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Oberlin Group Library Statistics
This document discusses analyzing 6 years of statistics from the Oberlin Group of liberal arts colleges. It describes transforming the data, checking for outliers, testing correlations between variables, and building a regression model. It finds that high group attendance impacts reference transactions but not circulation. High gate count results in high circulation. Circulation depends on volume count. The document also explores predicting US News rankings based on library-specific data like expenditures, staff salaries, collections, and transactions.
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Oberlin Group Library Statistics
- 1. Claremont Colleges LibraryOberlinGroup StatisticsJohn McDonaldClaremont Colleges Library
- 2. Claremont Colleges LibraryWhatand whyCheck validity of the measures
- 3. Are we allmeasuring the same thing?
- 4. Test the reliabilityof the variables
- 5. Are we allmeasuring consistently from year to year?In order to:Refine our measures collecting process
- 6. Provide stronger definitionsof measuresClaremont Colleges LibraryOberlin Group Data6 years of statistics
- 7.
- 8. All 80 schoolsrepresented, though not all years for all schools
- 9. 66 relatively consistentvariables collectedClaremont Colleges LibraryTransform dataTransposed Excel spreadsheet
- 10.
- 11.
- 12. Add US NewsRanking for 2009
- 13.
- 14.
- 15. Claremont Colleges LibraryStatisticsLookfor Outliers
- 16. Test Rank ordercorrelation
- 17. Build regression modelClaremontColleges LibraryClaremont Colleges
- 18. Claremont Colleges LibraryAtlantaUniversity CenterClaremont Colleges
- 19. Claremont Colleges LibraryGroupTransactions (Attendance)Group Transactions
- 20. Claremont Colleges LibraryFacultyFTEUndergraduate FTE
- 21.
- 22. Claremont Colleges LibrarySmithCollegeCirculation (Faculty)SimmonsFaculty FTE
- 23. Claremont Colleges LibraryHowmany electronic subscriptions do you receive?Total Library Expenditures
Editor's Notes
- #4 66 variables that I could use – I added two more, year of survey and US News rank in 2009. Not all variables reported for each school each year.
- #10 Here’s a great correlation, with few outliers.
- #11 Example of a two highly correlated measures, as we would expect
- #12 This one is more concerning, because each of the 11 dots starting to separate themselves in 2008-09 are different schools.
- #13 Here’s an example of tight clustering, but with some concerns
- #14 Here’s a great example of a variable we clearly didn’t understand.
- #16 Shows slower adoption of ejournals – the rank order correlation from the previous year and following year is good, but two years later, it’s poor.