair number of countries now make detailed library statistics available on the web. I am aware of the following – but there may be others:
  • the Nordic group: Denmark, Finland, Norway, Sweden
  • the Netherlands
  • the Commonwealth group: Australia, Canada, New Zealand
For more information about individual countries see Plinius Data.

Some countries, like Denmark and New Zealand, publish their primary data as spreadsheets. They offer access - which is good, but are large and difficult to work with. Other countries, like Finland, Norway and the Netherlands, provide structured databases. These are easier to manage, but actual usage still seems to be low. Librarians are more comfortable with words than with numbers. 

The road from potential access to actual usage requires statistical literacy. 

Indicators and assessment

In Germany, the library index BIX is well established both in the public and in the academic library sector. Libraries participate on a voluntary basis. But steady work with real data over many years has made an impact. In the United States, the new LJ Index for public libraries is professionally designed and very well presented. 

In academic libraries, the LibQual survey of user satisfaction is well established – and has contributed to a culture of assessment. Like BIX, LibQual is also moving into other countries. The web facilitates horizontal interaction and can support the social (as well as the centralized) approach to indicator development. 

In its Global Libraries Programme, the Bill and Melinda Gates Foundation insists on systematic data collection and evaluation. A dozen countries have benefited from this hard-nosed approach  - and could be used as models by other library communities. See Sawaya (2009) for an overview.

In the United States, a coalition of library and local government organizations, including the Gates Foundation, launched the Library Edge initiative in 2011. Edge is developing a rating system comprised of benchmarks and indicators designed to work as an assessment tool. The instrument looks very well designed: balanced, clear and comprehensive. 

Observation methods

Tools for observing user behavior inside libraries have started to appear – in Canada (seating sweeps), in the US, in Sweden and in Norway (CounT The Traffic). SeeTTT: Bibliography for details.

Web metrics

In public libraries, workable indicators of web traffic have started to appear, with Denmark as the front runner (Danmarks Biblioteksindex). In academic libraries, standardized measures of database use are being developed (COUNTER).

Global statistics

IFLA has taken a strong interest in statistics for advocacy and has adopted a 
statistical manifesto. The IFLA/FAIFE World Report series is a biennial publiocation that reports on the state of the world in terms of freedom of access to information, freedom of expresion and related issues. Includes statistics about the no. of libraries The reports are available online.

Libraries represent a small sector within the big picture. In Norway, which has a well-developed library system, its share of employment is about 0.2 percent. If you select one thousand workers at random, you would find one librarian and one library assistant. We can not expect politicians and managers to invest heavily in library statistics unless they invest in statistics in general. If the context improves, libraries improve. 

We should therefore support the development of educational and cultural statistics. This is already happening. Better educational statistics will help us document the work of academic and school libraries. Better cultural statistics will help us document the work of public libraries. 

In the South we should also encourage better community statistics. The more we know about local communities, the better we can document the need and assess the impact of school and public libraries. Much is happening in the wider field of statistics - see Global stats for some examples.


Some of the problems we face are:
  • Information is scattered and hidden. Library researchers and library agencies need to put their methods, their data and their discussions on the open web.
    • If this is not done, ordinary librarians will lack access to the results of empirical studies
    • The professional debate among experts will also be hampered by lack of information
  • Library authorities spend too much effort on repetitive data collection – and far too little on professional presentation and analysis of the results.
    • As a consequence, ordinary librarians take little interest in the statistics.
  • Many countries lack functioning statistical systems at the national level.
    • Here we need to offer simple methods for regular data collection that interested libraries can manage on their own

Libraries must compete for attention. Our access to verbal and visual resources is overwhelming. We live in an attention economy. There is no shortage of information. There is a shortage of eyes.

Within the library sector, we who work with statistics must also compete for attention. Academic libraries are beginning to see the need for systematic data. But public libraries are less committed. 

We want to change habits. That requires a combination of training, marketing and politics. We have no direct power. The customer is always right. 

Creative use of social media like blogs, twitter and even flickr is one approach. 


At the moment, the quantity and quality of statistical information on libraries varies enormously between countries. A handful of countries run advanced statistical systems with comprehensive coverage of their public, academic and special libraries. School libraries, which tend to be small and poorly staffed, is still a statistical problem, however. 

Efforts have been made to establish global library statistics. I do not believe, however, that a centralized approach, with one data base, standardized reports, a fixed set of indicators, an elected governing board, and so on is sustainable under today’s conditions. A distributed network of volunteers, with a bit of coordination, is something else.
  • Unesco produced international library statistics a long time ago, but discontinued the series. The statistical basis was too poor. 
  • IFLA has tried, and failed, to motivate Unesco to start again.
  • OCLC has tried, and failed, to create a global collection of library statistics

For empirical evidence I refer to the section onOCLC. Let me also add that failure is useful. Unless we accept the risk of failure, and are willing to learn from failure, we are stuck.

Level 4

The most advanced countries (based on web information) seem to be Finland, Norway, Denmark, the Netherlands and New Zealand. Sweden, Canada (some states) and Australia (some states) could be added. Let me call them group 4. 

Level 3

This group consists of countries with well-developed library systems and some good statistics at the national level, like Great Britain, Germany, Italy, much of Eastern Europe, the United States, Chile, Singapore and a few others. The main problem, seen from abroad, is the lack of extensive, user-oriented web publishing of the data. 

Level 2

This group consists of countries with more uneven and fragmented library systems. The public library sector tends to have greater difficulties than academic and special libraries.  But even the latter library types may suffer from a lack of national-level coordination. Most countries in the world belong to group 2. 

Level 1

The least developed countries (library-wise) have no national statistics whatsoever. In a few cases they publish some scattered library data in statistical yearbooks or reports on cultural statistics. But it is hard to find and hard to use such information. People do not have time to visit the few big libraries that receive such publications in order to dig out a few numbers. 

Central bodies

The quality and quantity of library statistics depend heavily on the size and strength of library organizations at the national level. Data must be collected, processed, published and applied by someone. That someone may for instance be
  • a specialized public agency, like the Institute of Museum and Library Services for public libraries and the National Center for Education Statistics for academic and school libraries in the United States. Individual states have their own systems.
  • the Central Bureau of Statistics, like in Japan
  • the relevant government ministries, for public libraries in Finland
  • the National Library, for academic libraries in Finland and for all libraries in Norway
Local data collection

In this situation information at levels 1-3 must be gathered locally, by people who live in the country, who are familiar with its statistical system and who can follow the constant changes that are likely to occur. Alle these countries are trying to improve their systems, from wherever they happen to be.

Processing needed

Level 4 is different. Information from level is available on the web to all interested parties. There is just so much data available. What we find in these countries is not a lack of data per se, but a shortage of statistical analysis, debate and practical use. There is too little processing going on.


This morning I said:

  • For many years we have tried to change the statistical practices of librarians through committees, concepts and proposals from the top.
  • This approach does not work.
  • We have to change from a top-down to a bottom-up approach.
  • Change can be encouraged at the top, but must be realized at the bottom.

That has both a negative and a positive side:

  • We have to do most of the statistical work ourselves
  • We can start immediately

Support from the top is welcome, of course. But the idea of uniformity is wrong.

Libraries cannot move forward at the same speed, one step at a time, like soldiers on parade. Start with networks and alliances among those who are capable and willing to invest in statistics.

  • Let the strong move ahead
  • Gain experience
  • Gather evidence
  • Support each other

As the field develops, weaker libraries can follow in their path and learn from their practices.

Underdeveloped statistics

Library statistics are rudimentary in most countries.

Improving this situation, through workshops, lectures, publications, web debates, and so on, must also be done through local initiatives.  Each country has its own library community. It is possible and useful to visit the discussions that go on in neighbouring countries.

But in Europe, at least, it is almost impossible to be an active participant in several discussion communities at the same time.

The strategy, or set of actions I believe in, are:
  • study the statistical system in your own country
  • share that information on the web
  • use a place that is easy to locate and easy to link to
  • report on current library statistics from time to time
  • interpret the data from time to time

Library censuses

A population census counts the people that live in a particular country and register some of their characteristics. A library census does the same for libraries.

Censuses describe the whole universe and provide a general basis for economic and social planning. They are large projects and are usually conducted every ten years.
Dominican Republic

Library censuses can be very useful in low- and middle-income countries ("the South"). Let us take a look at a well-designed census. In the Dominican Republic the National Library carried out a major study of the country’s public libraries.

The first national census of Dominican libraries was carried out in 1999. The investigation was carried out by the Oficina Nacional de Estadística with the assistance of the national library. It was an important step ahead, but the study suffered from many methodological weaknesses. The second census, about ten years later, was much improved.

The long-term, or strategic goal of the project, was to establish a sound empirical basis for library planning, so that libraries could become tools for social development in a wide sense. The goal of the first phase of the study (“the pre-census”) was to identify and briefly describe all existing information provision units in the country. This investigation, which covered school, public, special and academic libraries, was completed in 2010.

The goal of the second phase was to analyze the conditions and the situation of the public libraries, including their relationship with the local community.

Data collection

Data collection combined qualitative and quantitative methods

  •  a survey questionnaire with closed, semi-closed and open questions
  • a guiding list of questions for focus groups
  • a paper form to evaluate the collections

Some technical details

  • The pre-census identified 239 public libraries. Ten percent of these were selected for focus group interviews. The project developed and tested an interview guide with eleven open questions. Interviewers were trained in June 2010. The interviews were carried out in June 2011. They were taped and then transcribed.
  • One important observation: the users that participated had in general no clear concept of what a library was, and even less of concepts like “public library” and “school library”. They did not know what services libraries can contribute to communities. This means that they lack a framework for expressing opinions on services, staff qualifications, the promotion of reading through libraries, and so on.
  • The inventory study, which was restricted to non-fiction, represented a practical way of describing the currency of the book collection. The visiting census staff was asked to look at every tenth title on the (non-fiction) shelves, and to note the titles and the year of publication. The project estimated that this task will take about four hours in a small collection (< 500 books). In a collection of 1-2,000 books, up to 12 hours may be needed.

National Information System

The data were entered into a National Register and Information System for Libraries (Sistema Nacional de Información y Registro Bibliotecario – SINIREB), which described

  • the physical infrastructure
  • the collections
  • user characteristics
  • user satisfaction
  • technical processes
  • current services

in each library.


Most of the information above is based on correspondence between the Statistics and Evaluation Section (IFLA) and the organizers of the Dominican census. The organizers presented their work at the 2012 IFLA conference in Helsinki.

Plinius Data

People who work with library statistics can support the field by sharing their data.

The organization of library statistics differ from country to country. Mapping, using and comparing such statistics is often time-consuming.

Since I have an interest in comparative statistics, I have tried to document my own work in this field. The basic idea is simple. When I study Norwegian, or German, or South African library statistics, I must spend some time to explore the statistical system and additional time to process some of the data. If I publish my map and my processed data on the web, others can build on that. 

The map may be sketchy and the data may be limited, but a small slice of the pizza is better than nothing. Some relevant statistics from neighboring fields – like media, culture and education – may also be included.

Global statistics

Library statistics can not stand alone.

When we work with statistics about libraries, we will often need statistics from other fields. Population data are needed to calculate some of the most basic library indicators - for instance loans and visits per capita. Statistics about literacy and reading, health and education, infrastructure (water, roads, electricity, ...) and culture can often be used to
  • show the need for library services - focused on particular problems to be solved
  • the impact of library services - based on comparisons or time series
Library associations that want to use statistics for advocacy must therefore spend some time getting acquainted with relevant official statistics - outside the library sector - in their own countries.

Since Global statistics for advocacy is an international rather than a national project, we have to do the same at the global level. Since the Second World War, lots of good work has been done, by the UN and others, to collect, systematize and present comparative national statistics. 

 During the last fifteen years, internet has facilitated the development of sophisticated, user-friendly and interactive statistical delivery systems. The most impressive one to date is Gapminder - with the slogan Unveiling the beauty of statistics for a fact-based world view. Google has used Gapminder as a model for in its own platform for Global Public Data.  

The United Nations Population Information Network has created an excellent guide to population information on UN system web sites. The World Factbook - a well-known source among reference librarians - is still going strong. The data are good though the publisher (CIA) may be spooky. GeoHive - with detailed population data - is maintained by a single person (Johan van der Heyden). 

South Africa

In 2009 Elisha Chiware described the main problems in South Africa as follows:

  • Statistics collection tends to be quantitative and there is no trend analysis yet
  • There is an absence of readily available centralized sources of library data in the country
  • To a large extent the allocation of university library budgets in South Africa is based on factors other than statistical data that reflect library use and impact
  • There is need for authoritative statistical studies identifying institutions’ standing within a peer group in order to convince institutional executive managements of the need for more resources – it is important to use peer groups that are acceptable to management
  • Lack of accurate statistics in the e-arena creates problems for forecasting future trends

He noted, however, that many academic libraries have undertaken LibQUAL surveys to solicit, track, understand and act upon users’ opinions of service quality.

Strong networks

A global trend

Networks are part of global development

  • The industrial economy depended on linear production processes, from raw materials to consumer goods. 
  • Value was added step by step.
  • The new global economy is sometimes called a network economy. 
  • Value is added through complex interactions of many different parties.
Strong networks

Three examples of statistical networks are

Other potential networks of strong libraries are

as well as

  • major university libraries
  • major technical libraries


The Edge initiative was launched in 2011 by a coalition of library and local government organizations, including The Bill and Melinda Gates Foundation. The goal is to develop a suite of tools that support continuous improvement and reinvestment in public technology. Thia approach was probably inspired by the Gates Foundation, which has always emphasized evidence-based planning and assessment.

Edge is developing a rating system comprised of benchmarks and indicators designed to work as an assessment tool - will help library staff understand best practices in public access technology services for their communities and determine what steps they need to take to improve their technology services.

The whole system looks very well designed: balanced, clear and comprehensive. The instrument has been developed for a US context, but the basic approach could well be a model for similar intitiatives in other countries. 

Since we are looking at statistical indicators rather than evaluation and assessment in general, I have extracted the more statistical components. Version 1.0 of the benchmarks asks for three types of statistics: survey data, book-keeping data and web traffic data,

User surveys

The library surveys patrons annually about public technology use and outcomes in the following purpose areas: 
  • Workforce development 
  • eGovernment 
  • Education 
  • Health & wellness
The following questions are included in an annual survey:
  • Patron satisfaction with library technology
  • Personal importance of library technology
  • Importance of library technology to others in the community
Book-keeping data

The following metrics are tracked on an ongoing basis:
  • Number of hours public devices are in use by patrons
  • Number of attendees in technology classes
  • Average wait times for public devices
  • Number of wireless sessions
  • Number of requests for one-on-one technology help
  • The library has a sufficient number of device hours available on a per capita basis.
    • Proposed categories: 3 to 6 hours; 6 to 12 hours; more than 12 hours per capita
Web traffic
  • Web analytics are used to evaluate the use of online library resources annually
  • Library website usage reports are reviewed at least quarterly
  • Subscription content (e.g., databases) usage reports are reviewed at least quarterly

Fast and slow

Today I have looked at statistics, as applied to libraries, from three points of view:
  • as a form of literacy
  • as a form of advocacy
  • as a global trend
I consider
  • the literacy as a teachable set of technical skills and a learnable set of management practices
  • the advocacy as a practical and political approach to library development
  • the trend as a single strand in the global movement from intuitive to evidence-based decision-making 
Norms and behavior

In these three sessions, and the papers that underpin them, I have tried to stay close to the evidence.

Statistics is usually taught as a set of technical instructions, which people are just asked to follow. I am more interested in the actual statistical practices. To what extent do people follow the statistical advice?

If we look at what libraries actually do, we find a surprising discrepancy between statistical norms and statistical behavior. I have tried to document and to explain this gap in a couple of conference papers (Høivik, 2003; 2012).


I am not pessimistic about statistics. We are moving forwards on many fronts. But we need to understand that statistics is a hard subject. Descriptive statistics is not technically difficult. Statistics is hard because it is counterintuitive. The results often contradict cherished beliefs.

To understand the resistance to statistics I'd like to end with a reference to Kahneman's model of human reasoning. We have two thinking systems. System 1 is fast, intuitive and effortless. It is usually right, but occasionally dead wrong.

System 2 is slow, intentional and hard-working. If it involves a community of practice rather than a single practitioner, it tends to be right. Statistics, as well as scientific thinking, belongs to system 2.

System 1 (fast)
  1. Unconscious reasoning
  2. Judgments based on intuition
  3. Processes information quickly
  4. Logical reasoning
  5. Large capacity
  6. Prominent in animals and humans
  7. Unrelated to working memory
  8. Operates effortlessly and automatically
  9. Unintentional thinking
  10. Influenced by experiences, emotions, and memories
  11. Can be overridden by System 2
  12. Prominent since human origins
  13. Includes recognition, perception, orientation, etc.

System 2 (slow)

  1. Conscious reasoning 
  2. Judgments based on critical examination 
  3. Processes information slowly 
  4. Hypothetical reasoning 
  5. Small capacity 
  6. Prominent only in humans 
  7. Related to working memory 
  8. Operates with effort and control 
  9. Intentional thinking 
  10. Influenced by facts, logic, and evidence 
  11. Only used when System 1 fails to form a conclusion 
  12. Developed over time 
  13. Includes rule following, comparisons, weighing of options, etc.

Statistics is very practical. But it is a practical discipline that demands intellectual discipline.


  • Tord Høivik is a retired library teacher from Norway. He has a professional background in statistics and sociology and remains active on the web (https://sites.google.com/site/pliniushome/).
  • This text may be reused for non-commercial purposes, with reference to the source.