Deanne W. Swan, PhD IMLS / OPRE dswan@imls

1. Analysis of Library Data at the State & Local Level2013 SDC ConferenceSt. Louis, MODecember 12, 2013 Deanne W. Swan, PhD IMLS / OPRE dswan@imls.gov Frank Nelson Idaho Public Libraries Frank.Nelson@libraries.idaho.gov

2. Why data analysis?

3. We analyze data… … to discover useful information. … to answer questions. … to solve problems. … to make better decisions. … to tell a story.

4. What is data analysis?

5. Data analysis is… … a process… of inspecting, cleaning, transforming, and modeling data… … with the goal of uncovering information, supporting decision making, and telling stories.

6. Data Analysis – A Brief Introduction

7. Let’s start with an example…

8. The Problem Children who start school not ready to learn are at-risk for reading below proficiency at the end of third grade. Children who can’t read at grade level by the end of third grade have low academic achievement in later grades and are less likely to graduate from high school. Where should we invest our resources?

9. How big of a problem is this?

10. Does it affect all children the same way?

11. What are the differences between these children?

12. How early can we see evidence of this problem?

13. Does the magnitude of this problem change over time?

14. Is there a measurable difference between identifiable groups of children?

15. Is there some trait that might explain or differentiate this gap?

16. Are there additional factors that might exacerbate the problem?

17. Is this contextual factor consistent across geography?

18. Is there a community resource that could ameliorate this problem?

19. Is this resource utilized equally across child characteristics?

20. The Problem restated • In order to succeed in school, children need to be ready to learn, including having fundamental early literacy skills, when they enter school. • There is an opportunity gap. Certain children are at-risk for entering school not ready to learn. • These children include children who are Hispanic, children of immigrant parents, and children living in poverty.

21. The Problem restated • These children are often not enrolled in early education programs that help prepare children for entry to school, leaving these children and their families underserved. Question: What is the status of children’s programs in public libraries in areas of high concentration of child poverty and immigrant families?

22. Analysis What is the relationship between attendance at public library children’s programs to high levels of child poverty and immigrant status for the top 100 metropolitan areas? Data: PLS (IMLS) SAIPE and CPS (Census) Crosswalk of Top 100 MSAs

23. Analysis

24. Analysis

25. Analysis State and County Estimates for 2010 The files in the data directory contain estimates of poverty and income for 2010. There is one data file for each state (or US) with data for ALL with the 2010 statistics. Excel format: est10ALL.xls – US and all states and counties est10US.xls – US and states data

26. Analysis

27. Analysis

28. Analysis

29. Analysis Join (Merge) all of the files based on the linking variable: FIPSCO (FIPS county)

30. Analysis

31. Analysis

32. Analysis

33. Analysis

34. Is this resource available to children who are at-risk?

35. Is the difference in this resource dispersed equally geographically?

36. Result • In some areas with high concentrations of children with highest risk (poverty and COI status), there is lower attendance at children’s programs in public libraries.

37. Statistics without context have no meaning. They are simply numbers. In order to make our stories more compelling and powerful, we need to put public library data within context: • Place Geographic, Spatial Data • Time Temporal Data • Social Demographic Data • Economic Financial / Labor Data • Political Program and Policy Data

38. Data Analysis Data analysis is a process… … of inspecting, cleaning, transforming, and modeling data… … with the goal of uncovering information, supporting decision making, and telling stories.

39. Data Analysis

40. Find Data Where can I get data to analyze? Collect your own data OR Use data someone else collected.

41. Find Data Federal Statistical Collections IMLS: www.imls.gov PLS, SLAA U.S. Census Bureau: www.census.gov ACS, CPS, SAIPE / Data Ferrett NCES: www.nces.ed.gov NAEP, NHES, ECLS, CCD, SASS NCHS: www.cdc.gov/nchs/ NHANES, NHIS, NVSS BLS: www.bls.gov GDP, CPI, (Un)employment

42. Find Data

43. Find Data

44. Find Data First rule of analysis club: Read the data documentation. Second rule of analysis club: Read the data documentation.

45. Manage Data Managing data includes all of the activities needed to obtain, inspect, clean, scrub, transform, and manipulate data.

46. Manage Data Tools for Cleaning and Analyzing Data Statistical Packages: SAS, SPSS, Stata ($$$) Free Statistical Tools: R: http://www.r-project.org/ Data Applied: http://www.data-applied.com/

47. Manage Data Download the Data Determine the best format for your needs Read the data documentation. Resources Harvard University GIS tutorial: http://www.gsd.harvard.edu/gis/manual/data/ Sources of Spatial Data, Data Handling, Effective Cartography, Analytic Techniques U.S. Census Bureau: Download the database http://quickfacts.census.gov/qfd/download_data.html

48. Manage Data Join/Merge Data FIPS code (Federal Information Processing Standard) State, County, Place FIPS Crosswalk National Bureau of Economic Research (NBER): http://www.nber.org/data/ssa-fips-state-county-crosswalk.html

49. Manage Data How to merge two data files in R: Suppose you have two data files, dataset1 and dataset2, that need to be merged into a single data set. First, read both data files in R. Then, use the merge() function to join the two data sets based on a unique id variable that is common to both data sets: > merged.data <- merge(dataset1, dataset2, by=“FIPSCO")

50. Manage Data Explore/Clean Data