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Satisfaction with Data Reuse: Survey Results from Users of a Social Science Data Archive
Society of American Archivists 2013 Research Forum
August 13, 2013
New Orleans, LA
Adam KriesbergPhD Candidate, University of Michigan School of Information
akriesbe [at] umich [dot] edu
Twitter: @DIPIR_Project
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• An Institute for Museum and Library Services (IMLS) funded project led by Dr. Ixchel Faniel and Dr. Elizabeth Yakel.
• Studying data reuse in three academic disciplines to identify how contextual information about the data that supports reuse can best be created and preserved.
• Focuses on research data produced and used by quantitative social scientists, archaeologists, and zoologists.
• The intended audiences of this project are researchers who use secondary data and the digital curators, digital repository managers, data center staff, and others who collect, manage, and store digital information. For more information, please visit http://www.dipir.org
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DIPIR Project
Nancy McGovernICPSR/MIT
Ixchel FanielOCLC
Research (PI)
Eric Kansa Open
Context
William Fink UM
Museum of Zoology
Elizabeth Yakel
University of Michigan (Co-PI)
The Research Team
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Methods Overview
ICSPR Open Context UMMZ
Phase 1: Project Start up
Interviews Staff
10 Winter 2011
4 Winter 2011
10 Spring 2011
Phase 2: Collecting and analyzing user data
Interviews data consumers
43 Winter 2012
22 Winter 2012
27 Fall 2012
Survey data consumers
2000 Summer 2012
Web analyticsdata consumers
Server logsOngoing
Observations data consumers
10Ongoing
Phase 3: Mapping significant properties as representation information
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ICPSR Survey Objective and Structure
Research Question:
What contributes to data reuse satisfaction and intention to continue using ICPSR data among users of the repository?
Survey Structure: 3 Sections
1. Critical (Data Reuse) Incident Section• Asked about perceptions of data quality• Dependent variable: Satisfaction with data reuse experience
2. Repository Section• Asked about experiences using ICPSR• Dependent variable: Intention to continue using ICPSR
3. Demographics
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ICPSR Survey Development and Administration
• Instrument Development• Adapted items from management and management
information systems literature (e.g. Flavian et al., 2006; Lee et al., 2002; Wang and Strong, 1996)
• Created new items
• Survey Sample• 1,632 first authors of journal articles from the ICPSR
Bibliography of Data-Related Literature 2008-2012
• Survey Administration (May – June 2012)• Approximate response rate 16% (usable surveys)
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The Data Reusers
have contributed data to ICPSR12%
feel there is sufficient data available for reuse in their field62%
mean length of time respondents have been reusing data in research13 years
of research conducted by respondents relies on data collected by others
66%
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Demographics: Data Sources Used During Research
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Demographics: Data Integrators
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Demographics: Job Titles
198 t
enure
track
facu
lty
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Respondents: Academic Disciplines
SociologyMedicine/Public Health
EconomicsCriminology/Criminal Justice
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A Closer Look: Satisfaction by Field
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Differences in Satisfaction with the Data Reuse Experience
Pairs of FieldsMean
DifferenceStd. Error Sig.
Criminology/Criminal Justice (5.81)
& Sociology (6.47)-0.66087* 0.18556 0.013
Criminology/Criminal Justice (5.81)
& Medicine/Public Health (6.39)-0.57118* 0.18174 0.048
ANOVA Pairwise Comparisons
(* = significant at p < 0.05)
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Scopus: Average Journal Ranking by Discipline
Academic Field Mean Ranking (SJR) nPsychology 1.52 16Education 1.46 3Political Science 1.56 5Economics 2.10 21Sociology 1.26 65Medicine/ Public Health (and allied fields) 1.48 71Criminology/ Criminal Justice 1.69 31Social Work 0.74 12Other 1.00 8Total 1.45 232
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Discussion
These results represent a small part a larger survey, but what can they tell us?
• Different research communities report differing levels of data reuse satisfaction
• 41% combined data from multiple datasets during research• Consistency is key to facilitate this type of work
• 66% collect their own data, but only 12% contribute to ICPSR• Where are these data? How can repositories identify these
datasets?
Next Steps:• Conduct regression analysis to assess main predictors driving
satisfaction with data reuse
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Acknowledgements
• Institute of Museum and Library Services • LG-06-10-0140-10
• PI: Ixchel Faniel, Ph.D., OCLC Research• Co-PI: Elizabeth Yakel, Ph.D., UMSI• Partners: Nancy McGovern, Ph.D. (MIT), Eric Kansa, Ph.D.
(Open Context), William Fink, Ph.D. (University of Michigan Museum of Zoology)
• Julianna Barrera-Gomez, OCLC Diversity Fellow• Students: Morgan Daniels, Rebecca Frank, Jessica
Schaengold, Gavin Strassel, Michele DeLia, Kathleen Fear, Mallory Hood, Molly Haig, Annelise Doll, Monique Lowe
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Questions?
Adam Kriesberg
akriesbe [at] umich [dot] edu
@adamkriesberg
@DIPIR_Project
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----- Additional Slides -----
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Demographics: Data Source for the Critical Incident
Source Frequency Percent
ICPSR only 80 31.9%
Other source(s) 52 20.7%
ICPSR and other source(s) 110 43.8%
Do not remember 9 3.6%
n=251
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A Closer Look: Satisfaction by Field
Field N Mean (Satisfaction)
Psychology 18 6.3611
Education 5 6.5000
Political Science 5 5.9000
Economics 22 6.3636
Sociology 66 6.4697
Medicine/Public Health (and allied fields)
75 6.3800
Criminology/Criminal Justice
34 5.8088
Social Work 13 6.3462
Other 8 5.8750
Total 246 6.2967
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