Data, evidence and outcomes

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Presentation at the American Library Association Conference on June 28, 2014. Sponsored by counting Opinions

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  • Size of budget, size of collections, size (# of) buildings, same state, recognized peers, aspirational peers, Athletic conference, geography
  • Data, evidence and outcomes

    1. 1. Data, Evidence and Outcomes Joe Matthews American Library Association Sponsored by Counting Opinions June 28, 2014
    2. 2. Need for Data  Making management decisions  Increase program & service effectiveness  Justifying budgets  Evaluating performance  Doing advocacy
    3. 3. Types of Data  Quantitative  Counting  Surveys  evaluation of services  Customer satisfaction  Qualitative
    4. 4. Purpose The purpose of any data gathering activity & analysis is to INFORM
    5. 5. Strategies  User versus library’s perspective  Examining trends  Comparing libraries  Social context  Outcomes
    6. 6. User’s Perspective  How well?  How courteous?  How responsive?  How satisfied?
    7. 7. How courteous?  Welcoming  Attentive
    8. 8. How responsive?  Anticipatory  Helpful  Empathetic
    9. 9. How satisfied?  Expectations met  Materials obtained  Personal interaction  Ease of use  Equipment used  Environment  Comfort  Willingness to return
    10. 10. Library’s Perspective  How much?  How many?  How prompt?  How economical?
    11. 11. How much?  Magnitude  Percent of change from last year  Percent of overall change  Cost
    12. 12. How many?  Magnitude  Change
    13. 13. How prompt?  Cycle times  Turnaround time  Wait times  Anticipatory
    14. 14. How economical?  Resources used  Units processed  Productivity
    15. 15. Library & Customers Perspective  How valuable?  How reliable?  How accurate?  How well?
    16. 16. How valuable?  Effort expended  Costs  Benefits obtained
    17. 17. How reliable?  Dependability/Consistency  Access  Accuracy
    18. 18. How accurate?  Completeness  Comprehensiveness  Currency
    19. 19. How well?  Accuracy  Promptness  Courtesy  Expertise
    20. 20. Outcomes Change in a person’s life Affective – attitudes, confidence, satisfaction Behavioral Knowledge-based Competency-based – apply new skills
    21. 21. Tracking Trends  Year-to-year trends  Comparing trends  Indexing dollars for inflation  Indexing budgets for cost of living  Projecting future needs
    22. 22. Comparing Libraries  Identifying peers  Individual vs. grouped data  Rankings
    23. 23. Comparing Libraries Examples  Comparing Peers – criteria ?  Rankings  Percentages/grouped data  Summary stats  Benchmarking/standards
    24. 24. Libraries in Social Context  Social, political, & economic environment  Focusing on decision-makers’ concerns  Finding the right data  Creating the “hook”
    25. 25. Social Context Examples  PLs rank 2nd behind fire protection among local government services  Visits to PLs outnumber professional football attendance of X annually by a factor of Y  PLs loan 4.5 million items per day – FedEx delivers about 1.75 million packages each day
    26. 26. Evidence  How can evidence contribute to the quality of the decision making process?  What are the risks, costs, and benefits of an evidence-based approach?  Ensure that objective evidence is reflected in the decision making process.
    27. 27. Evidence  Depends on the need  BIG DATA  Combine data
    28. 28. Source of Data  IMLS data  ARL data  PLAmetrics  ACRLMetrics  Edge Initiative  Impact Survey – UW  Evidence-based Library & Information Practice
    29. 29. Collection Insights  collectionHQ – selection, management, promotion  Reports from your ILS
    30. 30. Patron Perspective  Segmentation analytics  Demographics  Lifestyles  Geography  Benefit segmentation
    31. 31. Program Priorities  Track program measures  Provide programs tied to strategic goals – aligned with community needs  Early childhood education  School age education support  Economic development  Community engagement  . . .
    32. 32. Advocacy Hooks  Use data for presentations  Use data to target likely voters  Use data to demonstrate value in the life of your customers
    33. 33. Outcomes Libraries cannot demonstrate value until they define outcomes of institutional relevance and then measure the degree to which they attain them
    34. 34. Outcomes  Focus on the end results  Logic Model  Orr’s Input-Process-Output-Outcomes Model
    35. 35. Logic Model use the if then exercise
    36. 36. If the library provides assistance to students with their homework then the students will do better with their schoolwork. If they do better with their schoolwork then they will get better grades and attend more regularly. If they get better grades and attend more regularly then they are more likely to graduate. If they graduate then they are more likely to become employed and have a higher standard of living.
    37. 37. Orr’s Model Input Process Output Outcome Impact
    38. 38. Gates Common Impact Measurement System  Digital inclusion  Culture & Leisure  Education  Communication  Health  Economic Development
    39. 39. Academic Library Outcomes  Student Learning  Teaching Effectiveness  Research
    40. 40. Public Library Outcomes  PLA Performance Measurement Task Force Focus is on programs and their outcomes  Summer Reading Programs  Early Childhood Education Programs  Civic Engagement  Digital Access & Learning  Economic Development
    41. 41. Communication  Know your customer needs  What’s going to resonate  Data + stories = success  Focus on value – not how busy you are
    42. 42. Data, Evidence and Outcomes Joe AT JoeMatthews.Org

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