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Combining Web Analytics and a Web-Based User Survey for LibGuides
Assessment

Carmen Howard, MSLS*a, Jung Mi Scoulas, PhDb, Allan Berry, MSLIS, MAc, Deborah L.
Lauseng, AMLSd

aSenior Instructor and Regional Health Sciences Librarian, Library of the Health Sciences –
Peoria, University of Illinois Chicago, Peoria, Illinois,
https://www.orcid.org/0000-0002-3460-5417 , choward4@uic.edu

bAssociate Professor and Assessment Coordinator, University Library, University of Illinois
Chicago, Chicago, Illinois, https://www.orcid.org/0000-0002-7536-295X , jscoul2@uic.edu

cClinical Assistant Professor and Web Services Librarian, University Library, University of
Illinois Chicago, Chicago, Illinois, https://www.orcid.org/0000-0002-1012-8601 ,
aberry3@uic.edu

dAssociate Professor and Regional Head Librarian, Library of the Health Sciences – Peoria,
University of Illinois Chicago, Peoria, Illinois,
https://www.orcid.org/0000-0001-9067-2932 , dlauseng@uic.edu

Cite as: Howard C, Scoulas JM, Berry A, and Lauseng DL. Combining Web Analytics and a
Web-Based User Survey for LibGuides Assessment. Hypothesis. 2024;36(2).
doi:10.18060/27669

Howard, Scoulas, Berry, Lauseng. All works in Hypothesis are licensed
under a CC BY-NC 4.0 DEED Attribution-NonCommercial 4.0 International. Authors own
copyright of their articles appearing in Hypothesis. Readers may copy articles without
permission of the copyright owner(s), as long as the author(s) are acknowledged in the copy,
and the copy is used for educational, not-for-profit purposes. For any other use of articles,
please contact the copyright owner(s).

1

https://www.orcid.org/0000-0002-3460-5417
https://orcid.org/
mailto:choward4@uic.edu
https://www.orcid.org/0000-0002-7536-295X
https://orcid.org/
mailto:jscoul2@uic.edu
https://www.orcid.org/0000-0002-1012-8601
https://orcid.org/
mailto:aberry3@uic.edu
https://www.orcid.org/0000-0001-9067-2932
https://orcid.org/
mailto:dlauseng@uic.edu
https://creativecommons.org/licenses/by-nc/4.0/


Peer Reviewed Article Hypothesis, Vol. 36, No. 2, 2024

Abstract

Objective: The aim of this method is to assess an online library guide, using both qualitative
and quantitative methods, with the specific objective of discovering who is using the guide
and why, as well as understanding the impact of the guide on user learning outcomes.

Approach: This method combines the use of web/data analytics (quantitative) and a
web-based survey (quantitative and qualitative) with an existing model for measuring learning
outcomes. By combining these methods, a more robust assessment of an online guide can be
achieved.

Data Type Used: The discussed online guide assessment used data from Google Analytics
and from a web-based survey, created in Qualtrics, with learning outcome questions
developed using the Project Outcome for Academic Libraries Toolkit.

Strengths: The combined analysis of the web analytics and survey data resulted in an
expanded assessment of the library guide and a richer picture of guide users, their behavior,
and their learning outcomes.

Limitations: Individually, each method used in this research has limitations. Web analytics
are unable to determine users’ motivation (why) for using the guide and whether they have
learned (impact) from their behavior/interaction while using the guide. Surveys collect
self-reported data and may be subject to recall bias. However, when the methods are
combined, each helps to counteract the individual limitations.

Overview
Library guides, a type of library-created online resource that is frequently built using
Springshare’s LibGuides, are labor-intensive to maintain over time. For this reason, many
libraries seek to assess their guides’ use and effectiveness. Most of these assessments focus on
quantitative data, whereas a few other studies have used qualitative methods, and very few
have used a combination of methods.1,2 While each method has its own strengths, Griffin and
Taylor highlighted the need to incorporate qualitative data with quantitative usage data and
advocated for “using multiple data sources and triangulating findings between data points”.1

Analytics data provides a picture of actual user behavior through tracking and reporting traffic
patterns with detailed aggregate statistics. Because it tracks every interaction, analytics data
eliminates recall bias. However, interaction tracking is not always as complete and
informative as might be expected. Although web analytics are objective in how data is
gathered, the choice of what data to gather is not. Tracking movement on a website also
cannot reveal user motivations, if they found what they were looking for, or whether they
learned anything from the interaction.

Surveys provide a structured format for collecting information on various topics of interest,
including demographics, behaviors, and attitudes. They have the potential to reach a range of
patrons and ask them questions to better understand their motivations, perceptions, and
experiences. Although they have many advantages, surveys also come with limitations. For

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Peer Reviewed Article Hypothesis, Vol. 36, No. 2, 2024

example, they rely on self-reported data provided by individuals who voluntarily participate,
which may introduce biases.3 Survey fatigue can occur due to the large volume of survey
requests, potentially impacting response rates. In addition, the extensive nature of some
surveys with multiple questions can discourage participants from dedicating the time
necessary to complete them.

Our research team sought to assess the University of Illinois Chicago (UIC) Library’s
Evidence Based Medicine (EBM) LibGuides pages (our guide) and plan for the future of our
guide using data (IRB Protocol #2019-0449). Given higher-than-anticipated usage statistics
and multiple external requests to reuse content, our research team speculated that our guide
was being used more broadly than initially intended. We aimed to understand user behavior,
both what they did and why they did it, and to measure the impact of our guide on user
learning experience and outcomes.4 Additionally, we sought to provide evidence for
transitioning our guide to a fully Open Educational Resource (OER).4 Therefore, we
developed an assessment approach employing two complementary methods used
concurrently: quantitative usage data and a web-based user survey.

Our research team initially reviewed the usage statistics integrated into LibGuides; however,
we found the data limited to high-level analysis, such as page views, monthly trends of use,
etc. Given our interest in a deeper understanding of users and their use patterns, we
determined that Google Analytics (GA) would provide a more robust data set. We also
realized that our questions could not be fully answered by analytics and that we would need to
obtain users’ input through a survey. Our interest in measuring educational outcomes led us to
the Project Outcome for Academic Libraries Toolkit, hereafter referred to as the Project
Outcome Model.5 The Project Outcome Model is a free online toolkit developed to measure
the impact of library programs and services. We adapted questions from the Project Outcome
Model into our survey to gain insights into user knowledge and confidence.

The combination of two methods, web/data analytics and a web-based survey, provides a
more robust approach in the assessment of online guides. This combination gave our research
team a richer understanding of our users, their behavior, and their perceptions of learning
outcomes. For librarians interested in expanding their evaluation of library guides or other
webpages, this article focuses on our process and the strengths and limitations of each method
and tool we used, as well as their combination. A summary of those strengths and limitations
can be found in Table 1 following the discussion. For a brief review of the literature on
evaluating library guides and a discussion of our research findings, please see our team’s
previously published work.4

Example with Discussion
UIC’s EBM (Evidence Based Medicine) Guide (https://researchguides.uic.edu/ebm) is a
subject guide that introduces the basic concepts in EBM with pages such as “Levels of
Evidence” and “Asking Clinical Questions.” Additionally, our guide covers higher level
concepts like “Clinical Filters” and “Appraisal.” Our guide was initially created for students in
the UIC College of Medicine and for non-UIC students in an online course for librarians.
However, integrated tracking statistics indicated that it was being used by far more people
than this limited pool of intended students. Our research team needed to analyze user
behavior to determine who was using our guide and why, so we looked at what methods

3

https://researchguides.uic.edu/ebm


Peer Reviewed Article Hypothesis, Vol. 36, No. 2, 2024

would assist us in this specific need. We also found that some pages were used at significantly
higher rates, which led us to develop survey questions about individual page use and to use
analytics data to look specifically at page traffic.

At the beginning of our research project, we considered a few different methods and tools. As
the project progressed, we discovered several strengths and limitations of the methods and
tools that we chose. See Table 1 for a summary.

Quantitative Method - Web/Data Analytics
Our research team chose to look at web analytics data because we believed it could answer
questions about how our users interacted with our guide - the pages they visited, how long
they stayed, and how they were interacting with the pages. We also knew that it could inform
us about the devices used, how users found our guide, and their general geographic location.
Analytics data can be obtained from various tools. Our research team considered and/or used
the following tools described below.

Springshare’s integrated "Guide Tracking"
Our research team initially used the integrated statistics from Springshare’s LibGuides
primarily because this is the software used to develop our guide. While this was helpful in
seeing counts of views per page and monthly trends in overall use, the data is only available at
a high level. Although Springshare does have additional tracking features, we needed a more
comprehensive, granular, look at how the site was used.

Google Analytics (GA)
Our research team decided to move to GA because GA was currently being used by the
library and is widely regarded as an industry standard.6,7 It is a clear market leader8,9 and
considered a required skill for entry-level marketing analytics professionals.10 GA met our
need to see how users interacted with our guide. During our research, we used the latest
version available of GA called “Universal Analytics” (UA). Google has subsequently released
a major update to the GA system, named “Google Analytics 4” (GA4).

GA provides rich data for user behavior analysis. We were specifically interested in the
following quantitative information from GA:

• how users were finding our guide

• how they moved between guide pages

• how they used specific pages (most-visited pages and average time on a page)

• their general geographic location, such as country and city (we did not collect specific
location data, such as street or ip addresses)

• the type of device they were using to access the page.

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Peer Reviewed Article Hypothesis, Vol. 36, No. 2, 2024

The first issue we encountered involved URLs and GA reporting. We configured GA to only
return data about our guide itself. Our guide consists of 10 total URLs: three unique URLs for
the home page and one unique URL for each of seven additional pages. We elected to remove
URLs with third party parameters for simplicity; for example, Facebook or Google Translate,
would sometimes add proprietary URL parameters like “translate.googleusercontent.
com/translate_c?depth=1&hl=ar&prev=search&rurl=translate.google.com&sl=en&sp=nmt4&u=”
to the beginning of our URLs and for their own purposes of user tracking. Accounting for
these additional URLs significantly increased the complexity of our report data. Most of these
modified URLs, while presumably representing legitimate user requests, were each only
accessed during a single session. Therefore, we explicitly excluded these proprietary URLs by
filtering out all traffic except to our expected 10 URLs. These 10 URLs covered 99.6% of all
unique users from the full GA pool during the research time-period. No excluded URL
represented more than 0.05% of our guide’s unique users. The added simplicity seemed worth
the small cost in comprehensiveness.

Additionally, our research team learned the importance of understanding the way that GA
data points are calculated. Initially, we anticipated using “bounce rate” and “session duration”
to inform our understanding of user behaviors. However, upon further investigation into our
guide’s specific data, all UIC guides data, and a deeper review of GA processing, we
determined that these metrics did not reveal much about the use of educational subject guides.

When a user visits a single page without further interaction, GA considers this a "bounce."11

Our guide had a large proportion of "bounces" (86% of sessions). For example, consider a
student completing a course assignment. They might spend 10 minutes using a guide page,
afterward closing their browser without further navigation. Most librarians would call this a
success. However, since “session duration” is calculated by time-stamping each action that a
user takes (for example, clicking a link), GA counts this as a "bounce", with a “session
duration” of zero seconds.11, 12 Sessions can also end automatically after an arbitrary time
expires without any actions.12

Google documentation acknowledges that a high bounce rate is only problematic if you are
trying to direct people to another page,11 such as when your home page is only a waypoint on
the way to a product page. Thus, while "bounces" were considered an important metric in
UA, they were later de-emphasized in GA4 in favor of other engagement metrics. In the case
of our guide, even the home page is an educational page, not meant to direct users to
subsequent pages, so high “bounce rates” were not a concern for us. For these reasons, our
research team found “average time on page” to be more informative in evaluating the success
of our guide than either “session duration” or “bounce rate.”

As we learned more about GA, we belatedly realized additional features could have been
activated prior to our research study, which may have provided even deeper insights. One
example is “scroll depth,” or how far a given user scrolls down a page, which can be a proxy
for user engagement. However, tracking data for “scroll depth” in UA required activation
before data collection; at the outset of our project, we did not consider it relevant. Later, it
seemed problematic to alter our data collection midstream. GA4 provides this feature
automatically, which should provide additional rich data for future research.

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Peer Reviewed Article Hypothesis, Vol. 36, No. 2, 2024

Quantitative & Qualitative Method - Web Survey
While GA offers insights into general user patterns, it does not provide information on user
motivation and reasoning or assess the impact of their usage on learning outcomes. So, in
addition to the GA data, our research team developed a survey to gather information from
users regarding their usage of our guide and the extent to which they achieved their learning
objectives. In the survey, we collected various information, including demographics, user
behavior, future referral intentions, user perceptions, satisfaction levels, and user learning
outcomes in terms of knowledge and confidence. Multiple survey tools and models are
available. Our research team adapted the Project Outcome Model to assess learning outcomes
by using a survey administered through Qualtrics software.

Project Outcome Model
To measure users’ learning outcomes, we utilized two questions from the Project Outcome
Model.5 This commonly used model is based on a series of self-assessment learning
statements and uses a Likert scale. We asked questions related to the user’s change in
knowledge and confidence. For example, we adapted this question: “After using the EBM
Guide, I feel more confident about my ability in each of the following areas.”4,5 The Project
Outcome Model allowed us to gain insights into users’ learning without asking them to take a
pre- and post-test of their knowledge. However, we acknowledge that survey responses were
based on self-perception and that the question formatting used in the Project Outcome Model
has been noted4 to have the potential of acquiescence bias, or the “tendency for the
interviewee to agree with the questioner.”13

Qualtrics
Qualtrics is a web survey platform used to create and distribute surveys. Additional web
survey tools are noted in Table 1. Our research team chose Qualtrics as our survey tool due to
its many useful features and its availability at our institution. It enables researchers to gather,
analyze, and report data effectively, with features to enhance data security.14 However, it is
important to note that there are financial costs associated with using advanced features and
conducting surveys with larger sample sizes. This may pose accessibility and affordability
challenges for survey designers operating on limited budgets. Additionally, certain features of
Qualtrics may have a learning curve, particularly when it comes to coding scales and setting
up surveys as originally intended. This learning process is crucial for ensuring accurate data
analysis and utilizing the platform to its fullest potential.

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Peer Reviewed Article Hypothesis, Vol. 36, No. 2, 2024

Table 1. Strengths and limitations of methods and tools

Analytics Survey

Product
(Platform)

Springshare
LibGuides
Analytics

Google Analytics Project Outcome
Model

Qualtrics
(Platform)

Research
questions

This data did not
answer our
research questions
but provided an
overview of guide
usage.

1. Who are the
users?
(demographics
such as geographic
location and
device used.)
2. How are users
interacting with
our guide?

1. What learning
outcomes are
achieved from the
users’
perspectives?

1. Who are the
users? (detailed
demographics such
as educational level
and profession.)
2. What are the
users’ motivations?

Method:
Strengths

1. Comprehensive data
2. Actual use patterns/behaviors
3. Automated data after set-up
4. Eliminates recall bias

1. Qualitative data
2. Motivational data
3. Question structure flexibility
allowing integration of existing
questions from the Project Outcome
Model or other models5.

Tool:
Strengths

1. Easy to use
2. Native to the
webtool/publishing
platform
3. Ability to
compare with our
other guides

1. Cost-effective
2. Detailed
statistics regarding
traffic patterns and
use
3. Industry
standard

1. Provides
existing questions
related to learning
outcomes

1. Comprehensive
options
2. Analysis and
reporting features
3. Enhanced data
security features

Method:
Limitations

1. Unable to understand users’
motivation (why) and whether they
have learned (impact)
2. Unable to collect certain
demographic info (i.e. - educational
level)
3. Possible user privacy concerns

1. Self-reported data which may be
skewed due to self-selected
participation
2. May be subject to recall bias
3. Survey fatigue

Tool:
Limitations 1. Limited data points

1. Data analysis
can be
complicated with
proprietary URLs
2. Data point
calculations (for
example, “bounce
rate”) may not be
intuitive

1. May be subject
to acquiescence
bias

1. Financial costs to
access advanced
features
2. May have
learning curve for
sophisticated
features

Methods:
Potential
alternatives

1. Quantitative Survey
1. Focus Groups
2. Semi-structured Interviews
3. Usability Testing

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Peer Reviewed Article Hypothesis, Vol. 36, No. 2, 2024

Tools:
Potential
alternatives

1. Adobe
Analytics
2. Amplitude
3. Cloudflare Web
Analytics
4. Matomo
Analytics
5. Mixpanel
6. Other options
are available

1. Several models
are available
depending on your
research topic

1. Email distribution
2. Google forms
3. LibWizard
4. Polling
5. Survey Monkey
6. Other options
are available

Conclusions
Our research team found that the use of both quantitative and qualitative data provided a
richer picture of our users, their behavior, and their learning outcomes. We suggest that GA
data and survey data each have their own strengths. When combined, these methods are
complementary; each helps to counteract the limitations inherent in the other. By combining
methods to include both web/data analytics and a web-based survey that incorporates the
Project Outcome Model, we were able to assess LibGuides pages more robustly and develop
an expanded methodological assessment approach.

The strength of the combined method can be seen in a variety of ways. First, GA kept the
survey shorter. Because GA could easily answer some of our questions, we did not have to
ask users about their navigation of the site or about the devices they used. Keeping surveys
shorter helps to address survey fatigue.

Second, an anonymous web-based survey can provide insights to many complex research
questions like understanding user motivation and outcomes. For example, although GA could
tell us if a user was coming to our site from an educational site, like a course management
system, GA cannot provide further insight into educational uses or outcomes. Using the
Project Outcome Model to measure changes in user confidence and their perception of
increased knowledge has provided a clearer understanding of user learning. The learning
outcome data, which showed an increase in self-reported knowledge and confidence, was used
to justify expanded work on our guide. For detailed results and a discussion of our findings,
please see our previously published article.4

Finally, the combination of the two methods removed the uncertainty of some GA data points
and increased our confidence overall that the survey participants were representative of all
users. Our research team included questions in the survey to look for data agreement or lack
of agreement with data gathered by GA. For example, in terms of geographic distribution, a
brief examination of GA data in preparation for our research revealed that most users were not
coming from the Chicago area. This could be an indication that they might not be UIC
affiliates, although many UIC students and faculty do attend and teach courses remotely. This
data point led us to include a question on the survey about UIC affiliation. When we ran the
survey and looked at concurrent GA data, we confirmed that most users were not associated
with UIC. Another example, where we included a question to look for data agreement but did
not find agreement, was in comparing how users came to our guide. GA reported much higher

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Peer Reviewed Article Hypothesis, Vol. 36, No. 2, 2024

levels of discovery by an organic search than users self-reported in the survey. Although it
was extra work and our results were somewhat mixed, we felt this effort to look for data
agreement was worth the investment for our overall increased confidence in the results.4

Our assessment was enhanced by carefully examining the strengths and limitations of each
method and tool used to address our research questions. Further, our assessment was
strengthened through a combination of methods and tools. The most important characteristic
in this type of assessment is the combination of quantitative data with a qualitative
component. Our research team proposes that this combined method could be used by others
to evaluate not just LibGuides pages but other types of web resources. Alternative tools can
be used in implementing this proposed method. For example, if your institution/organization
uses primarily Google Forms to gather feedback from your users, this tool could be used
instead of Qualtrics. If you want to explore challenges or behaviors for non-users or frequent
users in depth, conducting focus groups could be more appropriate. Librarians and
researchers simply need to examine the purpose of their project and the most appropriate
quantitative and qualitative methods and tools available to address their questions.

Resources
Web Content

• Project Outcome for Academic Librarians Toolkit:
https://acrl.projectoutcome.org/about

• Google. Analytics Help. Glossary (GA4):
https://support.google.com/analytics/topic/9355633?hl=en&ref_topic=9143232&sjid=-
95027 3908224713767-NA

• The Ultimate Google Analytics Glossary (2023 edition):
https://www.lovesdata.com/blog/google-analytics-glossary

Further Reading

• Connaway LS, Radford ML. Survey research and the questionnaire. In: Research
methods in library and information science. 7th ed. Santa Barbara, Calif.: Libraries
Unlimited; 2021. p. 129-168.

• Farney T. Getting the best Google Analytics data for your library. Libr Technol Rep.
[Internet] 2016;52(7):5-8. Available from: https://doi.org/10.5860/ltr.52n7

• Watson CA. Can an understanding of data use in academic libraries assist in public
service efforts? Public Serv Q. [Internet] 2021;17(1):59-64. Available from:
https://doi.org/10.1080/15228959.2020.1860861

9

https://acrl.projectoutcome.org/about
https://support.google.com/analytics/topic/9355633?hl=en&ref_topic=9143232&sjid=950273908224713767-NA 
https://support.google.com/analytics/topic/9355633?hl=en&ref_topic=9143232&sjid=950273908224713767-NA 
https://www.lovesdata.com/blog/google-analytics-glossary
https://doi.org/10.5860/ltr.52n7 
https://doi.org/10.1080/15228959.2020.1860861 


Peer Reviewed Article Hypothesis, Vol. 36, No. 2, 2024

CRediT
Carmen Howard: Conceptualization, Methodology, Investigation, Resources, Writing -
Original Draft, Writing - Review & Editing Jung Mi Scoulas: Conceptualization,
Methodology, Formal analysis, Investigation, Resources, Data Curation, Writing - Original
Draft, Writing - Review & Editing Allan Berry: Conceptualization, Methodology,
Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing
Deborah L. Lauseng: Conceptualization, Methodology, Investigation, Resources, Data
Curation, Writing - Original Draft, Writing - Review & Editing

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https://web.archive.org/web/20231226204029/https://support.google.com/analytics/answer/2731565?hl=en
https://www.oxfordreference.com/view/10.1093/acref/9780199679188.001.0001/acref-9780199679188-e-1783
https://www.oxfordreference.com/view/10.1093/acref/9780199679188.001.0001/acref-9780199679188-e-1783
https://www.qualtrics.com/platform/security/

