Presentation on creating a data-driven project for library decision making - day 1

1. Elaine Lasda, MLS, CAS
University at Albany Libraries
March 12, 2018

2. Introduction
Collecting Data
Interpreting Data
Visualizing Data
Class Missions
Agenda

3. Philosophy

4. What is Data?

5. The Data Cake Rubric
http://markjohnstone.co/wp-content/uploads/2015/11/data-cake-01.jpg

6. Where do I find data?

7. Types of Data

8. Nominal
Ordinal
Interval/Discrete
Ratio
“NOIR” Data Type Rubric

9. Numerical Datatypes:
Ordinal
Continuous
Discrete
I find these more useful

10. Text/Character Datatypes:
Character/String
Categorical
Interval
I find these more useful

11.  Total count of print monograph volumes in the Texas
State Library
 Top 20 Library Systems in the Southwest ranked by
annual budget
 Total overdue fines paid at the Albany Public Library in
FY 2016-2017
 Responses collected from a suggestion box at the
circulation desk of the Heermance Public Library
 Children’s reading levels by age range
 The zip codes encompassing your library’s service area
Quick check:
ID the Datatype

12. Collection Methods

13. Do do you have to
collect your own data?
First party data
Collected by entity doing the analysis
Unique; often a direct relationship to
data source
Trustworthy (?)
Smaller datasets (mostly)

14. Second party data
Access from external platform,
but you can obtain it
Repositories
Creator of platform has direct
relationship to data source
 Trustworthy (?)

15. Third party data
Access from another platform
Collected anonymously; without
user consent
“data exhaust”
Large, aggregated datasets
 Trustworthy (?)

16. Quantitative
Qualitative
Mixed Methods
Another Perspective

17. Empirical
Anecdotal
Logical
A Third Perspective

18. How does the type of data you
wish to collect affect the way in
which you collect it?
QUESTION FOR YOU

19. DATA CLEANING
Cleaning

21. QUESTION FOR YOU
What can happen to your data
when it is being collected that
requires you to “clean” it prior to
analysis?

22. Formatting error
Misspellings
Decimal points off
Numerical/text transpositions
Incomplete data
N/A vs. 0
Common Data “Dirt”

23. Data Transformation

24.  Apply a mathematical formula to correct for skew
 Log
 Square Root/Cube Root/Square
 Inversion
 For non-numeric data:
 Create frequency tables
 Assign a scale to a category
 Category dis/aggregation
What are transformations?

25. Data Interpretation

26. QUESTION FOR YOU
What’s are some differences
between Data and Statistics?

27. Descriptive Statistics

28. Inferential Statistics

29. Mean
Median
Mode

30. Range
Quartile
Variance
Standard Deviation

31. Frequencies
Frequency Distributions
Correlation/Causation
Measures of Error

33. Vizualization/Presentation

34. Table
https://www.imls.gov/sites/default/files/fy2015_pls_tables.pdf

35. Bar graph
http://www.nysl.nysed.gov/libdev/libs/biblcnct.htm

36. Line Graph
https://dpi.wi.gov/sites/default/files/imce/pld/pdf/wiplservicetrends.pdf

37. Area Graph

38. Scatter Plot
http://www.gislibrarian.com/portfolio-items/how-accessible-are-austins-public-libraries/

39. Pie Graph
http://nlcblogs.nebraska.gov/nlcblog/2015/09/09/the-data-dude-pie-charts/

40. Name some key considerations when
designing a chart, graph, infographic or
other visual display of your data.
QUESTION FOR YOU

41. Clear labels
ID Units of easure
Standard intervals
Avoid 3-D effects
LESS IS MORE
Best Practices

42. http://viz.wtf
Don’t let this happen to you

43.  Information is Beautiful
 Many Eyes
 The Grammar of Graphics
 Tufte- Visual Explanations
Develop Your Eye

44. Briefly: ETHICS

45. Ethics
How is the data collected?
How is the data used?
How is the data stored and preserved
and what are the implications?
How and when is the data disposed
of?

46. Questions?

47. Message Board:
Case Study
Assignment:
Explore A Dataset
Homework