Analyzing Data, Getting Results: Making it All Make Sense

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Riley, Jenn. "Analyzing Data, Getting Results: Making it All Make Sense." Statewide California Electronic Library Consortium (SCELC) Research Day, March 5, 2013.

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Analyzing Data, Getting Results: Making it All Make Sense

  1. 1. Analyzing Data, Getting Results Making it All Make Sense Jenn Riley University of North Carolina at Chapel Hill
  2. 2. 3/5/13SCELCResearchDay 2 Evidence-driven decisions are a powerful guide for library operations.
  3. 3. “The plural of anecdote is not data.” After a quote with the opposite meaning, by Raymond Wolfiger. 3/5/13SCELCResearchDay 3 Sometimes attributed to Frank Kotsonis.
  4. 4. “There are three kinds of lies – lies, damned lies, and statistics.” Mark Twain, perhaps after Benjamin Disraeli. 3/5/13SCELCResearchDay 4
  5. 5. Using data for planning library operations 3/5/13SCELCResearchDay 5 Existence/hours of service points Materials to buy/license/accept/ digitize/keep/preser ve Designing web sites and other online resources Effectiveness of/satisfaction with procedures/services Evaluating a pilot service or project Projecting future expenditures
  6. 6. Both cost and value are key ALCTS Heads of Technical Services in Large Research Libraries Interest Group, Task Force on Cost/Value Assessment of Bibliographic Control (2010) Proposes definitions of value for cataloging: 3/5/13SCELCResearchDay 6 Discovery success Use Display understanding Data interoperability Support for FRBR user tasks Throughput/timeliness Support administrative goals
  7. 7. Example studies •By Joyce Chapman, then at North Carolina State University • Benefits of manually enhanced metadata for images • Comparing effort to utility for specific EAD elements 3/5/13SCELCResearchDay 7 See Chapman, Joyce. “Metrics & Management: Cost & value of metadata workflows.” SAA 2011. http://www.academia.edu/1708422/Return_on_Investment_Metadata_met rics_and_management
  8. 8. Some common analyses 3/5/13SCELCResearchDay 8 Cost per unit produced Change over time Error/problem rate Predicting impact of a change Identifying unmet needs
  9. 9. Back to library scenarios 3/5/13SCELCResearchDay 9
  10. 10. Existence/hours of service points • Who is using what and when? • How can we most effectively staff them? • Costs • Staff time • Facilities management costs • Benefits • Number and type of visitors, and how they use it • Service transactions completed • Specific services used at the location • Other data to collect • Usage by time of day • Calculate cost per transaction 3/5/13SCELCResearchDay 10
  11. 11. Materials to buy/license/accept/digitize/ keep/preserve • Should we acquire, make more accessible, or keep this? • Costs • Initial purchase/license • Ongoing license/maintenance • Staff for cataloging/processing/digitizing/ingesting/preserving • Software • Hardware/storage • Benefits • Current and predicted future use • Opportunity for transformative use 3/5/13SCELCResearchDay 11
  12. 12. Evaluating a pilot service or project • Is the cost/benefit ratio appropriate? • What is the raw cost? • But it’s not all about cost/benefit: • Is the pilot achieving its aims? • Does this [whatever] do what we thought it would? • What collateral effects will it have? • Were the assumptions we made correct? • Data collection will be varied for this task 3/5/13SCELCResearchDay 12
  13. 13. Designing web sites and other online resources • A/B testing • User-centered design • Satisfaction surveys with previous iterations, similar sites, or prototypes • Web stats for previous iterations or similar sites • Task-based usability testing • Don’t forget the cost of sustaining it once you have it up! 3/5/13SCELCResearchDay 13
  14. 14. Effectiveness of/satisfaction with procedures/services • What parts of our current service are users most and least happy about? • What are the ineffieciences in our procedure for [whatever]? • Some data collection ideas • User surveys • Ratio of potential to actual users • Ratio of returning to non-returning users • Error/failure rates • Time from request to delivery • Time tracking during staff activity 3/5/13SCELCResearchDay 14
  15. 15. Projecting future expenditures • Equipment • Define its lifecycle • Amortize purchase cost • Add in maintenance costs • Compare to use as context • Staff • Educated guess at raises, turnover, benefit costs changes • Consider: • Inflation • Past trends • Upcoming sea changes 3/5/13SCELCResearchDay 15
  16. 16. Strategies for getting data that can be analyzed 3/5/13SCELCResearchDay 16
  17. 17. Tracking use • Circulation • COUNTER/SUSHI • Physical visitors • Web hits • Social media engagement • Attendance at events/sessions 3/5/13SCELCResearchDay 17
  18. 18. Tracking time • Can be effective when collected as a representative snapshot • Options for data collection • Clipboard next to a clock • Spreadsheet • Free time tracking apps • Make it as simple as possible 3/5/13SCELCResearchDay 18
  19. 19. Calculating costs • Staff time • 2080 hours per year is full time • Standard benefit percentages • Materials (including software) • Initial purchase • Maintenance contracts for big-ticket items • Amortize big costs over time in service • Overhead • Universities typically have standard rates 3/5/13SCELCResearchDay 19
  20. 20. Calculating error rates •Both objective and subjective criteria •Typically best when done as a sample •Consider both automated and manual means to locate errors for study 3/5/13SCELCResearchDay 20
  21. 21. Categorization • Putting things into like groups • Compare size of groups to one another • Compare effort spent on one group to another • Compare priority/value of one group to another • Can be done at time of data collection, or afterwards • Good idea to have some sense of categories at the beginning of the study 3/5/13SCELCResearchDay 21
  22. 22. Calculating benefit • Change in knowledge or status • Over time • After an interaction • Survey – ask about knowledge level before and after • Pre- and post-tests • Indirect measures • Number of people reached • Use 3/5/13SCELCResearchDay 22
  23. 23. Additional data analysis strategies 3/5/13SCELCResearchDay 23
  24. 24. Mechanics • Code qualitative data to make it processable • Make sure you pick a representative and consistent sample • Extrapolate based on known data when you need to • ALWAYS do a sanity check • Spreadsheets are your friend 3/5/13SCELCResearchDay 24
  25. 25. More advice • Context is key • Don’t be paralyzed by a perceived need for perfection • Know your basic analysis plans before you collect/identify data • Utilize pilot projects to generate data where there is none • Use the right tool for the job • Document your assumptions • It’s OK to use “napkin math” 3/5/13SCELCResearchDay 25
  26. 26. Get in the habit of collecting data. It will make your next decision easier. 3/5/13SCELCResearchDay 26
  27. 27. Thank you! Questions and discussion jennriley@unc.edu 3/5/13SCELCResearchDay 27

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