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Lewellen - Formulating, Interpretation and Presentation of Data
The document outlines best practices for data visualization, interpretation, and benchmarking in library assessment, emphasizing the importance of context and understanding audience demographics. It discusses methods for displaying various data types, correlation versus causation, and the significance of transparent data communication. Key elements include operational benchmarking strategies and fostering a culture of assessment within organizations.
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Lewellen - Formulating, Interpretation and Presentation of Data
- 1. Formulating, Interpretation and Presentationof Data Rachel Lewellen Head of Assessment and Program Management NISO Webinar November 9, 2018
- 2. Introduction • Data visualization •Correlational and causation • Benchmarking • Data discomfort
- 3. Starr Hoffman –October 26, 2018
- 4. Frankie Wilson -November 2, 2018
- 5. Sample Visualizations • Categories •Maps • Time • Numbers
- 6. Context Matters • Relativeto total • Relative to campus • Relative to peers • Relative to capacity (spaces) • Relative to other factors (per student, per faculty)
- 7. Categories • Who – Undergraduates –Graduates – Faculty – Staff – School, college or department – Other demographic categories • What – Books – Digital collections – Images – E-resources – Equipment – Rooms – Services
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- 10. Pie, Donuts, andTreemaps
- 11. Mapping • On-campus – Inlibrary spaces – In other spaces • Off-campus – Local – Far away • Collaborative or quiet study • Open stacks, storage, or partner libraries
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- 15. Art Traffic atthe Louvre A study of visitors’ behavior using Bluetooth data http://senseable.mit.edu/louvre/# https://youtu.be/rRjWaH1QHJU
- 16. Time Series Data •Time of day • Day of week • Week number • Month • By semester • During extended hours • By cyclical periods (exams, spring break, grant periods) • Calendar year/fiscal year • Project tracking
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- 18. Monthly and RunningTotal
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- 21. Time of Dayand Day of Week
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- 24. Numerical • Total (sum)– items, dollars, terabytes, images, feet • Difference • Unique counts • Mean (average) • Median (middle) • Mode (most frequently occurring) • Percent change • Percent of total • Year over year • Percent capacity • Distance to goal • Cost per use • Other ratios • Key performance indicators (KPI)
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- 29. Survey Data • Satisfaction •Importance • Interest • Skill • Learning • Value
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- 36. Know your audience DeliveringBusiness Intelligence with Microsoft SQL Server 2016, Fourth Edition by Brain Larson. Page 16.
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- 40. Correlation and Causation xkcd– a webcomic of romance, sarcasm, math and language https://xkcd.com/552/
- 41. The danger ofmixing up causality and correlation: Ionica Smeets at TEDxDelft https://youtu.be/8B271L3NtAw
- 42. Evidence to EstablishCausality • Association – Is there a relationship, is there a correlation? – Is x related to y? • Direction of influence – Temporal factors (chicken and the egg) – Does x come first? • Absence of alternative explanations – Non-spuriousness
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- 46. Benchmarking • Benchmarking isa comparison of performance measures between similar entities and/or against recognized standards. • Library benchmarks are typically comparisons of numerical (quantitative) statistics such as circulation, visits, and revenues. https://ivygroup.com/blog/benchmarking-library-performance/
- 47. How Benchmarking Works •Select an aspect, service or process to benchmark • Identify the key performance metrics • Choose comparison group to benchmark • Collect data on performance and practices • Analyze the data and identify opportunities for improvement • Adapt and implement the best practices, setting reasonable goals and ensuring organizational acceptance
- 48. Examples of LibraryBenchmarking • Resource Comparison – Funding levels – % of budget on collections or format – Size and type of staff • Service models – Reference service models – Writing center collaborations – Learning Commons – Copyright services • Reorganization – Administrative structure – Department alignments • Best Practices – Recruitment and retention – Improve printing – Staff training programs
- 49. Comparison Groups • Institutionaldefined peer group • Demographics • Within consortia • Other characteristics • Leaders or by reputation
- 50. Sources of BenchmarkingData • Integrated Postsecondary Education Data System (IPEDS) • The Association of College and Research Libraries (ACRL) • Association of Research Libraries (ARL) • Standardized surveys (LibQUAL, MISO, Ithaka, National Survey of Student Engagement (NSSE), Common Data Set (CDS) • Email, phone calls and site visits
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- 52. Build a Cultureof Assessment • Takes time • Transparency with data • Communication about data use • Highlight examples of data use • Build trust • Data as information not as a judgement
- 53. Practices • Explicitly acknowledgewhen data is used – Highlight positive outcomes big and small • Support data allies and champions – Honor and recognize data practitioners and practices • Normalize everyday data use – Include data competencies and practices in all job descriptions • Communication and transparency
- 54. Interpersonal Effectiveness • Validateand understand concerns • Acknowledge limitations of data when appropriate • Be trustworthy – Professional credibility – Personal credibility • Be reasonable • Be a leader • Best change management strategies
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