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Using the American Community Survey (ACS)

Using the American Community Survey (ACS). Maryland Sate Data Center Affiliate Meeting April 4, 2007. American Community Survey. Brief background of ACS Data release schedule 2005 ACS coverage Sampling error and statistical testing. Brief ACS Background.

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Using the American Community Survey (ACS)

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  1. Using the American Community Survey (ACS) Maryland Sate Data Center Affiliate Meeting April 4, 2007

  2. American Community Survey • Brief background of ACS • Data release schedule • 2005 ACS coverage • Sampling error and statistical testing

  3. Brief ACS Background • A large, continuous demographic survey, where data is collected monthly • Produces annual and multi-year estimates of the characteristics of population and housing (not counts) • Designed to replace the long form in 2010 • Is not a “snap shot” taken on one day, but more like a continuous (slightly blurry) video

  4. Brief ACS Background • Has been around since 2002 • From 2002 thru 2004 data was produced for geographic areas with a population of 250,000 or more (six jurisdictions plus test site in Maryland) • In 2005 expanded sample covers geographic areas of 65,000 or more

  5. Brief ACS Background • As we go through the remainder of this decade, will have one, three, and/or five-year estimates for geographic areas depending on their population size

  6. Data Release Schedule Prior to 2005 (2002 to 2004, ACS was produced for geographies of 250,000 or more (six jurisdictions plus test site in Maryland)

  7. 2005 ACS Coverage • 2005 is first year of full coverage (for households) • Total of 15 counties and Baltimore City; two places (Columbia and Silver Spring), also congressional districts, MSAs and CSAs

  8. 2005 ACS - First Year of Full Sample Size For U.S. => 250,000 hhs /month, 3 million per year

  9. Number Interviewed Around 1.5 %

  10. Things to Remember About 2005 ACS • Still Only HH population • GQs estimates coming for 2006 (pub 2007) • Are differences in some measurements compared to the 2000 Census • Census is a point in time. ACS – average over 12-month period • Residency rules: ACS – 2-month rule

  11. Things to Remember About 2005 ACS • ACS is characteristics, not counts • ACS HH population totals are controlled to Census Bureau’s annual intercensal population estimates for states and counties • When want A/R/S/ data for counties or states, the official source is the Bureau’s intercensal population estimates, NOT the ACS

  12. Available on MDP WEB Site

  13. Available on MDP WEB Site

  14. Baltimore County 2005 ACS

  15. Things to Remember About 2005 ACS • Sample size is much smaller than Census • One-year sample is 2.5 % of HHs vs 16.7% for Census long form • Over five-year period, ACS sample size is 12.5 percent • Therefore ACS sampling error will be larger

  16. Sampling Error & Standard Error • Sampling error occurs when estimates are derived from a sample rather than a census (complete count) of the population. • Standard error is an estimate of sampling error – how precise the survey estimates are

  17. Sampling Error & Margin of Error • Margin of Error = standard error for a given confidence interval (typically 90 percent). A measure of the precision of the estimate at a given confidence interval • Sampling error is often reported as the estimate “plus or minus” the margin of error

  18. Margin of Error (MOE) • MOE = 1.65 * Standard error 1.65 is used for the 90 percent confidence interval • Standard Error = MOE/1.65

  19. Baltimore City Median HH Income from 2005 ACS 90% C.I. = $32,456 +/- $1,849 = $30,607 to $34,305

  20. 90 Percent Confidence Interval • Odds are 9 to 1 that the interval contains the “true” value that you would have gotten from a full census

  21. Why you should care about Confidence Intervals • Lets you know how good the data is • Saves you from drawing erroneous conclusions. • Helps you decide how confident you can be about the assertions you make

  22. Comparing Two ACS Estimates • Given that estimates should now be viewed as ranges with confidence intervals • When is a difference between two estimates “statistically significant?” • There is the “easy way” and the (more correct) “hard way” • Then, there is an easy way to do the hard way

  23. Comparing Two Estimates • If have two estimates, need to determine if the apparent differences are “real” • Quick and dirty method is to “eye ball” whether the confidence intervals overlap

  24. Comparing Two Estimates(the easy way) • If the confidence intervals of two estimates do not overlap, then the two estimates are statistically different • If the confidence intervals of two estimates do overlap, then the two estimates are not statistically different (maybe)

  25. Estimates

  26. Estimates with Confidence Intervals

  27. 2005 ACS Median HH Income Data for Calvert & Howard Counties

  28. Comparing Two Estimates • Need to do a formal test of statistical significance if the confidence intervals do overlap (The Hard Way)

  29. Statistical Testing - Steps • Calculate the difference in the estimates • Calculate the standard errors of each estimate

  30. Statistical Testing - Steps • Calculate the standard error of the difference • Calculate the MOE of the difference • Compare the difference between the estimates to the margin of error of the difference.

  31. Statistical Testing - Steps 6. If the difference in the estimates is greater than the margin of error of the difference, then you conclude that the two estimates are statistically different 7. If the difference in the estimates is less than the margin of error of the difference, you conclude that the two estimates are not statistically different.

  32. 2005 ACS Median HH Income Data for Calvert & Howard Counties Calculate the Difference in the estimates Difference: = $91,184-$84,388 = $6,796 Step 1

  33. Calculate the Standard Errors of Each Estimate • SE = MOE/1.65 (for 90 % confidence interval.) Step 2

  34. 2005 ACS Data for Calvert & Howard Counties Calculate Standard errors (MOE/1.65) SE (Howard) = $3,386/1.65 = $2,052 SE (Calvert) = $5,101/1.65 = $3,092 Step 2

  35. Calculate the Standard Error of the Difference in the Estimates • Standard Error of (X – Y) Step 3

  36. Calculate the Margin of Error of the Difference MOE(X-Y) = SE(X-Y)*1.65 MOE(X-Y) = 3,711*1.65 = 6,123 Step 4

  37. Final Step • Compare the difference in the estimates to the MOE of the difference: • Difference = 6,796 • MOE = 6,123 • Difference is greater than MOE • CONCLUDE: the two estimates ARE different • at the 90 percent confidence interval Step 5

  38. Go to Spreadsheet (The easy way to do the hard way)

  39. Estimates

  40. Estimates

  41. Poverty Status in the Last 12 Months – 2004 ACS for Anne Arundel County

  42. Poverty Status in the Last 12 Months – 2005 ACS for Anne Arundel County

  43. ACS • Will have more “timely data” but need to be much more careful about using it • Besides statistical testing regarding differences, should look at MOE relative to the estimate

  44. Median Value of Owner Occupied Units by Year Built

  45. Summary • 2005 ACS is HH pop and not total pop • Use CB estimates, NOT ACS for A/S/R/ state & county estimates • Need to evaluate apparent differences to see if statistically significant • A good idea to look at MOE to evaluate how good an estimate is • May need to wait for 3 or 5-year estimates to get data with acceptable margins of error

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