Big picture in statistical frame – a statistical analysis and data visualization project of price change for electronic resources in academic libraries
| Pages | 12-16 |
| DOI | https://doi.org/10.1108/LHTN-09-2017-0071 |
| Date | 06 August 2018 |
| Published date | 06 August 2018 |
| Author | Cheng Cheng,Kevin Hayes,Kristy Lee,Jill Locascio,Colleen Lougen |
| Subject Matter | Library & information science,Librarianship/library management,Library technology,Library & information services |
Big picture in statistical frame – a statistical
analysis and data visualization project of price
change for electronic resources in
academic libraries
Cheng Cheng, Kevin Hayes, Kristy Lee, Jill Locascio and Colleen Lougen
Introduction
Today, most academic libraries are
spending a significant amount – if not
the majority – of their total expenditure
budget on electronic resources. While
they are high value assets for modern
libraries, the expensive annual
subscription cost and continuing price
increases also make them a major
budgeting burden. This makes it essential
to have a clearer statistical view of the
price-changing trends and patterns of the
E-resources and vendors. Meanwhile, it is
a reasonable assumption that there are
certain causes and mechanism, which will
trigger the price changes of certain
products in the market. Therefore, in early
2016, librarians from different State
University of New York (SUNY)
institutions decided to conduct a
statistical analysis and data visualization
project focused on such issues. There are
three primary objectives of this project:
analyze and visualize the price-changing
pattern of common electronic resources;
provide predictions for future price
changes at the vendor level; and discover
any potential causes of such price
changes. In addition to these primary
objectives, this project also made use of
skills and techniques for statistical
analysis and data visualization.
Methodology, tools and data cleaning
There were more than 80
aggregating databases with consistent
payment records from 2010 to 2015
selected for the initial analysis. No
electronic serials were involved, so the
team could ensure the scale of the
project would remain at a practical
level. Resources that were one-time
purchases or had annual access fees
were also not included. A senior student
from the Department of Statistics at
SUNY Oneonta was hired as an intern
for the project to process the data.
Microsoft Excel and IBM SPSS were
used as tools for statistical analysis and
data visualization. Excel was used to
sort, calculate and analyze the
percentage, average and median
increases of selected E-resources by
vendors, and visualize these data into
charts and graphics. After the inputted
expenditure data were retrieved from
payment records from the ILS, graphs
of average and median cost of selected
titles, as well as the percent increase
over the years, were created (Figures 1
and 2).
After the initial review of data, four
titles with values deviated far away
from the rest were considered as outliers
and eliminated. In addition to
eliminating outliers, the entire set of
data from 2010 was also eliminated, as
many titles were either not part of
collection or had incomplete payment
records. Figures 3 and 4with cleaned
data can be seen.
SPSS was used to compared prices
and usages of selected resources and
provide predictions for future price
changes, which was evaluated by their
correlation to existing expenditure
statistics from 2010 to 2015, which can
be seen in Figures 5 and 6.
Discoveries
Figures 1 and 2and then Figures 3
and 4(both before and after data
cleaning) show there is a consistent
price increase since 2011. The price
increases in 2014 and 2015 were fierce,
but the projectile (Figures 5 and 6)
based on past statistics suggests, in 2017
and 2018, that it may be relatively
moderate, which is likely a sign that this
price increase cycle is approaching its
cap. The model for future price
predication is based on the expenditure
data from the previous years, and that
value varies based on vendor. It is
important to note that the further into
the future the prediction, the less
accurate it is. It is also important to note
that compared to the enormous size of
the aggregating databases’ market with
numerous products available, this
project only covered a very small facet
of it (Figure 7).
Comparing the price-changing
trends of electronic resources after 2011
(Figures 1 to 4) and the picture of the
general economy (Figures 5 and 6)
during the same period, it is very
obvious that the pricing trend has been
influenced by the national economy
trend – the high percentage of price
increase during 2014/2015 correlated to
the steep value increase of the US dollar
during the same period, and the
relatively moderate price increase of
electronic resources since 2015 could
also refer to relatively moderate value
change of the US dollar.
The connection between usage and
cost is less obvious, although resources
with higher usage tend to cost more, as
shown by Figures 5 and 6, probably
because to vendors, high usage
indicates high demand. Most major
vendors do not publish detailed revenue
reports, as they are privately owned.
Hence there is a lack of information to
determine causes for price changes
other than national economic picture
and the demand.
12 LIBRARY HITECH NEWS Number 6 2018, pp. 12-16, V
CEmerald Publishing Limited, 0741-9058, DOI 10.1108/LHTN-09-2017-0071
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