1. DLI Training – Ontario Region April 3, 2008 Carleton University An Introduction to

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

3. 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.

4. 4 Use of the Data Documentation Initiative standard facilitates: Interoperability. XML-compliant DDI Codebooks can be 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

5. 5 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

6. http://www.esds.ac.uk/ http://www.nsd.uhttp://www.nsd.uib.no/cessda/home.html http://zacat.gesis.org/webview/index.jsp http://ess.nsd.uib.no/webview/index.jsp ZA Online Study Catalogue

7. 7 Nesstar Publisher produces DDI-compliant metadata using a set of structured tags, grouped into ‘tabs’ in Publisher.

8. Document Description Tab

9. 9 Study Description Tab

10. 10 Other Study Materials Tab

11. 11 File Description Tab

12. 12 Variables Tab

13. 13 Variable Groups Tab

14. 14 Data Entry Tab

15. 15 Other Materials Tab

16. 16 The “ Best Practices Document” is designed to guide you through the ‘cataloguing’ process. It is available on the WIKI at URL: odesi.ca

17. 17 Once ready, a ‘marked up’ survey file is ‘published’ to the Nesstar Server where it becomes available through Nesstar Webview. It is at this point that most of you will walk on stage…

18. 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

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

20. “Fixed” “Flexible”

22. 22

23. 23

24. 24

25. 25

26. 26

27. 27 Variable ‘groups’ Variables

28. 28 Basic ‘frequencies’ or ‘marginals’ for categorical variables…

29. 29 Descriptive statistics for ‘continuous’ variables…

30. 30 But what if we want to look at more than one variable at a time? Say, for instance, the issue of weight and gender ?

31. 31

32. 32 OK… now we want to add gender as a variable.

33. 33

34. 34 Opinion of own weight, by sex Proportionally, more women than men had the opinion that they were “Overweight”.

35. 35 OK, but how does this change if we add an ‘objective’ measure of weight, such as ‘Body Mass Index’ (BMI)?

36. 36 Start where we left off… ‘opinion of own weight’, by sex But add another variable as a ‘layer’…

37. 37 Add ‘BMI class’ as a layer…

38. 38 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’

39. 39 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’

40. 40 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”.

41. OK, I have an confession to make…

42. 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

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

44. 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

45. 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.

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

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

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

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

50. Search results – Simple search You get all the surveys that have the ‘keyword’ you searched for… but specific questions (variables) are NOT highlighted. You have to open each survey (click on the icon: ) and look for the question(s) containing your keyword. Again, specific questions containing your keyword are NOT highlighted.

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

52. Advanced Search Screen

53. Search results – Advanced search Here, specific variables that meet the search criteria are shown, with the option to “Open in context” If you “Open in context”, a new “Nesstar” window will open, specific to the chosen survey, and highlighting the selected question. Closing this new window will take you back to your results list.

54. 54 Barchart Table Time series graph Map Clear Weight Subset Export to spreadsheet Download Export PDF Print Create bookmark Help Menu options:

55. 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, CRIC, Ipsos Reid) 3.Survey files from other sources (academics, government)… coming soon. 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)…

56. 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

57. 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.

59. 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.

61. 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

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

64. 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

65. In conclusion, 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.

67. Description: The Health Status Index or Health Utility INDEX (HUI) is a generic health status index that is able to synthesize both quantitative and qualitative aspects of health. The index, developed at McMaster University’s Centre for Health Economics and Policy Analysis, is based on the Comprehensive Health Status Measurement System (CHSMS). It provides a description of an individual’s overall functional health, based on eight attributes: vision, hearing, speech, mobility (ability to get around), dexterity (use of hands and fingers), cognition (memory and thinking), emotion (feelings), and pain and discomfort. http://www.statcan.ca/english/sdds/document/3226_D5_T9_V3_E.pdf HUI ranges from zero to one, with zero being ‘death’ and one being ‘perfect health’. Statistics Canada has yet to explain those folks who have negative scores… ‘die hard, with a vengeance’…