2015 CE Survey Microda

Users’ Workshop

Sample Design and Weights

Brian T. Nix

Mathematical Statistician

Division of Price Statistical Methods

Bureau of Labor Statistics

U.S. Department of Labor

July 16, 2015
Overview

 History and Concepts Sample Selection

Define PSUsStratify and select a Sample of PSUsStratify and Select a Sample of HouseholdThe Future (2010 Census-based Sample D

 Weighting the households (CUs)
History of Sample Redes

 New sample of geographic areas and addres

selected every decade (2010)

 1980 Census-Based Sample Design (1986–1995) 1990 Census-Based Sample Design (1995–2004) 2000 Census-Based Sample Design (2005–p 2010 Census-Based Sample Design (2015–2024?)
Concepts

 Target Population=U.S. non-instit

civilian population

 Consumer Unit

 person or a group of persons in a househ

related by blood, marriage, adoption, or olegal arrangements

OR are unrelated but pool their incomes to 

joint expenditure decisions

Same as households approximately 98% o
Concepts (cont.)

 Sampling Frame – List of Househo

from which we draw our sample

 Unit Frame: Regular households (80%) Area Frame: Rural households (10%) Permit Frame: New construction (9%) Group Quarters: (1%)

 Will Change to Census Bureau’s Maste

Address File (MAF) in 2015 (2010 Censupdates twice per year by US Postal S
Sample Selection – Overv

 Geographic areas are randomly se

to represent the total U.S. 

 Households are randomly selected 

represent the geographic areas

Guiding principle:“Randomness ensures representativeness
Define PSU

PSU: Primary Sampling Unit

– Counties are geographically grouped to

to become units for sample selection

CBSA: Core Based Statistical Areas 

MSA)

– Counties are grouped together into geo

entities called core based statistical are(CBSA’s) by Office of Management and 

– Metropolitan – one or more counties 

centered around urban area of > 50,00people
Define PSU (cont.)

– Micropolitan – one or more counties 

around urban area of 10,000 - 50,000 

Over 3,000 county and county equiv

in the U.S.

Over 900 CBSAs defined by OMB


Selection of PSUs

PSU 

size

SR/ 

NSR

CBSA/

Non-CBSA

Population

Total Example

A SR Metropolitan

(urban)

More Than 2,700,000

A103 Boston A210 Clevela

X NSR Metropolitan

(urban)

Less Than 2,700,000

X486 Denver X218 Cincinna

Y NSR Micropolitan

(urban)

Y104 Ithaca NY426 Newpor

Z NSR Non CBSA

(rural)

Z210 Holmes Z324 Montagu
2000 Census-based Sample Selec

CPI – 75 PSUs; CE – 91 PSUs

PSU Size

Region 

Northeast Midwest South Wes

A 5 4 6 6

X 4 10 16 8 Y 2 4 6 4Z 2 4 6 4Total 13 22 34 2
Georgia PSU Selectio
Metropolitan CBSAs in Geor

CBSA

2000 

Population

P

of 

Augusta-Aiken, GA-SC 523,162 Columbus, GA-AL 274,624 Albany, GA 157,833 Total 955,619 

CBSA

2000 

Population

P

of 

Savannah, GA 293,000 Macon, GA 222,368 Athens, GA 207,668 Warner Robins, GA 134,433 Rome, GA 90,565 Total 948,034 
Metropolitan CBSAs in Geor

CBSA

2000 

Population

P

of 

 Augusta-Aiken, GA-SC 523,162 

Columbus, GA-AL 274,624 Albany, GA 157,833 Total 955,619 

CBSA

2000 

Population

P

of 

Savannah, GA 293,000 Macon, GA 222,368 

 Athens, GA 207,668 

Warner Robins, GA 134,433 Rome, GA 90,565 Total 948,034 
Number of Househol

 Allocate Target Sample to PSUs

Target size: ~7,000 interviewed household

(Based on Finite Budget)

– For Diary Survey per year– For Interview Survey per quarter

6,600 to Households used jointly by CE an

(for CPI cost weight calculations)

– 21 A-size PSUs – 54 X-size and Y-size PSUs

400 to CE Households

– 16 Z-Size PSUs
Number of Households (co

 Target Sample Size

7,000 interviewed households per year (D7,000 interviewed households per quarter 

(Interview, interviews 2-5 only)

 Target Sample Yield

– 14,000 weekly diaries per year (=7,000

– 28,000 quarterly interviews per year 

(=7,0004)
Number of Households (co

 Local Target Sample Size

Allocate 7,000 interviewed househo

individual PSUs, proportional to eacstratum’s population

Minimizes CE’s nationwide variance
Translate Addresses int

Interviewed Household

 80% “eligibility” rate: (most of the mis

20% are unoccupied)

 75% response rate 60% “participation” rate (0.60 = 0.80
Translate Interviewed Househ

into Addresses (con’t)

PSU

Interviewed

households Addre

A102 Philadelphia 169 32A103 Boston 195 28

A109 New York City 220 42A110 NY-Conn suburbs 212 33A111 NJ suburbs 182 29

etc. etc. et

Total 7,000 11,7
Select a Random Sample 

Households (Mechanics

 Sort households from poor to rich base

information from Decennial Census an

Number of people in householdTenure (owner, renter)Market value of home (owners)Monthly rent (renters)
Select a Random Sample 

Households (Con’t)

 Compute the sampling interval for eac Sampling interval = (# addresses in sa

frame) 

 (# addresses in CE sample)

 Typical sampling intervals:

Every 1,000th address (X+Y, Z PSUs)Every 5,000th address (A PSUs)
Select a Random Sample 

Households (Cont.)

 -- D --- I --- D --- I --- D --- I --- D 

-- D --- I --- D --- I --- etc.

 D=Diary, I=Interview Each “D” and “I” has enough sam

cover the next 10 years
The Future

 New Sample Selection

Based on 2010 Census

– Updated Metropolitan/Micropolitan Sta

Area definitions from OMB

– Updated populations used for selection 

probabilities

Currently Going Into Effect (2015)No Currently Released Data
?????

The Future
Weighting Process

 Usable Interviews

Diary Survey

– Good interviews 

• Diary complete enough to be counted

Interview Survey

– Good interviews

• Interviews 2 – 5• Completed enough to be counted
Weighting Process (Con’

 Base Weight (~10,000)

9,999 CUs + Self

 Weighting Control Factor (~1.00)

Apartment Building instead of a Hou

 Non-interview Adjustment Factor 

Type A: Refusal to Participate

 Calibration Adjustment Factor (~1

Adjusts sample estimate to CPS Tot
Weighting Process (Contin

 Final Weight

Base Weight 

* Weighting Control Fa

Non-interview Adjustment Factor 

*

Calibration Adjustment Factor

 ~15,000 to 18,000
Conclusion

 Both Sample Design and Weightin

Work Together to Produce:

Best Estimates of U.S. Expenditures

– Subject to Allotted CE Budget
Any Questions?
Contact Informatio

Brian T. Nix

Mathematical Statistician

www.bls.gov/cex

202-691-6877

Nix.Brian@bls.gov