Accounting for Missing Data 

in the Census Coverage 

Measurement Survey 

Federal Committee on Statistical Methodology Conference 

November, 2013 

Vincent Thomas Mule, Jr. and Scott Konicki 
2 

Census Coverage Measurement 

(CCM) Survey 

• Survey-based evaluation of the 2010 

Census 

• Net coverage of household population 

and housing units 

– Dual system estimation 

• Components of Census Coverage 

– Correct and erroneous enumerations 

– Omissions 
3 

Accounting for Missing Data in 

CCM Estimation 

• Focus on household population 

• Highlight procedures for dual system 

estimation 
4 

Dual System Estimation 

(Shaded in green: 

Can be measured) 

Enumerated in PES ? 

( P Sample ) 

YES NO Total 

Enumerated 

in Census ? 

( E sample ) 

YES N11 N12

 

N1+

NO N21 N22

 

N2+

Total N+1 N+2 N 
5 

Dual System Estimation (cont.) 

Enumerated in PES ? 

( P Sample ) 

YES NO Total 

Enumerated 

in Census ?

( E sample )

YES #2 

Matches

#3 Correct 

Enumerations 

NO

Total #1

P sample

Total 

Population 
6 

 1. Population (P) Sample 

Independent population interview 

 August to October 2010 

Sources of Missing Data: 

• Noninterviews 

• Demographic characteristics 

• Is the rostered person a resident on 

Interview Day? Inclusion status 
7 

Noninterviews 

3.1 percent noninterviews 

Noninterview adjustment procedure 

• Cell-based approach 

• Check: Noninterview < 2 x Interview 

• Spreading to larger aggregates instead of 

collapsing 
8 

Missing Characteristics of 

Population Sample 

Percentages of missing: 

• 2.8% race 

• 2.6% Hispanic origin 

• 6.1% age 

• 1.3% sex 

• 2.5% relationship 
9 

Characteristic Imputation for 

Missing Characteristics 

Census Edit and Imputation System 

• Formed Census-day and Interview-day 

households 

• Imputation of demographic characteristics 

and tenure 
10

Unresolved Population Sample 

Inclusion Status 

Is the person a resident of housing unit on 

Interview Day? 

• 2.9% unresolved 
11

Predictions for Unresolved 

Population Sample Inclusion Status 

• 2010: Logistic regression modeling 

• 2000: Weighted cell means 

• 1990: Logistic regression modeling 
Models for Unresolved 

Population Sample Inclusion Status 

12

Unresolved Group Model

1 Reported Name and 2 Characteristics

Census Day, Seasonal or Other 

address during Interview

All resolved cases

Followup reasons, demographics, 

type of address and others 

Address flags used in model 

2 Reported Name and 2 Characteristics

No address collected

All resolved cases 

Followup reasons, demographics, 

type of address and others 

Address flags not used in model

3 Did not report name and 2 

Characteristics

All resolved cases

Where rostered in interview? 

Residence status 

Housing unit matching 
2. Population Sample 

Match Status 

Does the Population person match to a 

correct enumeration in the census? 

Unresolved Match status in 2010 

13

Mover Status Number of Cases Unresolved Match 

Status

Nonmover 317,000 0.0%*

Outmover 1,200 0.0%*

Inmover 26,400 6.7%
14

Predicted probabilities for 

Unresolved Match Status 

Probability Approach

Pr(Inmover) Weighted average

Pr(Match|Inmover) Logistic regression of resolved 

inmovers

Pr(Match|Not Inmover) Logist regression of resolved 

nonmovers and outmovers
15

3. Census Correct Enumeration 

Status 

• Which census enumerations were correct 

enumerations? 

4.8 percent unresolved enumeration status 
16

Models for Unresolved 

Enumeration Status 

Unresolved Group Model

1 Census Day, Seasonal or Other 

address during Interview 

or

Duplicate found in census 

All resolved cases 

Followup reason, demographics, proxy 

interview and others 

 

Address flags and 

duplication flag used in model 

2 No address collected or duplicate 

found

All resolved cases 

Followup reason,demographics, proxy 

interview and others 

Address flags and 

duplication flag not used in model 
17

Conclusion 

Methods shown today to compensate for: 

• Unit-level missing data 

• Person-level missing data 

Major changes since 2000 

• Logistic regression models 

• Same characteristic system as Census 
18

Thank you 

Thanks to Rachel Bray, Andrew Keller, 

Mark Seiss, Robert Sands and Colt 

Viehdorfer 

Vincent.t.mule.jr@census.gov