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Reading Tables 201, Census of Population

Reading Tables 201, Census of Population. TRY Staff Conference May 5, 2009 Laine Ruus, Data Services Librarian, University of Toronto Powerpoint available at: http://www.chass.utoronto.ca/datalib/misc/try/try09.ppt.

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Reading Tables 201, Census of Population

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  1. Reading Tables 201, Census of Population TRY Staff Conference May 5, 2009 Laine Ruus, Data Services Librarian, University of Toronto Powerpoint available at: http://www.chass.utoronto.ca/datalib/misc/try/try09.ppt

  2. Define some terminology commonly used in statistical tables, eg census. Especially 2006 census products contain more derived measures than previous census products. Consider effect of some other characteristics of statistics in tables Suggest some ways to interpret the statistics appropriately Focus on means, medians and standard errors, and ratios, proportions, percentages, rates Objectives

  3. Characteristics of statistics in tables: selecting the right geography…

  4. Which Toronto? Source: Census of population, 2006: community profiles

  5. Which Toronto is it? • City of Toronto, the census subdivision (CSD)? • Metro Toronto, the census division/county (CD)? • Census metropolitan area (CMA)Toronto? • Health region (LHIN)? • GTA?

  6. Which Toronto is it? • City of Toronto, the census subdivision (CSD)? • Metro Toronto, the census division/county (CD)? • Census metropolitan area (CMA)Toronto? • Health region (LHIN)? • GTA?

  7. Which Toronto is it: City of Toronto, the census subdivision (CSD)? Metro Toronto, the census division/county (CD)? Census metropolitan area (CMA)Toronto? Health region (LHIN)? GTA?

  8. Which Toronto is it? • City of Toronto, the census subdivision (CSD)? • Metro Toronto, the census division/county (CD)? • Census metropolitan area (CMA)Toronto? • Health region (LHIN)? • GTA?

  9. And the GTA is… • CMA Toronto • plus CMA Oshawa (?) = Toronto economic region • plus CMA Hamilton (?)

  10. What difference does it make? Source: Census of population, 2006: community profiles

  11. Characteristics of statistics in tables: selecting the right product… Source: http://www12.statcan.ca/english/census06/index.cfm

  12. Community profiles: • 374 characteristics, counts, medians and selected rates only • CDs, CSDs, CMAs, health regions, CTs only • Cumulative profiles • 2172 characteristics, counts, means and medians • 11 geographic levels, incl. FSAs, DAs, FEDs • Topic-based tabulations • About 360 tables (so far) • More detailed subject matter, (usually) less detailed geography • Census trends • Selected tables for 1996-2001-2006 • Mainly ratios, percentages, rates, etc.

  13. Total income from the community profile Source: Census of population, 2006: community profiles

  14. Total income: from the cumulative profile Source: Census of population, 2006: cumulative profiles

  15. Characteristics of statistics in tables: selecting the right measure… • the median, an average, is the middle number in a list of numbers, smallest to biggest. So, the median of 2, 4, 7, 9, 12 is 7. So the median is the number that divides the bottom 50% of the population from the top 50%. • the mean is the mathematical average of a set of numbers. The average is calculated by adding up two or more scores and dividing the total by the number of scores. Consider the following number set: 2, 4, 6, 9, 12. The average is calculated as: 2 + 4 + 6 + 9 + 12 = 33 / 5 = 6.6. So the average of the number set is 6.6.

  16. What difference does it make? numerically Source: Census of population, 2006: cumulative profile of federal electoral districts

  17. What difference does it make? graphically Nova Scotia/Nouvelle-Ecosse (12)

  18. So what about that pesky “standard error of the average income”? Why does Stats Can produce that? And what does it mean? • In Statistics Canada speak: “it serves as a rough indicator of the precision of the corresponding estimate of average income. For about 68% of the samples which could be selected from the sample frame, the difference between the sample estimate of average income and the corresponding figure based on complete enumeration would be less than one standard error (SE). For about 95% of the possible samples, the difference would be less than two standard errors and, in about 99% of the samples, the difference would be less than approximately two and one half standard errors.” • In plainer language: Since this is an estimate, what is the outside range that the right value is likely to be? Answer: there’s a 68% chance that it’s somewhere between the the average income minus 1 SE and the average income plus 1 SE. And about a 95% chance that its between the average income minus 2 SEs and the average income plus 2 SEs.

  19. What does the standard error NOT mean? It does not mean that 68% of incomes are within one standard error of the mean, and 95% of incomes within 2 standard errors. Source: Census of population, 2006: cumulative profile of federal electoral districts

  20. Where: AverageLL = (average income – 1 standard error) AverageUL = (average income + 1 standard error) Source: Census of Canada, 2006: cumulative profile of federal electoral districts

  21. Computing income for the GTA: can we do it from the community profile? Answer: no. We can only compute it from the mean or average income Source: Census of Canada, 2006: community profiles

  22. Computing income for the GTA: can we do it from the cumulative profile? A: Yes we can. Only question is, which count to use? Note: this is a count too Source: Census of Canada, 2006: profile of census metropolitan areas and census agglomerations

  23. To compute the average income of the GTA: • We could just average the 2 average incomes: ((40704+39644)/2)=40174), but that would be wrong! And would underestimate the average income. To calculate it correctly: • Multiply ‘population with income’ by ‘average income’ for each of CMAs Toronto and Oshawa, to produce total income for each CMA • Add the two total incomes from #1 • Add the two populations (with income) • Divide the total income from #2 by the total population from #3

  24. Par example:

  25. Another common measure is median age A technique for making sense of tables of numbers: look for the largest numbers, and the smallest numbers. Think of a ‘story’ to explain why there is a difference. Between 2001 and 2006, in a static population, the median age should have become 5 years higher. What explains the difference between Petawawa and Port Hope? Source: 2006 Census: highlight tables.

  26. Ratios, proportions, percents, and rates

  27. Ratios express the amount of one quantity relative to another, and can be expressed as: • a proportion: (1456.6/18294.7=) .0796 [Ratio of the unemployed to the total labour force] • a percentage (the “unemployment rate”: 1456.6/18294.7 *100=) 7.96% [Ratio of the unemployed to the total labour force x 100] • an odds: (16838.1/1456.6=) 11.56 to 1, the odds on being employed [NB the odds is the ratio of the employed to the unemployed]

  28. These ratios show number of children relative to the number of persons over 64. In 2001, Toronto had 173 children to every 100 persons over 64. By 2006, there were only 156 children per 100 persons over 64. That’s a difference of 1.73-1.56=).17, or 17 fewer children per 100 persons over 64. Does Toronto need more elementary schools, or more facilities for elder care, compared to in 2001? Source: 2006 Census: highlight tables.

  29. PS It’s definitely worth while to read the footnotes!

  30. Ratios tend to be used for unrelated quantities, eg beds per 1,000 population:

  31. …and when expressing very small numbers Proportions: are ratios in which all cases in the numerator are also included in the denominator Age-standardized mortality ‘rate’ (a proportion) from female breast cancer, in Canada (annual, 2004): • per 100,000= 23.1 • per 10,000 = 2.31 • per 1,000 = 0.231 • per 100= 0.0231 • per 10= 0.00231 • per 1= 0.000231

  32. Rates are proportions in which the denominator is a measure of time(eg heart beats per minute) Statistics Canada provides rates such as: • unemployment rate: unemployment rate = ((unemployed labour force / total labour force) * 100) • participation rate: participation rate = ((labour force / total population 15 and over) * 100) • employment rate (aka employed/population ratio): employment rate = ((employed labour force / total population 15 and over) * 100) • Notice that the participation and employment rates have the same denominator. The unemployment rate, however, does not.

  33. From the 2006 census community profiles: Source: Census of Canada, 2006: community profiles

  34. And now for an open-ended quiz!What questions do you have about what I have covered?What didn’t I cover that I should have?

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