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Information Seeking Behavior of Scientists

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Develop survey instrument and interview ... PHP Surveyor used for web based survey. Other common choice at our school for simpler surveys is Survey Monkey. ... – PowerPoint PPT presentation

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Title: Information Seeking Behavior of Scientists


1
Information Seeking Behavior of Scientists
  • Brad Hemminger
  • bmh_at_ils.unc.edu
  • School of Information and Library Science
  • University of North Carolina at Chapel Hill

2
How to Study Information Seeking Behavior of
Scientists?
  • Survey
  • Reach many people
  • Address common questions
  • Produce lots of feedback for libraries
  • Quantitative, models of variance (positivist
    approach)
  • Interviews
  • In depth coverage of selected groups
    (bioinformatics)
  • Use grounded theory and critical incident
    techniques to capture more qualitative,
    contextual experiences
  • Develop models of information processing and use

3
Survey--Long Term Plan
  • Conduct initial (pilot) study at UNC.
  • Develop survey instrument and interview
    methodologies.
  • Based on analysis of results of the pilot study
    draft national version, and then get feedback
    from national sites.
  • Run national study. Setup so that other sites
    only have to recruit subjects the entire survey
    runs off of UNC website. Hopefully this results
    in large number of sites and participants for
    minimal experimental costs.

4
Survey Sampling Technique
  • Census
  • Need to be able to reach all members
  • Best if can get response from large segment of
    population
  • Results in potentially more input from wider
    audiences, especially when having comment type,
    open ended questions
  • Subject to bias
  • Random sample
  • Statistically, generally a better choice
  • Higher cost due to follow-up
  • Would require significantly more work for our
    design were we expressly do not record any
    identifying info

5
Questions
  • Questions were based on
  • Prior studies we wished to correlate our results
    with. This is facilitated by authors who have
    published their surveys (in papers as appendix,
    e.g. Brown), and especially to folks who have put
    theirs collections of surveys online, e.g.
    Tenopir). Consider national resource!
  • Covering issues that our librarians and LIS
    faculty were concerned about
  • Developed during several drafts and review by
    representatives from all libraries on campus.

6
Survey Instrument Choices
  • Paper
  • Phone
  • Email
  • Web-based. While these can require more effort
    than anticipated, if the number of survey
    respondents is over several hundred it is
    generally more cost effective. This seemed the
    best choice since our pilot survey was of several
    thousand subjects, and our national survey was
    planned for tens of thousands. Since we have web
    and database expertise we were able to automate
    the process with minimal startup costs.
  • Schonlau 2001, Conducting Research Surveys
    via E-mail and the Web.

7
Technical Details
  • PHP Surveyor used for web based survey. Other
    common choice at our school for simpler surveys
    is Survey Monkey. PHP Surveyor allowed us to ask
    multi-part questions, and to constrain answers to
    specific format responses.
  • PHP Surveyor dumps data directly into mysql
    database.
  • Data is cleaned up, then feed into SAS for
    analysis.

8
Contact and Follow-up
  • Subjects are university faculty, grad students
    and research staff.
  • We approached all science department chairs to
    get support first.
  • Contact
  • Initial contact was by email giving motivation
    for study, indication of support by deptscampus,
    and link to web-based survey.
  • Follow-ups by letter, then two emails
  • Flyers in department

9
Analysis
  • For the quantitative response variables standard
    descriptive statistics (mean, min, max, standard
    deviation) are computed, and histograms are used
    to visualize the distribution.
  • Categorical variables are reported as counts and
    percentages for each category, and displayed as
    frequency tables.

10
Analysis Correlations
  • Categorical vs Categorical
  • Chi-square
  • Categorical vs Quantitative
  • Analysis of Variance
  • Quantitative vs Quantitative
  • Correlation
  • Examples are by dept analysis of other features
    age vs preferred interface (Google or Library)

11
Look at Survey
  • Review questions
  • Cover examples of where we learned better ways to
    do things
  • Some hot of the press results

12
Wrap-up and Promotional Message
  • Information about national survey
  • We will hold an open meeting about the national
    survey immediately following this session, just
    outside
  • We are still recruiting sites
  • Any and all sites are welcome. Entire survey run
    from UNC sites only need to recruit
    participants.
  • For more information see the protocol and study
    announcement document
  • http//ils.unc.edu/bmh/isb/UNC_survey_announcemen
    t.htm
  • or
  • bmh_at_ils.unc.edu
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