2. 1 OLA Conference February 2008 Session 1022 Jeff Moon Head, Maps, Data, & Government Information Centre (MADGIC) Queen’s University An Introduction to

3. No statistics Do I want to use Statistics ? NO Flowchart: ‘Do I want to use statistics?’

4. What we’ll cover: What is survey data, and what’s the big deal? What’s happening in Ontario on the ‘data front’? Show me the goods… Why is this important at my library?

5. What is Survey Data and what’s the big deal? Tables, Charts, Graphs (in Books, CD-ROM, the WWW) A ‘number’ Survey Data (machine-readable) Data continuum… (Microdata)

6. What is Survey Data and what’s the big deal? Percentages Counts Standard Deviations Cross-tabs More advanced AnalysisMeans Statistical Analysis continuum… Descriptive Statistics Inferential Statistics

7. What is Survey Data and what’s the big deal? Tables, Charts, Graphs (in Books, CD-ROM, the WWW) A ‘number’ Survey Data (machine-readable) Statistics… Percentages Counts Standard Deviations Cross-tabs More advanced AnalysisMeans Statistical Analysis… (Microdata)

8. Survey DataAggregate Data PostcardCamera “Fixed” “Flexible” What is Survey Data and what’s the big deal?

9. We’ll look at the flexibility of survey data a bit later on… In the mean time, let’s look at the situation in Ontario right now…

10. 1990’s Home-grown survey data systems - Guelph, Western, Queen’s - No ‘cataloguing’ standard - Varying features/capabilities - Served a purpose at the time 2000’s Emerging data cataloguing standards Data Documentation Initiative -- an international standard for describing survey data. Like ‘MARC’, only for data Mature commercial software solutions Software such as Nesstar, SDA, and others

11. In 2005, the Data IN Ontario (DINO) working group of OCUL (O ntario Council of University Libraries) started thinking about moving beyond ‘home-grown’ data solutions, adopting the DDI standard, and building a province-wide data solution. A discussion paper followed… In 2007, with funding from OCUL and “Ontario Buys”, a Project Director was hired, and hardware/software purchased through Scholars Portal. OCUL & Ontario Buys Commercial Software Scholars Portal DDI Standard O ntario D ata Documentation, E xtraction S ervice and I nfrastructure Initiative

12. Lead institutions in are Carleton and Guelph, with in-kind assistance from Queen’s University. First step was developing a Canadian ‘best practices’ document for cataloguing data files using DDI – analogous to AACR2 for MARC. Next, survey files were ‘marked up’ (catalogued) and loaded onto a test server at Guelph. The team at Scholars Portal is working with to establish a data server and load data files.

13. 12 Use of the Data Documentation Initiative standard facilitates: Interoperability. XML-compliant DDI Codebooks can exchanged and transported seamlessly, and applications can be written to work with these homogeneous documents. Richer content. The DDI encourages better description of social science datasets, providing researchers with a better ‘window’ into what is available Single document - multiple purposes. DDI codebook contain all of the information necessary to produce several different types of output, including: a traditional social science codebook, a bibliographic record, and SAS/SPSS/Stata data definition statements. Thus, the document may be repurposed for different needs and applications. On-line subsetting and analysis. Because the DDI markup extends down to the variable level and provides a standard uniform structure and content for variables, DDI documents are easily imported into on-line analysis systems, rendering datasets more readily usable for a wider audience. Precision in searching. Since each of the elements in a DDI-compliant codebook is tagged, searches across documents and studies are possible. www.ddialliance.org

14. 13 SOFTWARE CHOSEN  NESSTAR Developed by the “Norwegian Social Science Data Services” -- Ne tworked S ocial S cience T ools a nd R esources In use internationally (Europe, UK, US, Canada) In Ontario: Queens, Guelph, Carleton, Windsor, Ottawa, U. of T. and Statistics Canada use Nesstar DDI compliant Search by keyword for surveys and survey questions Do basic data exploration and analysis on the web Download full datasets or subsets in popular formats Export tables and charts

15. http://nesstar.esds.ac.uk/webview/ http://www.nsd.uib.no/cessda/extcessda.jsp

16. 15 Nesstar Publisher produces DDI-compliant metadata using a set of structured tags, grouped into ‘tabs’ in Publisher.

17. Document Description Tab

18. 17 Study Description Tab

19. 18 Other Study Materials Tab

20. 19 File Description Tab

21. 20 Variables Tab

22. 21 Variable Groups Tab

23. 22 Data Entry Tab

24. 23 Other Materials Tab

25. 24 Once ready, a ‘marked up’ survey file is ‘published’ to the Nesstar Server where it becomes available through Nesstar Webview.

26. Let’s take a look at how can be used to answer a research question. How do men and women differ in perceptions of their health (using weight as an example). Concepts? Health Body Mass Index (BMI) Weight Males/Females

27. Starting point: A simple search on the Statistics Canada web site…

28. “Fixed” “Flexible”

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35. 34 Variable ‘groups’ Variables

36. 35 Basic ‘frequencies’ or ‘marginals’ for categorical variables…

37. 36 Descriptive statistics for ‘continuous’ variables…

38. 37 But what if we want to look at more than one variable at a time? Say, for instance, the issue of weight and gender ?

39. 38 Before proceeding, you must log into the Nesstar System

40. 39 OK… now we want to add gender as a variable.

41. 40

42. 41 Opinion of own weight, by sex Proportionally, more women than men had the opinion that they were “Overweight”.

43. 42 OK, but how does this change if we add an ‘objective’ measure of weight, such as ‘Body Mass Index’ (BMI)?

44. 43 Start where we left off… ‘opinion of own weight’, by sex But add another variable as a ‘layer’…

45. 44 Add ‘BMI class’ as a layer…

46. 45 Of respondents who were ‘objectively’ underweight, proportionally more women than men had the ‘subjective’ opinion that they were “Just About Right”. Layer = those with a BMI indicating ‘underweight’

47. 46 Of respondents who were ‘objectively’ normal weight, proportionally more women than men had the ‘subjective’ opinion that they were “Overweight”. Layer = those with a BMI indicating ‘normal weight’

48. 47 Layer = those with a BMI indicating ‘overweight’ Of respondents who were ‘objectively’ overweight, proportionally more MEN than women had the ‘subjective’ opinion that they were “Just About Right”.

49. OK, I have an confession to make…

50. Statistical Weight… All the previous slides ignored an important concept… that of weight. Not ‘weight in kilograms’ but rather ‘statistical weight’. We don’t want to describe the sample… we want to describe the population at large (in this case, Canadians 18+). Statistical weights are assigned by statisticians, not surprisingly, to each individual in a sample, based on a variety of demographic and sampling considerations. These weights reflect how many people a given respondent ‘represents’ in the population being studied. Sample count  Population Estimate Statistical weight

51. Weight ‘off’: Note the sample sizes Weight ‘on’: Note the sample sizes But also note the differences in percentages…

52. In general, you must apply the Statistical Weight in order to get valid results. It is easy to turn weight ‘on’ in Nesstar ( ), or other statistical packages (e.g. SPSS, SAS, STATA). BUT READ THE DOCUMENTATION

53. They say a picture is worth a thousand words… If this is true, then a good chart has to be worth at least a couple of hundred… Let’s revisit our data visually using the ‘bar chart’ feature of Nesstar.

54. Weight is on Barcharts showing weighted results: Proportionally, of those who are objectively underweight, more women than men think they are ‘just about right’

55. Weight is on Barcharts showing weighted results: Proportionally, of those who are objectively normal weight, more women than men think they are overweight

56. Weight is on Barcharts showing weighted results: Proportionally, of those who are objectively overweight, more men than women think they are ‘just about right’

57. Searching for ‘questions’ in Nesstar: Simple Search

58. Search results – Simple search You get all the surveys that have the ‘keyword’ you searched for… but specific questions (variables) are NOT highlighted.

59. Searching for ‘questions’ in Nesstar: Advanced Search Advanced Search

60. Advanced Search Screen

61. Search results – Advanced search Here, specific variables that meet the search criteria are shown, with the option of “opening in context”

62. 61 Barchart Table Time series graph Map Clear Weight Subset Export to spreadsheet Download Export PDF Print Create bookmark Help Menu options:

63. OK, so what kind of data can I expect to find using ODESI? 1.Statistics Canada survey files released through the Data Liberation Initiative (Census PUMF’s, Special Surveys, General Social Surveys, and more) 2.Public Opinion Polls (e.g. Gallup) 3.Survey files from other sources (academics) These surveys and polls include questions on all manner of topics (politics, health, work, leisure, education, drug use, aging, spending, internet use, and many more)…

64. Let’s take a look at some Gallup questions… Dataset: Canadian Gallup Poll, August 1951, #212 In some cities in Canada, horsemeat is now being sold, because of the high price of other meats. If horsemeat were available here, would you be willing to try it? 35.9% of respondents said “Yes” they’d be willing. Of course, this questions begs for a yea or ‘ neigh ’ answer

65. Dataset: Canadian Gallup Poll, September 1956, #251 WOULD YOU FAVOR REQUIRING EVERY ABLE-BODIED YOUNG MAN IN THIS COUNTRY, WHEN HE REACHES THE AGE OF 18, TO SPEND ONE YEAR IN MILITARY TRAINING AND THEN JOIN THE RESERVES OR MILITIA? 65.7% favoured this.

67. Dataset: Canadian Gallup Poll, August 1953, #231 HOW MUCH DO YOU THINK A YOUNG MAN SHOULD BE EARNING PER WEEK BEFORE HE GETS MARRIED? $41 - $50 per week equals roughly $2100 - $2600 annually.

69. Dataset: Canadian Gallup Poll, August 1953, #231 THERE'S AN ATTEMPT BEING MADE BY SOME FASHION LEADERS TO SHORTEN WOMEN'S SKIRTS. DO YOU THINK THAT WOMEN SHOULD FOLLOW THIS LEAD - AND WEAR SKIRTS SHORTER THAN THEY ARE NOW? 13% Shorter 82 % About the same 5 % Longer

70. DO YOU APPROVE OF THE USE OF BIRTH CONTROL? Tracking Opinions over time

72. 1.Researchers can search across all surveys in a collection. 2.Researchers have the ability to explore surveys in more detail (e.g. looking at questions by gender, province, age group, income, etc.). 3.Tables can be saved in Excel or Adobe format. 4.Researchers can download data for use in more powerful statistical packages (SPSS, SAS, etc.) Key points about survey data in

73. In conclusion, ODESI will: 1.Provide a more level ‘data’ playing field for Ontario Universities. 2.Provide students and researchers with access to a substantial and growing body of survey and polling data, both current and historical. 3.Provide an easy, yet powerful, search and exploration tool (Nesstar) that will serve both beginners and ‘power users’. 4.Encourage cooperation and sharing of data and metadata in Ontario. 5.Serve as a potential model for other jurisdictions.