Numbers that count
Selecting and using service indicators for public library
Materials for a workshop at the 7th Northumbria International Conference
on Performance Measurement in Libraries and Information Services,
Spier Wine Estate, Stellenbosch, South Africa, August 13-16, 2007.
Oslo University College
With the right methods, it is easy to measure the services your library provides. This
half-day workshop will give you a practical introduction to the use of statistical indicators
in public and academic libraries. We will concentrate on three topics:
1. how to measure lending - by sample surveys and by using catalogue data
2. how to measure visits to the library - by sample surveys and by electronic (or
3. how to organize user surveys - and how to process the data
The workshop will be highly interactive and practice oriented. We will combine brief
introductions, explanatory discussions and practical exercises (individual and in small
groups) with seminar-type reflection towards the end.
Printed teaching materials will be distributed at the start of the workshop. All teaching
materials, including lecture notes, have been published on the open web.
The workshop is aimed at practicing librarians and advanced library students with an
interest - but no particular background - in measuring the impact of their services. The
needs of libraries that lack automated catalogs will be addressed by presenting simple
"manual" survey methods.
Through the workshop the participants should gain a basic understanding of:
* The value of systematic measurement
* Some basic measurement techniques (manual and automated systems)
* Some typical errors and mistakes that should be avoided
* The way statistical results can be used in library planning and advocacy
The teaching materials are available in two formats - as a series of blog posts in Pliny the
Librarian (July 25-30, 2007) and as a single Google Docs file (see link at Pliny the
Librarian - www.pliny.wordpress, July 25).
The materials may by freely used or adapted for teaching and other non-commercial
purposes, as long as normal attribution is provided.
Wednesday, August 15, 2007
1400-1415. Introducing the topic and the participants.
1415-1430. What are your statistical needs?
1430-1500. Use sampling to minimize the work load: how to select books, days
and users. (Blog post: The idea of sampling)
1530-1600. When books meet users: how to measure lending - and the number
1625-1725. Know your users: a practical introduction to user surveys. (Blog posts:
Library traffic and data collection methods)
1725-1730. Workshop evaluation.
1. The idea of sampling - p. 4
1.1. First example: selecting books - 5
1.2. Second example: selecting days - 6
1.3. Third example: selecting users - 7
2. When books meet users - p. 8
2.1. One year at Maktaba - 9
2.2. Who borrows what? - 10
2.3. Activity distributions - 11
3. Listening to your public - p. 12
3.1. Study visitors - 12
3.2. Study users - 12
3.3. Study publics - 12
3.4. Do not confuse visitors with users - 13
4. When users visit libraries - p. 14
4.1. The number of visits - 14
4.2. But what do they do? - 14
4.3. The floor plan - 14
4.4. Fifteen activities - 15
4.5. Observation technique - 16
4.6. Observation schedule - 16
4.7. Data processing - 16
4.8. Once a week ... - 17
4.9. Aggregating the data - 17
1. The idea of sampling
I want to measure what is happening at my library. I want to know, say, about:
1. the circulation of stock: which items are in heavy and which are in low demand
2. the number of people who visit the library
3. the ways they actually use it: study, recreational, IT, meeting friends, etc,
For the sake of the argument, I assume that the library works on a manual basis: no
automated catalog and no electronic counter at the entrance. But the idea of sampling is
the same in the manual and the automated case.
It is clear that I cannot monitor and write down what is going on a continuous basis. That
would more or less double the total work load.
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 matter whether we had 13.415 or 13.671
visitors in 2006. But the difference between thirteen and fifteen thousand visitors
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 real 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. Thge 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.
1.1. 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 turn 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 200 consecutive cards from the nearest drawer. Nor should you
rummage around, taking one here and one there as the mood takes you. The sample
come from the whole population
not depend on a series of human choices
You should act like a robot with no personal interest in the outcome.
There are many ways of achieving this. The simplest is probably to take (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.
1.2. Second 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 experience, that
library use tends to vary systematically during the day, during the week and during the
If I want to know the true number of visitors,
I cannot take my "best hour" - and multiply by 1724.
I cannot take my best day - and multiply by 288
I cannot take my best week- and multiply by 48
I have to choose my sample from the "whole population" - and to do it in a proper
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
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.
1.3. Third example: selecting users
Concepts are important. If we want to study users, we must first decide the limits of the
For instance, does user mean:
1. The people that visit the library on a regular basis?
2. The people that have visited the library at least once during the last year?
3. The people that have visited the library at least once during the last five years?
4. The people that are registered as users?
5. The people that are registered as users and have borrowed materials during the
6. The people that are registered as users and have borrowed materials during the
last five years?
And so on.
In this example I define user as a person that is registered as a user. My population
consists of a set of registration cards.
I want to check the social impact of the library by looking at the geographical distribution
of the users. Where do these people live? Are there places we have "missed" - and where
we might do some extra marketing?
Let us say we have 6.000 registered users. I choose 200 at random. The selection
procedure follows the first example. Since 6.000/200 = 30, I may simply take every
thirtieth card, write down the addresses and plot them on a local map.
2. When books meet users
It is common practice to report on lending - and on library activcities in general - once a
year. The reporting period is usually the chronological year (2006, 2007, 2008, ...) - but
it could also be the school year (05/06, 06/07, 07/08, ....) or, in some cases, the
financial year (if it differs from the chronological year). Since school years are separated
by (summer) vacations, some libraries utilize the quiet period to organize their statistics
amd write their annual reports.
One year's lending
We may visualize each year's lending as a table with books (or media) on the horizontal
and lenders (or users) on the vertical axis.
Table I. Books and users in 2006
Book A Book B ... Book ZZZ H SUM
# User 1
User 1 1 0 ... 0
# User 2
User 2 1 1 ... ...
... ... ... ... ... ...
# User n
User nnn 0 0 ... 1
# Loans of
V SUM # Loans of A # Loans of B ... Total loans
In this way of thinking, all users (active as well as passive) and all books (with and
without loans) should be included.
The most basic number is of course Total loans. This number can be related to
The number of users: Loans per user = Total loans divided by the number nnn
The size of the collection: Loans per book = Total loans divided by the number
Some books are in great demand, some in moderate demand, and some do not circulate
at all. The Vertical sums indicate the popularity of each book.
Some users are heavy readers, some are light readers, and some do not borrow books at
all. The Horizontal sums show how users differ. Both sets of numbers are interesting and
can be analyzed with simple methods.
If your catalogue is automated, all the data you need should be available "inside the
system". To get them "out of the system" you must set up a program to generate the
relevant reports. Each catalogue system has its own way of doing this - and you may
need the help of data person. Here I consider what can be done manually.
Even a small community library will often provide many thousand loans during a year. To
get a manageable set of numbers, we must take a representative sample. The method
for selecting days, which was outlined during the first session, will - roughly - cut the
numbers (and the statistical work) by a factor of 25.
2.1. One year at Maktaba
Let us imagine a library called Maktaba - the word means library in Swahili. The head of
the library, Mrs. Sabuni, has decided to define 12 days during the year as counting days.
She has also asked one of her assistants to register all loans during these twelve days.
At the end of the year, she has a sample of approximately 750 loan transactions. The
annual total must be around 750 * 24 = 18.000 loans. If Maktaba has 6.000 registered
users and 9.000 books, we find:
Loans per user = 18.000 / 6.000 = 3.0
Loans per book = 18.000 / 9.000 = 2.0
We can further use this sample to study
who our active users are - by age, gender, education, and so on...
which books are being used - by age level, genre, subject, language, difficulty,
and so on .... .
The simplest way is probably to
write down the data for each loan on a 5 x 8 card
sort the cards manually
count the number of cards in each stack
This can be done repeatedly, for different variables (age, gender, age level, genre, ....)
Note that the sample reflects actual usage - not the total universe (population) of library
users or books. Only active users and borrowed books are included - and the chance of
being selected increases with the level of activity.
2.2. Who borrows what?
We can also use this sample to study "who borrows what" - in other words, the
relationship between user characteristics, on the one hand, and book characteristics, on
the other. We might for instance find:
Table II. Books and users
children and Adult fiction Sum
Children 200 50 50 300
Adult women 70 100 100 270
Adult men 30 50 100 180
Sum 300 200 250 750
Using Table II, we may compare the three user groups by calculating the percentages
within each group:
Table III. Distribution of book categories in different user groups. Percentages
children and Adult fiction Sum
101 % (N =
Children 67 % 17 % 17 %
100 % (N =
Adult women 26 % 37 % 37 %
101 % (N =
Adult men 17 % 28 % 56 %
100 % (N =
Sum 40% 27 % 33 %
In this particular case, it is also meaningful to compare the three categories of books, by
calculating the percentages within each category:
Table IV. Distribution of users for different categories of books. Percentages
Adult fiction Adult non-
Juvenile loans Sum
loans fiction loans
Children 67 % 25 % 20 % 40 %
Adult women 23 % 50 % 40 % 36 %
Adult men 10 % 25 % 40 % 24 %
100 % (N = 100 % (N = 100 % (N = N0 100 % (N =
300) 200) 250) 750)
2.3. Activity distributions
Since the sample of loans is taken "from inside the table", it cannot be used to study the
Vertical or the Horizontal sums.
If we want to study the lending distribution of individual users, we could select -
say - 120 users at random (every fiftieth user in the card register) and study how
many books each of the had borrowed during the year.
If we want to study the lending distribution of individual books, we could select -
say - 180 users at random (every fiftieth book in the card catalogue) and study
how many times teach had been borrowed during the year.
The curves that represent "activity distributions" tend to be J-shaped rather than bell-
shaped: high near the origin - and lower the further you move right on the X-axis.
Most users borrow only a few books a year. There are, in other words, many light users.
Relatively fewer persons borrow books regularly. Let us call them moderate users. Only a
very few use the library intensely throughout the year - and count as heavy users.
A typical distribution, based on sample of 120 users, might look like this:
Here we have:
15 heavy users = 13 %
35 moderate users = 29 %
70 light users = 58 %
When you deal with J-shaped distributions, I recommend using ordinal parameters
(media, quartiles, percentiles; quartile differences) rather than averages and standard
3. Listening to your public
3.1. Study visitors
Is often used to study the user's sequence of activities and her or his actual
physical path through the library. Observation may be participative or not. If it is
not participative, it may be overt - people know they are being observed - or
Usually, a sample of visitors during a period are approached for a brief interview.
May be used to collect data on their library use in general - or on what they did
and experienced today.
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).
Usually, 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 experience today. Fairly short questionnaires that can be
completed then and there are recommended.
3.2. Study users
A sample of users - usually from a list of registered or active users - are
approached for interviews. Since these often takec place in peoples' homes, they
may be a bit longer than the "surprise" or ad hoc visitor interviews.
Since users and loans have to be registered, all lending transactions between
users and libraries are logged by the systems, whether they are manual or
electronic. Library systems
Questionnaires are sent to a sample of users - usually from a list of registered or
Small groups of users - typically five to eight - are "interviewed" as a group about
their library experiences and expectations. The setting is more social and normal
than is the case with questionnaires and interviews.
3.3. Study publics
A sample of persons from the local community - both users and non-users - are
approached for interviews.
Questionnaires are sent to a sample of persons or addresses.
Small groups of people from the community are "interviewed" as a group about
their views on the library.
People with a special position in - or exceptional understanding of - the local
community are interviewed. Such interviews, which are very common in
anthropological field work, tend to focus more on the informant's perception and
interpretation of the total situation than on her or his personal views and
3.4. Do not confuse visitors with users
When you collect and analyze data, it is important to be aware of the difference between
visitors, users and publics. Library publics consist of users and non-users. Users may be
divided into three groups - heavy, moderate and light users - or subdivided even further.
The people we tend to meet in public libraries are the steady customers (the heavies),
with a sprinkling of moderates. When you investigate visitors, you are really studying the
universe of library visits rather than the population of users.
We know, in general, far too little about the composition of - and the
differences between - the various subgroups. Above I list a variety of data collection
methods - related to each of the three groups.
There are many other methods that could be used. They are described in many books. In
smaller libraries I recommend methods that are simple, standardized and so cheap that
studies can be repeated often. If necessity is the mother of invention, repetition is the
father of understanding.
One statistical mistake is very frequent. People study library visitors - and assume the
results apply to library users. This is not the case.
I will illustrate with a religious example. In Norway, 85 % of the population are
members of the Lutheran State Church. We could call them State Church users. On an
average Sunday, about 3 % of the population attends High Mass. These are State Church
Our first female minister was ordained in 1961. Six of Norway's nine bishops boycotted
her ordination. Our first female bishop was consecrated in 1993.
I note, in passing, that Denmark ordained their first female minister in 1948 - and
Sweden in 1960. The Anglican Church of South Africa did the same in 1992 - and the
Mombasa Diocese of the Anglican Church of Kenya in 2000.
If I want to know what our Lutheran visitors think about female bishops, I could conduct
exit interviews on Sundays. If I want to know what our Lutheran users think about
female bishops, I would have to phone or visit them in their homes.
The distribution of answers will probably differ a lot. Conservative Lutherans often go to
church every Sunday. Agnostics and atheist members of the church - there are lots of
those in Norway - might visit once a year - on Christmas Eve, at a wedding or at a
In the same way, a random sample of library visitors will necessarily include a high
percentage of intensive library users. Those that come nearly every day have a much
greater chance of being selected than those who only visit the library a few times a year.
Frequent users are probably more satisfied with the library than infrequent users -
otherwise they would stop coming. Thus, visitor studies are likely to give an image that is
too rosy. If you aim to serve the community as a whole, you should listen to the public in
general - non-users as well as users.
4. When users visit libraries
4.1. The number of visits
Libraries generally find it easier to count the number of loans than the number of visitors.
All loans are registered, by manual or electronic methods. Electronic systems can show
the number of loans day by day, or even hour by hour. Manual loans can be added up at
the end of each day. If there are libraries with manual systems that skip the daily
counting, they can get a good annual estimate by using counting days, as explained
Visitor numbers are more tricky. Many libraries have electronic counters at the entrance.
Visitors cross the beam twice - going in and going out, and libraries often calculate the
number of visitors by dividing the total by two. But I would not rely totally on electronic
counters. We should distinguish between visitors that come to libraries as customers -
and visitors that just happen to pass the beam - including dogs, messengers and children
running in and out. The instrument - in this case the counter - should be calibrated. We
can do this by selecting a sample of days (or hours) and carry out a manual count while
the electronic counter is running.
Manual counts are best carried out on a sample of days throughout the year. Manual
counts have the advantage that some personal characteristics - gender, and to some
extent age - can be included as well. A simple way of separating children from the rest, is
to see whetrher they are below or above a a particular height (like they do in some
amusement parks). By calculating the number of loans per visitor, we also get an idea of
the library profile: is it mainly a place for lending or a place for reading and other
activities on the premises.
4.2. But what do they do?
Librarians have, in general, very little systematic information about what goes on inside
their libraries. They have many impressions, ideas and intuitions that derive from their
personal encounters with users on the job. But this information is qualitative rather than
quantitative. They know about the types of activity - reading, talking, snoring - that
occur in libraries, but not their relative frequencies.
For evaluation, planning and advocacy purposes it is very useful to have solid data in this
area. Here I present a data collection method, based on systematic observation, that is
not too time-consuming and that can be carried out by the library's own staff.
Introducing new statistical methods in an organisation is both a technical and a political
(or social) process. I assume the work will be carried out by a staff group, with a leader
or coordinator. Involving several (or many) staff members is recommended. But I start
with the technical part.
4.3. The floor plan
First you (plural) need a floor plan of the library. An architectural plan may be available -
otherwise make your own sketch of all areas open to the public. Patterns of activity will
differ from place to place inside the library. People will walk when they browse the stacks,
but sit when they try to study - usually. They will talk with friends in cozy corners, but
keep quiet in the main reading room - usually.
Divide the public area into zones that you believe have different patterns of use. Trace a
path on the plan that will take you through all zones. Observation takes place while you
walk at a moderate pace throughout the building.
4.4. Fifteen activities
Second you need to define the activities you want to register. Since you will be observing,
rather than interviewing, library users, you must stick to behavior that can be easily
observed. Sitting, standing and walking are visible activities. So is speaking and reading.
But you cannot - in general - tell which kind of book a person is reading without getting
Below I present a list of fifteen activities. There is nothing sacred about this particular list.
IBut I believe that library researchers tend to use too many ad hoc instruments. Most
professional library debate takes place within - rather than between - countries.
Therefore, standardisation is particularly useful within the national library communities.
The instrument below was made for Norwegian public libraries. It has been tried out in
two urban libraries - one in a town with 25 and one in a town with 60 thousand
inhabitants. I believe it fits current European settings, but it might need adaptation for
use in academic and special libraries. The fifteen categories are:
1. Contact with staff
Covers all direct contact with staff. Here we want to register activities where staff spens
time with the users, whether it involves speaking, writing, demonstrating or walking
Covers all visible waiting, whether in a proper line or not: waiting for staff, waiting for
access to equipment, toilet queues, aso.
3. Browses alone
Covers browsing or scanning of items on shelves while standing or walking around.
4. Walks or stands alone
Covers standing or walking around without browsing and without relating to library staff or
5. Sits alone
Sits alone without relating to media, to library staff or to other users.
6. Sits alone reading (or writing)
Sits and reads by her/himself. Includes individual work - reading and or writing - without
using ICT. Includes listening/viewing music or other media - without using ICT.
7. Sits alone with own (mobile) computer
Sits alone with active computer (screen on). Choose 7 if in doubt between 6 and 7.
8. Sits alone with library computer
Sits alone with active computer (screen on).
9. Browses in company
Participates in a group of two or more persons that browse or scan items on shelves
together while standing or walking around.
10. Walks or stands in company
Participates in a group of two or more persons that stands or walks around without
browsing and without relating to library staff.
11. Sits in a group without media
Participates in a group of two or more persons that does not relate to books or other media
or to library staff.
12. Sits in a group that uses media
Participates in a group of two or more persons that does relate to books or other media.
Does not include grouops with active PC.
13. Sits in a group with own (mobile) computer(s)
Participates in a group of two or more persons that is using one or more PCs of their own
14. Sits in a group with library computer(s)
Participates in a group of two or more persons that is using one or more library PCs (screen
15. Other activities
All activities not covered by 1-14.
4.5. Observation technique
When you walk around, you observe one zone at a time. In each zone you count the
number of persons involved in each of the fifteen activities - as you pass them. Write
down the numbers in a standard Observation Sheet with Zones on one and Activities on
the other axis.
4.6. Observation schedule
The observation tours take place during a sample of days (or hours) throughout several
weeks or months. Since activity patterns may be cyclical - on a daily, weekly and annual
basis, our data will be more reliable if the total period of observation is fairly long. A full
year is ideal, but a period of 3-6 months is probably adequate. After a bit of training and
experience, one observation tour - in a medium-sized library (50.000 volumes) - will
probably take 15-20 minutes. In small libraries, 5 to 10 minutes may be sufficient. In
large libraries, which may have several floors, it may take 30-45 minutes.
If you want to complete the observation project as fast as possible, I suggest the
following observation schedule:
Start on a suitable Monday. If the library opens at 8 am, do the first round at 8.30
am. Continue once an hour (starting on the half-hour) throughout the day.
On the second week, do the tours on Tuesday; on the third, on Wednesday; and
Continue until you have "two full weeks". This would take about three months.
Your observation data will be more valuable if they can be combined with other data from
the same sample of days. I therefore suggest that you register - at least - the number of
loans and the number of visitors during the specified counting days.
4.7. Data processing
Each observation round results in a hand-written Observation Sheet The data can be
processed manually or by spreadsheet. I advice processing the data quickly, for instance
at the end of each counting day. This means that you get some results immediately -
which is good for morale. Here I suggest one way of handling the process, illustrated by
a fictitious case..
Our imagined Maktaba library is open from 9 am till noon and from 2 till 6 pm on
weekdays. On Saturdays, it is only open in the morning. We start the Traffic Observation
Project on Monday, January 7 - and intend to observe library activities once a week for
the next three months. Our twelve observation days* are thus:
1. Monday, January 7
2. Tuesday, January 15
3. Wednesday, January 23
4. Thursday, January 31
5. Friday, February 7
6. Saturday, February 15
7. Monday, February 23
8. Tuesday, March 3
9. Wednesday, March 11
10. Thursday, March 19
11. Friday, March 27
12. Saturday, April 3
4.8. Once a week ...
At the end of each counting day (or early next morning), we
add up the numbers (make the sums) in each Observation Sheet
enter the activity sums in an Activity Sheet - and add up
enter the zone sums in a Zoning Sheet - and add up
The sums in the right-hand column of the Activity Sheet show you the pattern of
activities for the day as a whole. If you want to know the percentages, divide by the
The sums in the bottom row show you the number of observations for each hour during
the day. From a statistical point of view you may use the first sum as an estimate of how
many persons were inside the library - on the average - between 9 and 10 am. And so on
for the later sums.
The sums in the right-hand column of the Zoning Sheet show you the location of
activities - and hence of persons - for the day as a whole. This column tells you which
parts of the library are most and least used. If you want to know the percentages, divide
by the Grand Total.
Since the zones differ in extent, large numbers do not necessarily indicate overcrowding.
To interpret the numbers in that direction, you also need to look at the size and function
of the zone. Finally, add the numbers from the hourly Observation Sheets to make a Full
Day Observation Sheet.
4.9. Aggregating the data
At the end of our imaginary project, we have the following data:
One Observation Sheet (OS) for each observation round - 76 alltogether.
One Activity Sheet (AS) for each counting day - 12 sheets
One Zoning Sheet (ZS) for each counting day - 12 sheets
One Full Day Observation Sheet (FDOS) for each counting day - 12 sheets
We can now add all our data, creating
A Total Activity Table - which shows the typical distribution of activities in the
library during an average day
A Total Zoning Table - which shows the typical location of persons in the library
during an average day
A Total Observation Table - which sows the typical distribution of activities by
These Tables will probably
confirm many things you knew (or guessed) already - and
also reveal some things you and your staff did not know about the library
The advantage, in both cases, is that these data carry weight, since they are based on
systematic observation rather than intuition and opinion. Many opinions are perfectly
correct - but for decisions they need support. The "traffic information" may be useful for
planning and advocacy as it stands. But doing this just once, is not the best approach. If
we repeat the counting from time to time (every third year, perhaps?), we see how
usage patterns change.
If we use the information to change library layout and services - and repeat the exercise
afterwards, we can measure the impact of the changes. Then we are really entering the
virtuous circle of experiential learning: Routine - Measure - Evaluate - Improve - Measure
- Evaluate - Improve
... and so on to the stars - per ardua ad astra
* If any of these dates are unsuitable because of national holidays (Wikipedia ) or other
special events, they may be exchanged between different weeks. March 3 is the
Liberation Day of Bulgaria, so a Bulgarian library might switch weekdays between 7 and
8 - observing on Tuesday, February 24 and Monday, March 2, instead. Such switches do
not matter as long as they are done "robotically" - and not in order to influence the
The workshop organizer, Tord Høivik, is an associate professor in library and information
science at Oslo University College, Norway, where he teaches research methods, library
management and web-based reference work. He has an MA in mathematical statistics
from the University of Oslo (1969) and broad international experience as a sociologist
and peace researcher. From 2007 he represents Norway in the IFLA Statistics and
Evaluation Section. Høivik has written a graduate level textbook and numerous articles
on social science methods. International work includes two years as a visiting lecturer in
statistics at the Cooperative College, Moshi, Tanzania. Current projects include a study of
statistical indicators for Norwegian public libraries and statistical consultancy work for the
Norwegian Archive, Library and Museum Authority. Recent statistical publications in
From official statistics to effective strategies. In: Management, marketing and
promotion of library services based on statistics, analyses and evaluation / edited
by Trine Kolderup Flaten. München : Saur, 2006 (= IFLA publications).
Why do you ask? Reference statistics for library planning, Performance
measurement and metrics, vol. 4 (2003), no. 1, pp. 28-37.
He blogs regularly in Norwegian - as Plinius - and occasionally in English as Pliny the
Librarian. More info at: http://pliny.wordpress.com/about/