seating sweeps: a data collection tool for observing ... · a data collection tool for observing...

Seating Sweeps:A Data Collection Tool for

Observing Patron Behavior

Joe Fennewald & Victoria RaishKnowledge Commons, Penn State

PaLA 2015 Conference, State College, PA

Objectives for Session

• Observe how we designed and conducted our search study

• Understand the importance of ‘triangulation’

• Introduced to ‘seating sweeps’ as a tool

• Design your own initial ‘seating sweeps’ tool

• Collaborate and network

• Anticipate challenges and solutions

GOAL

At the end of this session you should leave with a TANGIBLE product that you can take back and build on for your own seating sweep document

Knowledge Commons

•An informal learning space in Penn State libraries

•Technology-equipped space

•Designed originally for undergraduates

•Research calls to understand how students are using space and how space is impacting student success

Tombros McWhirter Knowledge CommonsPenn State University Libraries

s2,2222ment

Library

statement

IT Services

statement

Multimedia

7,107 Contacts (Fall 2014)

2,016 (Fall 2010)

6,666 Contacts(Fall 2014)

4,050 (Fall 2010)

1,152 Contacts(Fall 2014)

375 (Fall 2010)

Group Study Rooms

statement

Computer Stations OBS

During Fall 2014, 22,903 room reservation requests. Of these, unable

to fill 1,443.

Students spend more time at computerswith larger monitors and work surfaces.

Over 4,300 videos made in one semester, 197 hours.

Reactions, comments, thoughts . . .

Reactions, comments, thoughts . . .

“There is really nowhere else we could have such a meeting. Given that we really try to have a quiet environment yet the privacy to discuss our drafts without disturbing other students who are studying.”

“The rooms give us privacy and freedom to have open conversations. I cannot think of a place on campus where we can have such virtual meetings with our clients.”

Head Knowledge Commons

What did we want to know?

• Why the Library? Wouldn’t this be as effective anywhere else on campus?

• We know a lot about the frequency of computer use but not what students are actually doing on the computers

• Do students behave differently when a computer lab is in a library?

• Are there temporal differences of student behavior?

Knowledge Commons, Penn State University Libraries Pollock Computer Lab, Penn State University

Renovated 2012Library, IT, Multimedia Service Desks24/561 Computers Food / drink allowed 14,916 Unique users (Fall 2014)

Renovated 2013IT, Multimedia Service Desks24/7172 ComputersFood / drink restrictions 13,933 Unique users (Fall 2014)

Research StudyData Collection Methods

‘Qu

ick’

Eth

no

grap

hy Seating

Sweeps / Field

Observations

Service Desks Transactions

Interviews / Survey

Computer Use / Google

Analytics

Triangulation

• Triangulation is a research tradition based in quantitative research

• It has also moved to the qualitative tradition to increase credibility and rigor of the study

• The importance is to collect multiple forms of data to provide diverse perspectives at a singular phenomenon

• Illuminate full understanding of that phenomenon

‘Quick ethnography’ Handwerker, 2001

Seating Sweeps

• Needed to see if there were OBSERVABLE differences in student possessions and activities in two informal learning spaces

• Intended to capture a ‘sweep’ of student behavior in a specific area

• Not intended for in-depth observation or field notes

• Should be supplemented with participant observation

Identified Relevant Space

Literature Review

Designed Seating Sweep

Created Walking Plan

Trained Research Team

Pilot Testing

Modified Seating Sweep

Arranged Times of Seating Sweep

Conducted First Sweep

Worked Out Issues

Finished, Compiled, Analyzed

DESIGN YOUR OWN DATA COLLECTION TOOLDYODTL

Collaborative Activity 1

• For our collaborative activity you will be working with your colleagues to choose the space you want to observe, what is the behavior you want to observe, and what are your expected outcomes

• Use the walking layout plan we provided to imagine how you may observe the particular space you are interested in

• Answer the questions following this plan with your colleagues

Collaborative Activity 2

• Design your own seating sweeps using the template provided

• Think about the possessions that users of your library typically have, the common activities you see, and what activities you may be interested in seeing

• The template is purposely BLANK as to not stifle your creativity with what we observed

• After the activity we will talk about what we observed, what worked, and what didn’t work

Recap

• Discuss people’s experiences of

– What spaces, activities, and outcomes you expect

– How the design of the seating sweeps went

– What activities you plan to focus on

Lessons Learned from Data Triangulation (the Researchers’ Perspective):

• To avoid drowning in the data always keep in mind the research questions.

• Stay organized. You will have an unbelievable amount of data before you know it and an organization system that each researcher is comfortable using is invaluable.

• Communicate effectively. You will likely have several people involved in data gathering and analysis. Each person brings different perspectives and have different understandings.

• Collaborate with people who have different skill sets and levels of expertise

• Train – people come with different background and experiences. One person may be comfortable interviewing but not observing. Train people in how to properly engage in these data collection methods.

Lessons Learned from Seating Sweeps

• Run pilot tests before the actual data collection

• Delete categories that do not work well for a ‘seating sweeps’ style data collection – IE – using scanners, talking to staff

• Capture data over a range of times and days to get a full understanding of user activity and behaviors in your space

• Input from seating sweeps participants at beginning

How to Anticipate and Succeed Through Challenges

• Plan Well

• Review literature to see how other people overcame issues

• Expect issues

• Ensure that data collection methods are answering your research questions

• Collect data with the understanding you are biased

Thank you and Contact Info

• Thank you very much for attending our presentation at PaLA

• We hope you have an enjoyable rest of your conference

• Contact Info

– Joe Fennewald, jaf23@psu.edu

– Victoria Raish, Victoria@psu.edu