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Blending Administrative Data with a Probability Sample of Nonparticipants to Produce 

National Estimates: The NCS-X NIBRS Estimation Project

2020 Federal Committee on Statistical Methodology

Marcus Berzofsky
Background

o The Uniform Crime Reporting (UCR) Program – aggregated by the FBI – 

has been the repository of crimes reported to the police for the last 100 

years 

o Traditionally, police agencies have reported crime in “summary” form –

aggregate monthly counts 

o The FBI is sunsetting “summary reporting system” (SRS) UCR transitioning 

agencies to the National Incident Based Reporting System (NIBRS) 

o NIBRS provides incident-level level information for each reported crime 

o But, law enforcement agencies need to upgrade their record management 

systems in order to submit NIBRS compliant data to the FBI

2
Understanding the Problem: Transitioning to NIBRS

3

By 2018, approximately 8,000 out 

of 18,000 Law Enforcement 

Agencies (LEAs) have transitioned to 

NIBRS. 

Reporting agencies are not 

randomly distributed and skewed 

towards less populated states
4

oRecruiting a probability sample of 400 law enforcement agencies (LEAs) 

who were nonreporting agencies in 2011 to supplement the existing NIBRS 

data 

oAll nonreporting agencies with 750+ police officers were included in these 

400 LEAs 

oCombine data from these 400 agencies with data from the 8,000+ existing 

reporting agencies to produce national estimates 
h = 2

h = 3

...h = 

11

h = 

12

NCS-X 2011

Early Adopters, h = 1 

(LEAs already reporting to NIBRS in 2011)

Eligible NCS-X Universe 

(universe of LEAs eligible to be sampled for NCS-X 2011)

NCS-X 2011

Participating NCS-X 

(LEA sampled for NCS-X 2011 reporting to NIBRS in 2018)

Non-Participating NCS-X 

(LEA sampled for NCS-X 2011 not reporting to NIBRS in 2018)

Late Joiners 

(LEA not sampled for NCS-X that began reporting to NIBRS 

between 2012 and 2018)

Non-Participants 

(LEA not sampled for NCS-X and still not reporting to NIBRS in 

2018)

Stratum

NIBRS Estimation 

Methodology

5
NCS-X Sample Agencies (n=400)

6
NIBRS Estimation Project: Objectives

o Determine the optimal method to 

weight and blend the NCS-X sample 

with “early adopters” and “late joiners” 

to produce representative national 

and state-level estimates 

o Validate the estimation methodology

7
Estimation Approach #1: Top-Down Approach

TOP-DOWN APPROACH 

• Develops national weights first 

• Uses national weights to develop 

subnational estimates

8
Estimation Approach #1: Top-Down Approach (cont.)

• Advantages 

– The intended approach for the NCS-X 

sample

– Most efficient (in terms of precision) 

approach for national estimates

• Disadvantages 

– Agency weights may represent agencies 

in different states making state 

estimates difficult to produce

– Long term, does not lend itself to 

subnational estimation 

9
Top-Down Approach Weighting/Estimation Strategy

10

NIBRS 

Reporters

Large 

Agencies

SelfRepresenting 

Medium & 

Small 

Agencies

2011 NIBRS 

Reporters

2011 NIBRS 

Non-reporters

New NIBRS 

Reporters 

since 2011

328 NCXSample 

Agencies*

*72 out of the 400 NCS-X sample agencies are large agencies (750+ officers). 

Weighting
Estimation Options: Intermediate Approach

INTERMEDIATE APPROACH 

• Begins with a mix of weights designed 

for estimation at different levels of 

geography 

• State-level estimates created where 

able; regional estimates created when 

needed 

• Together state and regional weights can 

produce national estimates

11
Estimation Options: Intermediate Approach (cont.)

• Advantages 

– Provides for state-level estimates sooner 

than top-down approach

– Allows most flexibility with the type of 

subnational areas available for 

estimation

• Disadvantages 

– Weights are not as efficient as top-down 

approach, but more efficient than 

bottom-up approach

– Subnational estimates will be available 

on a flow basis

12
• Weighting by state if coverage 

ratio exceeds 80% 

• For remaining states, produce 

top-down weights 

• Over time, will achieve a 50-

state design which will allow 

for simultaneous estimates at 

the national and state levels

Intermediate Approach Weighting/Estimation Strategy

13

Weighting by State

NIBRS 

Reporters

Large 

Agencies

Selfrepresenting 

Medium & 

Small 

Agencies

NIBRS 

Reporters
Weighting Strategy: Naïve Design-Based

o Advantages 

• Closest strategy to the original sample design 

• Theoretically should have the smallest 

amount of bias 

o Disadvantages 

• Unclear if original sample design still 

applicable given level of late joiners since 

2011 NCS-X sample drawn 

• Will result in estimates with larger variances 

than other strategies

14

SelfRepresenting

Late 

Joiners

SelfRepresenting

Early 

Adopters

Representing 

All Non-joiners

NCS-X 

Sample
Representing 

All Non-joiners

Late 

Joiners

NCS-X 

Sample

Weighting Strategy: Blending New Joiners

o Advantages 

• Helps smooth the weights compared to 

the naïve design-based method

• Since late joiners come from the same 

pool of agencies the NCS-X sample was 

drawn they are more likely to be similar to 

the NCS-X sample agencies than the 

early adopters

o Disadvantages 

• Deviates from the original sample design

• Allowing late joiners to represent nonjoiners may introduce bias in the 

estimates

15

SelfRepresenting

Early 

Adopters
Weighting Strategy: Blending All Reporters

Representing 

All Non-joiners

Early 

Adopters

LateJoiners

NCS-X 

Sample

o Advantages 

• Minimizes the variance in 

estimates

o Disadvantages 

• Deviates from the original 

sample design

• Strategy that is most likely to 

introduce bias into the estimates

16
Crime Statistics Validation

17

o For each blending, weighting, and 

estimation strategy, we tabulated 

estimated crime totals and 

compared against external 

benchmarks 

o Validation Sources: 

• SRS for crime count estimates

• UCR arrest data for arrest count 

estimates

Offense Type

Weighted Total

among NIBRS 

Reporters

SRS Total

among all 

LEAs

Relative 

Bias(%)

Estimate SE RSE Estimate

All Crime

All Violent Crime

 Murder

 Manslaughter

 Rape

 Robbery

 Assault

 Aggravated 

Assault

 Simple Assault

All Property Crime

 Burglary

 Larceny

 Vehicle Theft