using the american community survey (acs) maryland sate data center affiliate meeting april 4, 2007

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

• 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


• 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


• 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

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)

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

2005 ACS - First Year of Full Sample Size

For U.S. => 250,000 hhs /month, 3 million per year

Number Interviewed Around 1.5 %

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


• 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

Available on MDP WEB Site

Baltimore County 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

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

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

Margin of Error (MOE)

• MOE = 1.65 * Standard error1.65 is used for the 90 percent confidence interval

• Standard Error = MOE/1.65

Baltimore City Median HH Income from 2005 ACS

90% C.I. = $32,456 +/- $1,849

= $30,607 to $34,305

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

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

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

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

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)

2005 Median Household Income & Benefit Estimates *

$32,456

$33,317

$45,581

$47,771

$56,295

$59,137

$61,592

$62,939

$63,365

$65,343

$69,573

$71,961

$73,149

$75,833

$82,187

$84,388

$91,184

$0 $10,000 $20,000 $30,000 $40,000 $50,000 $60,000 $70,000 $80,000 $90,000 $100,000

Baltimore City

Allegany

Wicomico

Washington

Baltimore Co

Cecil

Maryland

St. Mary's

Prince George's

Harford

Charles

Anne Arundel

Frederick

Carroll

Montgomery

Calvert

Howard

Estimates


$0

$10,000

$20,000

$30,000

$40,000

$50,000

$60,000

$70,000

$80,000

$90,000

$100,000

Baltim

ore C

ity

Allegan

y

Wic

omic

o

Wash

ingto

n

Baltim

ore C

oCec

il

Mar

ylan

d

St. M

ary'

s

Prince

Geo

rge's

Harfo

rd

Charle

s

Anne Aru

ndel

Freder

ick

Carro

ll

Montg

omer

y

Calver

t

Howar

d

Estimates with Confidence Intervals

2005 ACS Median HH Income Data for Calvert & Howard Counties

Comparing Two Estimates

• Need to do a formal test of statistical significance if the confidence intervals do overlap

(The Hard Way)

Statistical Testing - Steps

1. Calculate the difference in the estimates

2. Calculate the standard errors of each estimate


3. Calculate the standard error of the difference

4. Calculate the MOE of the difference

5. Compare the difference between the estimates to the margin of error of the difference.


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.

2005 ACS Median HH Income Data for Calvert & Howard Counties

Difference: = $91,184-$84,388 = $6,796Calculate the Difference in the estimates

Step 1

Calculate the Standard Errors of Each Estimate

• SE = MOE/1.65 (for 90 % confidence interval.)

Step 2

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

Calculate the Standard Error of the Difference in the Estimates

• Standard Error of (X – Y)

22 [SE(Y)] [SE(X)]

711,33,092 2,052 22

Step 3

Calculate the Margin of Error of the Difference

MOE(X-Y) = 3,711*1.65 = 6,123

MOE(X-Y) = SE(X-Y)*1.65

Step 4

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

Go to Spreadsheet

(The easy way to do the hard way)


$32,456

$33,317

$45,581

$47,771

$56,295

$59,137

$61,592

$62,939

$63,365

$65,343

$69,573

$71,961

$73,149

$75,833

$82,187

$84,388

$91,184

$0 $10,000 $20,000 $30,000 $40,000 $50,000 $60,000 $70,000 $80,000 $90,000 $100,000

Baltimore City

Allegany

Wicomico

Washington

Baltimore Co

Cecil

Maryland

St. Mary's

Prince George's

Harford

Charles

Anne Arundel

Frederick

Carroll

Montgomery

Calvert

Howard

Estimates

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

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

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

Median Value of Owner Occupied Units by Year Built

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

using the american community survey (acs) maryland sate data center affiliate meeting april 4, 2007

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