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EPSY6240_abstract

Page history last edited by Starr Hoffman 15 years ago

Research Project Abstract

 

Name: Starr Hoffman (Jennifer Hoffman)

 

Group: green

 

Research question: Is the presence of electronic reference services in academic libraries  affected by the number of employed librarians, the number of FTE students, and/or the total library expenditures?

 

Abstract:

Academic libraries are increasingly turning from traditional face-to-face and phone methods of reference to electronic formats, such as email and online chat.  However, although these technologies are cheap and able to be quickly implemented, not all academic libraries use them for reference services.  I wish to determine what, if any, effect that the size of professional staff, the number of FTE students at the university, and/or the total library expenditures have on the presence or absence of electronic reference services.

 

My dataset is derived from the NCES (National Center for Educational Statistics) Academic Survey of Libraries (ASL) for 2006.  I am limiting the study to institutions with graduate programs, either at the master's or doctoral level.  This parameter provides 855 subjects (academic libraries).  The variables will include: presence or absence of electronic reference services, number of librarians employed, Carnegie classification code, total FTE students, total library expenditures, and total library expenditures per FTE student.

 

I plan to analyze these variables using a multi-way ANCOVA.  This will enable me to determine the effects of number of librarians, number of FTE students, and total library expenditures on the presence or absence of electronic reference services.  The benefit of doing a multi-way ANCOVA is that I can measure the effect of these variables both separately and together.

 

Predictor variables:

  • Number of librarians (continuous) (NUMLIBRARIANS)
  • Number of students (continuous) (NUMSTUDENTS)
  • Expenditures (continuous) (EXPEND)
  • Expenditures per student (continuous) (EXPENDPER)
  • Carnegie classification (categorical) (CARNEGIE)
  • Presence or absence of distance learning program (categorical) (OPTIONAL--MAY BE ADDED LATER) (DISTANCE)
  • Time (year when data was recorded, for a decade) (TIME)

 

Response variable:

  • Presence or absence of electronic services (categorical) (ESERVICE)

 


try using an ANCOVA to calculate this....

 

  • model.lm(ESERVICE~DISTANCE*TIME)

 

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