Bureau of Justice Statistics Research and Development Series

ASSESSING THE COVERAGE AND RELIABILITY OF 

SUBNATIONAL GEOGRAPHIC IDENTIFIERS IN THE 

NCVS PUBLIC-USE FILE 

Bonnie E. Shook-Sa, MAS

Philip Lee, BS

Marcus Berzofsky, DrPH

RTI International

NCJ 249467

R&DP-2015: 03

NCJ 249467

December 28, 2015

Research papers have been reviewed by the Bureau of Justice Statistics (BJS) to ensure the 

general accuracy of information presented and adherence to confidentiality and disclosure 

standards. This paper is released to inform interested parties of research and to encourage 

discussion. Any opinions and conclusions expressed herein are those of the authors and do 

not necessarily represent the views of BJS and the U.S. Department of Justice.
This document was prepared using federal funds provided by the U.S. Department of 

Justice under cooperative agreement number 2011-NV-CX-K068. The BJS project 

managers were Lynn Langton, BJS Statistician, and Michael Planty, Victimization Unit 

Chief.
iii

CONTENTS

Section Page

Section 1. Background...............................................................................................................1

Section 2. Assessing the Coverage of the NCVS Sample in Generic Areas Defined 

From the Public-Use File..................................................................................................3

2.1 Overview of Coverage Assessment .........................................................................3

2.2 Defining Comparable Areas ....................................................................................3

2.3 Coverage Evaluation................................................................................................9

Section 3. Weighting Evaluation .............................................................................................19

3.1 Overview of Weighting Evaluation .......................................................................19

3.2 Calibration of NCVS Weights in Generic Areas...................................................19

3.3 Comparison of Recalibrated Weights to Original Weights ...................................21

3.4 Determining Appropriate Collapsing and Pooling of Data for Generic 

Area Estimation .....................................................................................................26

3.5 Weight Recommendations.....................................................................................34

Section 4. Evaluation of Generalized Variance Functions in Subnational Areas....................35

4.1 Overview of Generalized Variance Function Evaluation ......................................35

4.2 Comparison of Direct Estimates to GVFs in Subnational Areas...........................35

4.3 Variance Estimation Recommendations................................................................48

Section 5. Findings and Recommendations.............................................................................49

Section 6. References...............................................................................................................50
iv

LIST OF TABLES

Table Page

Table 2.1. Generic area variable definitions ....................................................................................5

Table 2.2a. 2002 National Crime Victimization Survey generic area classifications and 

mappings.........................................................................................................................7

Table 2.2b. 2008 National Crime Victimization Survey generic area classifications and 

mappings.........................................................................................................................7

Table 2.2c. 2012 National Crime Victimization Survey generic area classifications and 

mappings.........................................................................................................................8

Table 2.3a. Comparison of Census and NCVS population estimates by demographic 

characteristics: region × population size × urbanicity ..................................................11

Table 2.3b. Comparison of Census and NCVS population estimates by demographic 

characteristics: region × population size.......................................................................13

Table 2.3c. Comparison of Census and NCVS population estimates by demographic 

characteristics: region × urbanicity...............................................................................15

Table 2.3d. Comparison of Census and NCVS population estimates by demographic 

characteristics: population size × urbanicity.................................................................17

Table 3.1. Characteristics for person-level poststratification model by number of levels.............20

Table 3.2. Characteristics for household-level poststratification model by number of 

levels .............................................................................................................................20

Table 3.3. Population size collapsing options................................................................................26

Table 3.4a. Percentage of victimization rates flagged as unreliable by type of crime: 

Option 1—Crimes reported and not reported to police.................................................28

Table 3.4b. Percentage of victimization rates flagged as unreliable by type of crime: 

Option 1—crimes reported to police.............................................................................29

Table 3.5a. Percentage of victimization rates flagged as unreliable by type of crime: 

Option 2—crimes reported and not reported to police..................................................30

Table 3.5b. Percentage of victimization rates flagged as unreliable by type of crime: 

Option 2—crimes reported to police.............................................................................31

Table 3.6a. Percentage of victimization rates flagged as unreliable by type of crime: 

Option 3—crimes reported and not reported to police..................................................32

Table 3.6b. Percentage of victimization rates flagged as unreliable by type of crime: 

Option 3—crimes reported to police.............................................................................33

Table 4.1. Percentage of estimates for which GVF standard error is less than direct 

variance standard error: Differences by year and generic area type .............................38
v

Table 4.2a. Percentage of estimates for which GVF standard error is less than direct 

variance standard error: Differences by crime type (region × population size × 

urbanicity) .....................................................................................................................40

Table 4.2b. Percentage of estimates for which GVF standard error is less than direct 

variance standard error: Differences by crime type (region × population size)............42

Table 4.2c. Percentage of estimates for which GVF standard error is less than direct 

variance standard error: Differences by crime type (region × urbanicity)....................44

Table 4.2d. Percentage of estimates for which GVF standard error is less than direct 

variance standard error: Differences by crime type (urbanicity × population 

size)...............................................................................................................................46

Table 5.1. Findings and recommendations regarding the coverage and reliability of NCVS

public-use file subnational geographic identifiers ........................................................49
vi

LIST OF FIGURES

Figure Page

Figure 3.1. One-year victimization rates: Calibrated compared with uncalibrated 

estimates........................................................................................................................22

Figure 3.2. Three-year victimization rates: Calibrated compared with uncalibrated 

estimates........................................................................................................................22

Figure 3.3. One-year victimization totals: Calibrated compared with uncalibrated 

estimates........................................................................................................................23

Figure 3.4. Three-year victimization totals: Calibrated compared with uncalibrated 

estimates........................................................................................................................24

Figure 3.5. Relative standard errors of calibrated compared with uncalibrated 

estimates: 1- and 3-year victimization rates..................................................................25

Figure 3.6. Relative standard errors of calibrated compared with uncalibrated 

estimates: 1- and 3-year victimization totals.................................................................25

Figure 4.1. Comparison of GVF to direct variance RSEs: 1-, 3-, and 5-year 

victimization rates.........................................................................................................36

Figure 4.2. Comparison of GVF to direct variance RSEs: 1-, 3-, and 5-year 

victimization totals........................................................................................................37
1

SECTION 1. BACKGROUND

The National Crime Victimization Survey (NCVS), sponsored by the Bureau of Justice 

Statistics (BJS), estimates the incidence and describes the characteristics of criminal 

victimization in the United States. The NCVS has been a rich source of information about 

criminal victimization at the national level since its inception, but subnational estimates would be 

useful in better understanding local crime patterns and trends. As part of its subnational

estimation program, BJS intends to develop generic area typologies based on various geographic, 

social, economic, and demographic characteristics. These generic areas will then represent all 

places that are similar to each other based on the characteristics of interest.

The primary objective of this analysis was to assess the coverage and reliability of the 

NCVS sample in the subnational geographic areas that can be created from the public-use files 

(PUFs): Census region, population size, and urbanicity. Ideally, BJS would like to create generic 

areas based on these three variables and examine patterns and trends in victimization rates across 

these areas. Before releasing generic area estimates, it was necessary to first examine the 

coverage and reliability of the samples in these areas, to determine the most appropriate survey 

weights to use in estimation, and to assess the best variance estimation method for producing 

reliable estimates.

The evaluation was broken into three parts: (1) assessment of coverage, (2) assessment of 

reweighting methods, and (3) assessment of the reliability of the generalized variance function 

(GVF) estimates for generic areas. Section 2 describes the assessment of coverage within generic 

areas formed on the basis of two- and three-variable crosses of Census region, population size, 

and urbanicity. Section 3 discusses the approach for reweighting the NCVS sample within 

generic areas and the analysis of the effects of reweighting on key NCVS estimates and 

precision. It also discusses recommendations on the collapsing of levels within generic areas and 

pooling of data across multiple years to achieve adequate precision. Because GVFs have 

traditionally been used for NCVS estimation but were not designed for use in subnational areas, 

Section 4 assesses the reliability of GVF estimates within generic areas. Section 5 summarizes 
2

key findings across the analysis and provides overall recommendations for calculating estimates 

within the generic areas formed with these three subnational variables.
3

SECTION 2. ASSESSING THE COVERAGE OF THE NCVS SAMPLE IN GENERIC 

AREAS DEFINED FROM THE PUBLIC-USE FILE

2.1 Overview of Coverage Assessment

As proposed by Planty (2012), the three geographic identifiers available on the NCVS 

PUF (Census region, population size, and urbanicity) can be crossed to produce four types of 

generic areas:

• Census region × population size,

• Census region × urbanicity,

• Population size × urbanicity, and

• Census region × population size × urbanicity.

With appropriate collapsing of levels and pooling of data, the sample sizes within these 

generic areas are sufficient to produce estimates with reasonable precision (as further discussed 

in Section 3.4). However, the NCVS was designed to produce exclusively national estimates, 

which means analysis weights were created to produce representative victimization counts, rates, 

and proportions only at the national level without regard for smaller geographic areas. Thus, 

subnational estimates could exhibit systematic bias from undercoverage due to variation in the 

primary sampling units selected within these areas and how the sample was weighted (i.e.,

nonresponse and poststratification adjustments do not control weights at the subnational level). 

The first step in evaluating the reliability of estimates in the four types of generic areas was to 

assess the coverage of the NCVS sample within these areas by comparing weighted distributions 

of key person- and household-level demographic characteristics to external gold-standard control 

totals. This section documents how gold-standard estimates were derived and includes the results 

of the coverage assessment.

2.2 Defining Comparable Areas

To assess the coverage of the NCVS sample within generic areas, gold-standard 

population estimates were identified for comparison to NCVS population estimates. The two 
4

primary sources of local population estimates in the United States are the decennial Census and 

the American Community Survey (ACS). Decennial Censuses before 2010 included both a short 

form questionnaire, which was administered to all households and contained basic demographic 

information, and a long form questionnaire, which was administered to one in six households and 

provided more detailed socioeconomic information about the population. In 2010, only the short 

form was administered to all households, and additional data previously obtained from the long 

form questionnaire are now provided by the ACS. The ACS began in 2005 and produces 1-, 3-, 

and 5-year national and local population estimates.

Both the decennial Census and the ACS provide estimates that can be used to assess the 

coverage of persons and households in generic areas defined by Census region, population size, 

and urbanicity. However, the local area estimates needed to make population size and urbanicity 

classifications are available in the ACS only for 5-year data, limiting the utility of publicly

available ACS data for this evaluation. Five-year ACS data are available starting only in 2010, 

and because these estimates span a 5-year period, they are of limited utility in assessing the 

coverage of the NCVS sample for single-year estimates. For this reason, decennial Census data 

were used for comparisons with NCVS estimates.1

Table 2.1 contains definitions for each of the three subnational identifiers available on the 

NCVS PUF, including the levels and sources used to define each variable. Note that the levels of 

each variable are those used in the coverage evaluation and do not reflect the final collapsing of 

variables recommended for analysis, as discussed in Section 3.4.

 1 In the future, if characteristics such as employment, education, and marital status would be useful in calibration 

models, ACS microdata could be considered for poststratification purposes.
5

Table 2.1. Generic area variable definitions

Generic area 

variable1

PUF 

variable Generic area variable levels Generic area variable sources

Census region V2127B 1 = Northeast

2 = Midwest

3 = South

4 = West

Census region classification (defined at the 

state level)

Population 

size

V2126B 1 = Not in a place

2 = < 10,000

3 = 10,000–49,999

4 = 50,000–99,999

5 = 100,000–249,999

6 = 250,000–499,999

7 = 500,000–999,999

8 = 1,000,000 +

Census place size code

▪ 1990 Census population for the 1996– 2005 NCVS

▪ 2000 Census population for the 2006–

2012 NCVS

Urbanicity V2129 1 = Central or principal city of a 

MSA/CBSA (urban)

2 = In MSA/CBSA, but not in the 

central or principal city (suburban)

3 = Not in an MSA/CBSA (rural)

CBSA/MSA status

▪ 1993 MSA and central city 

classifications for the 1996–2005

NCVS

▪ 2003 CBSA2 and principal city 

classifications for the 2006–2012

NCVS

Notes: CBSA = Core Based Statistical Area; MSA = Metropolitan Statistical Area; NCVS = National Crime 

Victimization Survey; PUF = public-use file.

1 Census region and population size were not included on the PUF until quarter 3 of 1995.

2 Only metropolitan areas are classified as CBSAs; micropolitan areas are considered not in a CBSA.

Census region is classified at the State level and is thus fixed over time, but population 

size and urbanicity are redefined periodically on the basis of population growth in local areas 

(Census places and Metropolitan Statistical Areas [MSAs]/Core Based Statistical Areas 

[CBSAs]). The 1996–2005 NCVS samples are based on the 1990 Census and use 1993 MSA 

classifications, and the 2006–2012 samples are based on the 2000 Census and use 2003 CBSA 

classifications.2 This means that the population size and urbanicity classifications for the NCVS 

sample are fixed for a 10-year period regardless of the growth in that area during that period or 

any updates made to MSA/CBSA classifications during that time.

 2 Years in the phase-in/phase-out periods contain some cases based on the prior years’ design and others based on 

the new design, and therefore have variables that are defined with multiple Census definitions.
6

Whereas geographic variables are assigned to NCVS respondents on the basis of the 

Census used to develop the primary sampling units during sampling, NCVS weights are 

calibrated based on the most recent Census data available. Therefore, calculating comparable 

Census population estimates in generic areas requires a two-step process:

1. Map the generic area classifications from the decennial Census used to select the 

sample to the most recent decennial Census data available for the calculation of 

population estimates; and

2. Using intercensal population projections, adjust decennial Census estimates to 

account for population changes between the most recent decennial Census data and 

the year of estimation.

This process varies depending on the decennial Census used to select the sample, the 

most recent decennial Census data year, and the number of years between the most recent 

decennial Census data year and the year of estimation. The coverage of the NCVS sample and 

ultimately the estimates themselves need to be assessed for multiple time periods to fully 

understand how well estimates within generic areas perform, so the coverage evaluation included 

three NCVS years that vary on the basis of the above characteristics:

1. A year that is based on the 1990 decennial Census but occurs after the 2000 decennial 

Census (2002);

2. A year that is based on the 2000 decennial Census but occurs before the 2010 

decennial Census (2008); and

3. A year that is based on the 2000 decennial Census but occurs after the 2010 decennial 

Census (2012).

Comparable Census estimates for these 3 years were calculated by applying the two-step 

process outlined above. First, generic area variables were mapped from the decennial Census 

used to select the NCVS sample to the most recent decennial Census population totals available. 

Each of the three generic area variables is assigned at a different geographic level, so each was 

mapped to the most recent decennial Census using a different geographic crosswalk. The 
7

assignment level of each variable, the mapping years, and the crosswalks used to map geographic 

variables across decennial Census years are outlined in Tables 2.2a–2.2c for the 2002, 2008, and 

2012 estimates, respectively.

Table 2.2a. 2002 National Crime Victimization Survey generic area classifications and 

mappings 

Generic area 

variable Assignment level Mapping Crosswalks used

Census region State 1990–2000 N/A; Census region definitions did 

not change from 1990 to 2000

Population size Census place 1990–2000 RTI-created 1990 to 2000 Census 

place crosswalk1

Urbanicity MSAs: county or county subdivision

Central cities: Census place

1990–2000 Publicly available 1990 to 2000 

county crosswalk2

RTI-created 1990 to 2000 county 

subdivision crosswalk1

RTI-created 1990 to 2000 Census 

place crosswalk1

Note: MSA = Metropolitan Statistical Area.

1 RTI developed a crosswalk from 1990 to 2000 places and county subdivisions by overlaying the Census place and 

county subdivision shape files from the 1990 Census onto the 2000 Census blocks and assigning the 

Census place and county subdivision associated with the centroid of each 2000 Census block.

2 Available from https://www.census.gov/geo/reference/county-changes.html

Table 2.2b. 2008 National Crime Victimization Survey generic area classifications and 

mappings

Generic area 

variable Assignment level Mapping1 Crosswalks used

Census region State N/A N/A 

Population size Census place N/A N/A

Urbanicity CBSAs: county

Principal cities: Census place

N/A N/A

Note: CBSA = Core Based Statistical Area.

1 For the 2008 NCVS, the decennial Census used during sampling is the most recent decennial Census data 

available, so no mapping is required.
8

Table 2.2c. 2012 National Crime Victimization Survey generic area classifications and 

mappings

Generic area 

variable Assignment level Mapping Crosswalks used

Census region State 2000–2010 N/A; Census region definitions did 

not change from 2000 to 2010

Population size Census place 2000–2010 Publicly available 2000 to 2010 

Census place crosswalk1

Urbanicity CBSAs: county

Principal cities: Census place 

2000–2010 Publicly available 2000 to 2010 

county crosswalk2

Publicly available 2000 to 2010 

Census place crosswalk1

Note: CBSA = Core Based Statistical Area.

1 Available from https://www.census.gov/geo/maps-data/data/comp-place.html

2 Available from https://www.census.gov/geo/reference/county-changes.html

After generic areas were mapped from the decennial Census that was used to select the 

sample to the most recent decennial Census for obtaining estimates (step 1), decennial Census 

population totals were adjusted to account for population changes between the most recent 

decennial Census data available and the year of estimation (step 2). Adjustment factors were 

calculated for each estimation year by taking the ratio of the intercensal annual projected 

population counts for households and persons at the Census region level3 (PROJHH and 

PROJPER) to decennial estimated population counts of households and persons from the most 

recent Census (HH and PER). The household and person adjustment factors for Census region i, 

estimation year j, and decennial Census reference year k, are

HHHHHHHHHHiiiiii = PPRRRRRRRRRRiiii

iiii

and PPPPPP iiiiii = PPRRRRRR RRRRiiii

PP iiii

The decennial Census reference year is the most recent Census year associated with the 

estimation year (i.e., 2000 for the 2002 and 2008 estimation years and 2010 for the 2012 

estimation year). These adjustment factors inflate the estimates from the decennial Census 

reference year up to the estimation year and were applied to household- and person-level 

demographic characteristics to obtain the gold-standard Census population totals in each generic 

area for comparison with the NCVS estimates.

 3 Available from https://www.census.gov/popest/data/historical/index.html
9

After the implementation of step 1 and step 2 as described above, Census estimates were 

calculated within generic areas that were comparable to the NCVS generic areas. Estimated 

totals and population distributions were calculated from the Census for the person- and 

household-level demographic characteristics listed below for 2002, 2008, and 2012. These 

characteristics were available in each decennial Census short form.

Person-level characteristics:

• Gender

• Age category

• Race/ethnicity

• Number of persons in the household

• Household tenure

Household-level characteristics:

• Age of householder

• Race/ethnicity of householder

• Number of persons in the household

• Household tenure

2.3 Coverage Evaluation

To assess how well the NCVS population totals in generic areas aligned with the true 

gold-standard Census population totals, NCVS population estimates were calculated in the 114 

generic areas4 for the same set of person- and household-level characteristics listed in 

Section 2.2.

Within each of the 114 generic areas, NCVS population estimates were compared to 

Census gold-standard estimates. For each person- and household-level demographic 

characteristic analyzed, significance testing was conducted to determine whether the NCVS and 

 4 The 114 generic areas comprised 56 region × population size × urbanicity areas, 32 region × population size areas, 

12 region × urbanicity areas, and 14 urbanicity × population size areas. The number of generic areas is not 

always equal to the total number of combinations of the geographic variables because some variable 

combinations do not exist in the population (e.g., urban areas that are not in a Census place).
10

Census estimated totals were significantly different at the 95 percent confidence level. These 

significance tests assumed that the NCVS and Census estimates are independent and that the 

Census estimates have no variation. Tables 2.3a–2.3d summarize the number of statistically 

significant differences across each of the two- or three-variable generic area groups by 

demographic characteristic.

In general, the NCVS estimates align well with the Census estimates. However, there 

were a nonnegligible number of statistically significant differences across all four types of 

generic areas. The differences vary slightly for the three survey years and vary based on the 

demographic characteristic of interest (e.g., householder age tends to align well, whereas the 

number of persons in the household does not align as well). In addition, differences were not 

uniform across generic area types. Census region × urbanicity NCVS totals align better with 

Census estimates compared with the other three types of generic areas.

Because the differences between the NCVS estimated population demographic 

distributions and the Census estimates in the generic areas were statistically significant for a 

nonnegligible number of comparisons, further assessment was needed to determine whether 

estimates produced with the original NCVS weights would exhibit bias. Differences in 

population totals do not necessarily indicate that NCVS estimates calculated with the original 

NCVS weights will be biased. Slight differences in the population distributions do not 

necessarily equate to differences in resulting victimization totals and rates. Further analysis was 

needed to determine whether and how calibrating NCVS weights to Census control totals within 

generic areas would affect key estimates.
11

Table 2.3a. Comparison of Census and NCVS population estimates by demographic 

characteristics: region × population size × urbanicity

2002 2008 2012

Num 

diff.

a Pct diff.

Num 

diff.

a Pct diff.

Num 

diff.

a Pct diff.

Person-level estimates

All persons 12+ 9 16.1% 8 14.3% 11 19.6%

Gender

Male 8 14.3% 7 12.5% 10 17.9%

Female 7 12.5% 8 14.3% 7 12.5%

Age

12–15 2 3.6% 7 12.5% 7 12.5%

16–19 9 16.1% 11 19.6% 7 12.5%

20–24 6 10.7% 8 14.3% 5 8.9%

25–34 13 23.2% 8 14.3% 8 14.3%

35–49 7 12.5% 9 16.1% 10 17.9%

50–64 0 0.0% 13 23.2% 7 12.5%

65 or older 7 12.5% 12 21.4% 4 7.1%

Race

Whiteb 7 12.5% 5 8.9% 12 21.4%

Blackb 11 19.6% 10 17.9% 10 17.9%

Otherc 52 92.9% 34 60.7% 38 67.9%

Ethnicity

Hispanic 35 62.5% 21 37.5% 32 57.1%

Non-Hispanic 8 14.3% 5 8.9% 14 25.0%

Number of persons in the household

1 56 100.0% 54 96.4% 18 32.1%

2 24 42.9% 24 42.9% 10 17.9%

3+ 36 64.3% 32 57.1% 13 23.2%

Household tenure

Own 3 5.4% 2 3.6% 7 12.5%

Rent/no cash rent 13 23.2% 13 23.2% 7 12.5%

(continued)
12

Table 2.3a. Comparison of Census and NCVS population estimates by demographic 

characteristics: region × population size × urbanicity (continued)

2002 2008 2012

Num 

diff.

a Pct diff.

Num 

diff.

a Pct diff.

Num 

diff.

a Pct diff.

Household-level estimates

All households 4 7.1% 5 8.9% 9 16.1%

Age of householder

Younger than 25 5 8.9% 8 14.3% 14 25.0%

25–34 2 3.6% 3 5.4% 9 16.1%

35–44 4 7.1% 12 21.4% 7 12.5%

45–54 0 0.0% 8 14.3% 5 8.9%

55–64 2 3.6% 16 28.6% 6 10.7%

65 or older 6 10.7% 11 19.6% 6 10.7%

Race/ethnicity of householder

Whiteb 4 7.1% 7 12.5% 5 8.9%

Blackb 4 7.1% 7 12.5% 10 17.9%

Hispanic 11 19.6% 14 25.0% 12 21.4%

Other/more than one raceb,c 21 37.5% 6 10.7% 9 16.1%

Number of persons in the household

1 0 0.0% 9 16.1% 15 26.8%

2 1 1.8% 5 8.9% 12 21.4%

3+ 9 16.1% 12 21.4% 10 17.9%

Household tenure

Own 2 3.6% 3 5.4% 9 16.1%

Rent/no cash rent 5 8.9% 8 14.3% 8 14.3%

Note: NCVS = National Crime Victimization Survey.

a Number of areas (of 56 total) where NCVS and Census estimated totals are statistically significant at the 95% 

confidence level.

b Excludes those of Hispanic origin.

c Includes Asian, Native Hawaiian, Other Pacific Islander, American Indian, Alaska Native, and persons with two or 

more races.
13

Table 2.3b. Comparison of Census and NCVS population estimates by demographic 

characteristics: region × population size

2002 2008 2012

Num 

diff.

a Pct diff.

Num 

diff.

a Pct diff.

Num 

diff.

a Pct diff.

Person-level estimates

All persons 12+ 25.0% 6 18.8% 7 21.9%

Gender

Male 7 21.9% 4 12.5% 6 18.8%

Female 6 18.8% 6 18.8% 5 15.6%

Age

12–15 1 3.1% 2 6.3% 3 9.4%

16–19 4 12.5% 4 12.5% 3 9.4%

20–24 2 6.3% 3 9.4% 3 9.4%

25–34 10 31.3% 6 18.8% 4 12.5%

35–49 6 18.8% 5 15.6% 8 25.0%

50–64 1 3.1% 11 34.4% 4 12.5%

65 or older 7 21.9% 9 28.1% 3 9.4%

Race

Whiteb 5 15.6% 5 15.6% 11 34.4%

Blackb 3 9.4% 7 21.9% 3 9.4%

Otherb,c 30 93.8% 23 71.9% 26 81.3%

Ethnicity

Hispanic 24 75.0% 14 43.8% 21 65.6%

Non-Hispanic 5 15.6% 4 12.5% 10 31.3%

Number of persons in the household

1 32 100.0% 32 100.0% 19 59.4%

2 14 43.8% 18 56.3% 8 25.0%

3+ 29 90.6% 29 90.6% 12 37.5%

Household tenure

Own 3 9.4% 1 3.1% 4 12.5%

Rent/no cash rent 10 31.3% 10 31.3% 7 21.9%

(continued)
14

Table 2.3b. Comparison of Census and NCVS population estimates by demographic 

characteristics: region × population size (continued)

2002 2008 2012

Num 

diff.

a Pct diff.

Num 

diff.

a Pct diff.

Num 

diff.

a Pct diff.

Household-level estimates

All households 15.6% 5 15.6% 9 28.1%

Age of householder

Younger than 25 2 6.3% 4 12.5% 12 37.5%

25–34 2 6.3% 3 9.4% 6 18.8%

35–44 3 9.4% 11 34.4% 3 9.4%

45–54 1 3.1% 7 21.9% 4 12.5%

55–64 2 6.3% 12 37.5% 3 9.4%

65 or older 6 18.8% 8 25.0% 7 21.9%

Race/ethnicity of householder

Whiteb 3 9.4% 5 15.6% 4 12.5%

Blackb 2 6.3% 5 15.6% 6 18.8%

Hispanic 6 18.8% 11 34.4% 6 18.8%

Other/more than one raceb,c 14 43.8% 3 9.4% 2 6.3%

Number of persons in the household

1 0 0.0% 9 28.1% 14 43.8%

2 2 6.3% 6 18.8% 8 25.0%

3+ 7 21.9% 11 34.4% 7 21.9%

Household tenure

Own 2 6.3% 5 15.6% 7 21.9%

Rent/no cash rent 3 9.4% 6 18.8% 7 21.9%

Note: NCVS = National Crime Victimization Survey.

a Number of areas (of 56 total) where NCVS and Census estimated totals are statistically significant at the 95% 

confidence level.

b Excludes those of Hispanic origin.

c Includes Asian, Native Hawaiian, Other Pacific Islander, American Indian, Alaska Native, and persons with two or 

more races.
15

Table 2.3c. Comparison of Census and NCVS population estimates by demographic 

characteristics: region × urbanicity

2002 2008 2012

Num 

diff.

a Pct diff.

Num 

diff.

a Pct diff.

Num 

diff.

a Pct diff.

Person-level estimates

All persons 12+ 0.0% 1 8.3% 0 0.0%

Gender

Male 0 0.0% 2 16.7% 0 0.0%

Female 0 0.0% 1 8.3% 0 0.0%

Age

12–15 0 0.0% 1 8.3% 1 8.3%

16–19 1 8.3% 1 8.3% 1 8.3%

20–24 1 8.3% 1 8.3% 0 0.0%

25–34 3 25.0% 1 8.3% 0 0.0%

35–49 0 0.0% 4 33.3% 1 8.3%

50–64 0 0.0% 7 58.3% 0 0.0%

65 or older 3 25.0% 1 8.3% 0 0.0%

Race

Whiteb 3 25.0% 1 8.3% 3 25.0%

Blackb 2 16.7% 3 25.0% 1 8.3%

Otherb,c 11 91.7% 7 58.3% 10 83.3%

Ethnicity

Hispanic 11 91.7% 5 41.7% 11 91.7%

Non-Hispanic 1 8.3% 0 0.0% 3 25.0%

Number of persons in the household

1 12 100.0% 12 100.0% 8 66.7%

2 4 33.3% 6 50.0% 5 41.7%

3+ 8 66.7% 10 83.3% 6 50.0%

Household tenure

Own 0 0.0% 1 8.3% 0 0.0%

Rent/no cash rent 3 25.0% 2 16.7% 1 8.3%

(continued)
16

Table 2.3c. Comparison of Census and NCVS population estimates by demographic 

characteristics: region × urbanicity (continued)

2002 2008 2012

Num 

diff.

a Pct diff.

Num 

diff.

a Pct diff.

Num 

diff.

a Pct diff.

Household-level estimates

All households 0.0% 0 0.0% 2 16.7%

Age of householder

Younger than 25 7 58.3% 5 41.7% 8 66.7%

25–34 0 0.0% 0 0.0% 5 41.7%

35–44 0 0.0% 7 58.3% 0 0.0%

45–54 0 0.0% 2 16.7% 0 0.0%

55–64 1 8.3% 7 58.3% 0 0.0%

65 or older 0 0.0% 0 0.0% 0 0.0%

Race/ethnicity of householder

Whiteb 0 0.0% 0 0.0% 0 0.0%

Blackb 3 25.0% 4 33.3% 7 58.3%

Hispanic 4 33.3% 7 58.3% 5 41.7%

Other/more than one raceb,c 11 91.7% 3 25.0% 1 8.3%

Number of persons in the household

1 0 0.0% 6 50.0% 8 66.7%

2 1 8.3% 4 33.3% 6 50.0%

3+ 0 0.0% 2 16.7% 0 0.0%

Household tenure

Own 0 0.0% 2 16.7% 1 8.3%

Rent/no cash rent 1 8.3% 0 0.0% 1 8.3%

Note: NCVS = National Crime Victimization Survey.

a Number of areas (of 56 total) where NCVS and Census estimated totals are statistically significant at the 95% 

confidence level.

b Excludes those of Hispanic origin.

c Includes Asian, Native Hawaiian, Other Pacific Islander, American Indian, Alaska Native, and persons with two or 

more races.
17

Table 2.3d. Comparison of Census and NCVS population estimates by demographic 

characteristics: population size × urbanicity

2002 2008 2012

Num 

diff.

a Pct diff.

Num 

diff.

a Pct diff.

Num 

diff.

a Pct diff.

Person-level estimates

All persons 12+ 28.6% 2 14.3% 1 7.1%

Gender

Male 3 21.4% 2 14.3% 1 7.1%

Female 4 28.6% 2 14.3% 1 7.1%

Age

12–15 0 0.0% 2 14.3% 1 7.1%

16–19 1 7.1% 2 14.3% 1 7.1%

20–24 0 0.0% 0 0.0% 1 7.1%

25–34 5 35.7% 3 21.4% 3 21.4%

35–49 3 21.4% 4 28.6% 3 21.4%

50–64 0 0.0% 5 35.7% 1 7.1%

65 or older 4 28.6% 5 35.7% 1 7.1%

Race

Whiteb 3 21.4% 2 14.3% 5 35.7%

Blackb 0 0.0% 4 28.6% 1 7.1%

Otherb,c 14 100.0% 13 92.9% 14 100.0%

Ethnicity

Hispanic 10 71.4% 9 64.3% 12 85.7%

Non-Hispanic 3 21.4% 2 14.3% 5 35.7%

Number of persons in the household

1 14 100.0% 14 100.0% 10 71.4%

2 8 57.1% 7 50.0% 4 28.6%

3+ 12 85.7% 13 92.9% 5 35.7%

Household tenure

Own 1 7.1% 1 7.1% 1 7.1%

Rent/no cash rent 5 35.7% 3 21.4% 1 7.1%

(continued)
18

Table 2.3d. Comparison of Census and NCVS population estimates by demographic 

characteristics: population size × urbanicity (continued)

2002 2008 2012

Num 

diff.

a Pct diff.

Num 

diff.

a Pct diff.

Num 

diff.

a Pct diff.

Household-level estimates

All households 0.0% 1 7.1% 4 28.6%

Age of householder

Younger than 25 6 42.9% 5 35.7% 8 57.1%

25–34 2 14.3% 0 0.0% 4 28.6%

35–44 1 7.1% 9 64.3% 1 7.1%

45–54 0 0.0% 2 14.3% 1 7.1%

55–64 0 0.0% 7 50.0% 4 28.6%

65 or older 1 7.1% 2 14.3% 2 14.3%

Race/ethnicity of householder

Whiteb 1 7.1% 2 14.3% 3 21.4%

Blackb 2 14.3% 2 14.3% 5 35.7%

Hispanic 3 21.4% 7 50.0% 6 42.9%

Other/more than one raceb,c 12 85.7% 1 7.1% 0 0.0%

Number of persons in the household

1 2 14.3% 4 28.6% 7 50.0%

2 2 14.3% 3 21.4% 5 35.7%

3+ 5 35.7% 4 28.6% 3 21.4%

Household tenure

Own 1 7.1% 2 14.3% 4 28.6%

Rent/no cash rent 1 7.1% 0 0.0% 3 21.4%

Note: NCVS = National Crime Victimization Survey.

a Number of areas (of 56 total) where NCVS and Census estimated totals are statistically significant at the 95% 

confidence level.

b Excludes those of Hispanic origin.

c Includes Asian, Native Hawaiian, Other Pacific Islander, American Indian, Alaska Native, and persons with two or 

more races.
19

SECTION 3. WEIGHTING EVALUATION

3.1 Overview of Weighting Evaluation

On the basis of the results of the coverage analysis, NCVS weights were calibrated to 

Census control totals at the generic area level, and key victimization rates and totals were 

calculated both with the original NCVS weights and the calibrated weights. Comparisons were 

made between the two sets of weights based on the estimates themselves and the precision of 

estimates. Calibrating the NCVS weights to Census control totals should minimize potential bias 

in estimates at the generic-area level. However, because calibration models often increase the 

variation in analysis weights, calibrated estimates are likely to be less precise than estimates 

based on the original NCVS weights. The weight calibration approach and the results of these 

comparisons are discussed in the sections below. In addition, this section compares the precision 

of single- and pooled-year estimates, evaluates the effects of collapsing within generic area 

levels on resulting precision, and makes recommendations for the most appropriate weighting 

approach for generic area estimation.

3.2 Calibration of NCVS Weights in Generic Areas

For each of the four generic area types, weight calibration models were fit for the 

household- and person-level NCVS data files for each analysis year on the basis of the set of 

characteristics that are commonly defined in the NCVS and the Census (those used in the 

coverage comparison tables). Because the gold-standard Census estimates were designed to 

accurately represent each generic area, these population counts can be used as control totals in 

the poststratification models. Table 3.1 lists the characteristics that were used for person-level 

models, and Table 3.2 lists the characteristics that were used for household-level models. The set 

of characteristics used in the person-level models includes both person- and household-level 

characteristics. The set of characteristics for the household-level models includes characteristics 

about the householder (i.e., the reference person in the NCVS) and the household.
20

Table 3.1. Characteristics for person-level poststratification model by number of levels

Characteristic Number of levels

Gender of respondent 2

Age category of respondent 7

Race of respondent 3

Ethnicity of respondent 2

Number of persons in the household 3

Household tenure 2

Table 3.2. Characteristics for household-level poststratification model by number of 

levels

Characteristic Number of levels

Age of householder 6

Race/ethnicity of householder 4

Household tenure 2

Number of persons in the household 3

To ensure that control totals were maintained for each generic area within each type (e.g., 

totals for Northeast/Urban area were maintained within the Census region × urbanicity model), 

calibration models included a variable specifying each generic area and the interactions between 

this variable and all demographic variables.

Calibration models were fit using generalized exponential modeling (GEM; available in 

SUDAAN’s “WTADJUST” procedure) for single-year estimates (2002, 2008, and 2012) as well 

as 3-year pooled estimates (2000–2002, 2006–2008, and 2010–2012). For the 2002 and 2000–

2002 models, the race of the respondent was excluded from the person-level models because the 

NCVS and Census population estimates were not defined comparably.

5 However, no further 

collapsing or removal of variables was required for the calibration models to converge.

 5 The 2000 Census classified respondents into race categories that included a “multiple races” option. The NCVS 

instrument for 2000–2002 did not allow respondents to select multiple races. For this reason, the Census and 

NCVS distributions by race are not comparable.
21

3.3 Comparison of Recalibrated Weights to Original Weights

After the original NCVS weights were calibrated to Census control totals for each generic 

area and analysis year, key person- and household-level victimization rates and totals (listed 

below) were calculated overall and for crimes reported to the police:

• All crime • Violent crimes involving a weapon

• Violent crime • Violent crimes involving a firearm

• Serious violent crime • Violent crimes committed by a stranger

• Rape/sexual assault • Violent crimes committed by an intimate

• Robbery • Violent crimes committed by other relative

• Assault • Violent crimes committed by other known offender

• Aggravated assault • Violent crimes occurring during the day

• Simple assault • Violent crimes occurring at night

• Personal theft

• Property crime

• Household burglary

• Motor vehicle theft

• Theft

Estimates were computed two ways: (1) on the basis of the original NCVS weights 

(uncalibrated estimates) and (2) on the basis of the generic-area specific adjusted weights 

(calibrated estimates). For each crime type, the estimates and relative standard errors (RSEs) 

were calculated using both sets of weights. The calibrated and uncalibrated victimization rates 

for all generic areas are compared in Figures 3.1 and 3.2 for 1- and 3-year estimates, 

respectively. The figures on the left contain all rates computed (11,868 comparisons), whereas

the figures on the right are limited to rates that were not flagged as unreliable on the basis of the 

sample size and RSE6 (4,109 and 6,607 comparisons for 1- and 3-year estimates, respectively). 

For both 1- and 3-year estimates, calibrated and uncalibrated rates align nicely along the 

45-degree line of equality, particularly when the estimates compared are limited to those with 

 6 Estimates based on 10 or fewer victimizations or with RSEs greater than 30% are flagged as unreliable.
22

Figure 3.1. One-year victimization rates: Calibrated compared with uncalibrated 

estimates

Figure 3.2. Three-year victimization rates: Calibrated compared with uncalibrated 

estimates
23

reasonable precision (i.e., reliable estimates). This provides evidence that weight calibration to 

Census control totals within generic areas did not lead to substantive differences in key NCVS 

victimization rates.

Similarly, the calibrated and uncalibrated victimization totals for all generic areas are 

compared in Figures 3.3 and 3.4 for 1- and 3-year estimates, respectively. The figures on the left 

contain all totals (11,868 comparisons), whereas the figures on the right are limited to totals that 

were not flagged as unreliable (3,665 and 5,866 comparisons for 1- and 3-year estimates, 

respectively). As with the victimization rates, for both 1- and 3-year estimates, calibrated and 

uncalibrated totals align nicely along the 45-degree line of equality, providing further evidence 

that weight calibration to Census control totals within generic areas did not lead to substantive 

differences in key NCVS estimates.

Figure 3.3. One-year victimization totals: Calibrated compared with uncalibrated 

estimates
24

Figure 3.4. Three-year victimization totals: Calibrated compared with uncalibrated 

estimates

The impact of calibrating the NCVS weights on the precision of key victimization rates 

and totals was also assessed. As previously noted, calibrating estimates often adds variation to 

analysis weights, which makes resulting estimates less precise. The precision of calibrated and 

uncalibrated estimates for all generic areas is compared in Figures 3.5 and 3.6 for victimization 

rates and totals, respectively. These figures plot the RSEs for calibrated estimates against the 

RSEs for the corresponding uncalibrated estimates for 1- and 3-year periods, but they are limited 

to estimates for which both the calibrated and uncalibrated estimates had RSEs of less than 50 

percent (8,076 and 9,707 comparisons for 1- and 3-year rates, respectively; 7,786 and 9,397 

comparisons for 1- and 3-year totals, respectively), as estimates with RSEs above 50 percent are 

not reliable and do not provide useful comparisons.

For both 1- and 3-year estimates and for both victimization rates and totals, the majority 

of comparisons fall below the 45-degree line of equality, indicating that calibrated estimates have 

higher RSEs than uncalibrated estimates. For victimization rates, 55 percent of 1-year 

comparisons and 65 percent of 3-year comparisons fall below the 45-degree line, whereas for 

victimization totals, 54 percent of 1-year comparisons and 63 percent of 3-year comparisons fall 

below the 45-degree line. Thus, the calibration process resulted in a loss of precision for key 

NCVS estimates while not substantively affecting the estimates themselves.
25

Figure 3.5. Relative standard errors of calibrated compared with uncalibrated estimates: 

1- and 3-year victimization rates

Figure 3.6. Relative standard errors of calibrated compared with uncalibrated estimates: 

1- and 3-year victimization totals
26

3.4 Determining Appropriate Collapsing and Pooling of Data for Generic Area 

Estimation

In addition to assessing the best analytic weights to use within generic areas, the RTI 

team evaluated the best approach for defining generic areas and pooling data across years to 

achieve precise but timely estimates within generic areas. One-year estimates provide the 

timeliest data within generic areas, but they are less precise than 3- or 5-year estimates because 

of smaller annual sample sizes. Similarly, collapsing levels within generic areas provides added 

precision, particularly for domains with small sample sizes. During the weighting evaluation, 

several of the generic areas had low levels of precision for key estimates, especially those 

defined within the Census region × population size × urbanicity generic area type. To improve 

precision levels across all generic areas such that key estimates would have reasonable precision, 

both variable collapsing and pooling of data across years were explored.

Because the population size variable has the most levels and can be collapsed into 

categories that are reasonable from a substantive perspective (i.e., it is not reasonable to collapse 

across Census regions or urbanicity categories), three potential collapsing options for population 

size were evaluated. The first option collapses population size into 6 levels that are consistent 

with the NCVS Victimization Analysis Tool (NVAT) and leads to a total of 40 Census region × 

population size × urbanicity categories. The second option further collapses the middle levels of 

population size, leading to 28 total Census region × population size × urbanicity categories. The 

third option instead collapses the highest levels of population size, leading to 32 total Census 

region × population size × urbanicity categories. These options were selected to ameliorate 

precision concerns for the levels of population size that had the lowest levels of precision in the 

weighting evaluation. The three collapsing options considered are presented in Table 3.3.

Table 3.3. Population size collapsing options

Option 1 Option 2 Option 3

Not in a place Not in a place Not in a place

<100,000 <100,000–249,999 <100,000

100,000–249,999 250,000–999,999 100,000–499,999

250,000–499,999 1,000,000+ 500,000+

500,000–999,999

1,000,000+ 
27

In addition to considering the three collapsed versions of population size, the RTI team

also evaluated the effects of pooling 3 and 5 years of data to calculate victimization rates within 

generic areas. The percentage of 1-, 3-, and 5-year estimates that would be flagged as unreliable 

on the basis of sample sizes and percent RSEs was calculated for each of the three collapsing 

options. Tables 3.4a and 3.4b present the percentage of estimates that would be flagged as 

unreliable on the basis of collapsing option 1 for all crimes and for crimes reported to police, 

respectively. Tables 3.5a and 3.5b present similar estimates for collapsing option 2, and 

Tables 3.6a and 3.6b present estimates for collapsing option 3. All tables include 2002, 2008, 

and 2012 estimates for 1-year periods; 2000–2002, 2006–2008, and 2010–2012 estimates for 3-

year periods; and 1998–2002, 2004–2008, and 2008–2012 for 5-year periods.

Both four-level versions of population size (collapsing options 2 and 3) led to 

significantly fewer flagged estimates than the six-level NVAT version (collapsing option 1). 

Collapsing option 3 led to more reliable estimates for the Census region × population size 

generic areas, and collapsing option 2 led to more reliable estimates for the Census region × 

population size × urbanicity generic areas (most likely because it leads to fewer levels of this 

variable). Because the Census region × population size × urbanicity generic areas were the most 

problematic in terms of precision, it is recommended that collapsing option 2 be implemented 

because it provides the most precision gains for the most problematic generic area type.

The appropriate number of years of data to pool is dependent on the key estimate and the 

generic area types included in the analysis. In general, 3-year pooled estimates have reasonable 

precision for most types of crime for the two-variable generic area types (Census region × 

population size, Census region × urbanicity, and population size × urbanicity). Pooling 5 years of 

data is required to obtain reasonable precision for many crime types for the Census region × 

population size × urbanicity generic areas. However, even after collapsing the population size 

variable and pooling 5 years of data, some of the rarest crime types (e.g., rape and sexual assault, 

personal theft, violent crimes committed by other relatives) do not have adequate precision for 

the majority of generic areas and would require further pooling of years or further collapsing of 

generic area levels to produce estimates with reasonable quality.
28

Table 3.4a. Percentage of victimization rates flagged as unreliable by type of crime: Option 1—Crimes reported and not 

reported to police

1-year 3-year 5-year

Type of crime

Reg. ×

pop. 

size

Reg. ×

urbanicity

Urbanicity ×

pop. 

size

Reg. ×

pop. 

size ×

urbanicity

Reg. ×

pop. 

size

Reg. ×

urbanicity

Urbanicity ×

pop. 

size

Reg. ×

pop. 

size ×

urbanicity

Reg. ×

Pop. 

Size

Reg. ×

urbanicity

Urbanicity ×

pop. 

size

Reg. ×

pop. 

size ×

urbanicity

All crime 1.4 0.0 0.0 7.5 0.0 0.0 0.0 4.2 0.0 0.0 0.0 1.7

Violent crime 27.8 8.3 3.3 43.3 11.1 5.6 0.0 17.5 4.2 2.8 0.0 11.7

Serious violent crime 59.7 30.6 23.3 78.3 26.4 5.6 0.0 39.2 11.1 2.8 0.0 21.7

Rape/sexual assault 100.0 97.2 100.0 100.0 94.4 63.9 56.7 96.7 83.3 47.2 36.7 89.2

Robbery 86.1 69.4 43.3 97.5 45.8 30.6 20.0 67.5 33.3 22.2 10.0 54.2

Assault 34.7 5.6 3.3 49.2 15.3 5.6 0.0 23.3 8.3 2.8 0.0 15.8

Aggravated 86.1 55.6 40.0 94.2 41.7 8.3 0.0 55.0 22.2 11.1 3.3 33.3

Simple 44.4 11.1 3.3 57.5 19.4 8.3 0.0 27.5 11.1 2.8 0.0 17.5

Personal theft 100.0 97.2 90.0 100.0 83.3 44.4 56.7 93.3 66.7 36.1 36.7 82.5

Property crime 2.8 0.0 0.0 7.5 0.0 0.0 0.0 2.5 0.0 0.0 0.0 0.8

Household burglary 20.8 2.8 0.0 36.7 8.3 0.0 0.0 17.5 4.2 0.0 0.0 7.5

Motor vehicle theft 65.3 36.1 36.7 82.5 19.4 11.1 0.0 45.0 6.9 11.1 0.0 23.3

Theft 4.2 0.0 0.0 8.3 1.4 0.0 0.0 4.2 0.0 0.0 0.0 1.7

Violent crimes involving a weapon 73.6 52.8 30.0 89.2 33.3 16.7 0.0 48.3 19.4 13.9 0.0 30.8

Violent crimes involving a firearm 93.1 75.0 66.7 99.2 68.1 38.9 30.0 85.0 41.7 25.0 3.3 65.0

Violent crimes committed by a

stranger

52.8 27.8 0.0 68.3 22.2 8.3 0.0 35.0 9.7 2.8 0.0 20.0

Violent crimes committed by an 

intimate

91.7 88.9 70.0 99.2 75.0 30.6 26.7 85.0 51.4 8.3 3.3 60.8

Violent crimes committed by other 

relative

97.2 94.4 96.7 100.0 86.1 55.6 56.7 91.7 70.8 36.1 33.3 83.3

Violent crimes committed by other 

known offender

69.4 27.8 20.0 84.2 34.7 8.3 3.3 43.3 25.0 5.6 0.0 28.3

Violent crimes occurring during the 

day

48.6 19.4 3.3 64.2 22.2 8.3 0.0 34.2 15.3 8.3 0.0 25.0

Violent crimes occurring at night 45.8 19.4 10.0 59.2 20.8 8.3 0.0 30.8 11.1 2.8 0.0 23.3

HH crimes occurring during the day 8.3 5.6 0.0 20.0 1.4 0.0 0.0 4.2 0.0 0.0 0.0 3.3

HH crimes occurring at night 9.7 2.8 0.0 17.5 4.2 0.0 0.0 6.7 1.4 0.0 0.0 4.2

Notes: Estimates based on 10 or fewer sample cases, or with relative standard errors greater than 30%, are flagged as unreliable. HH = household.
29

Table 3.4b. Percentage of victimization rates flagged as unreliable by type of crime: Option 1—crimes reported to police

1-year 3-year 5-year

Type of crime

Reg. ×

pop. 

size

Reg. ×

urbanicity

Urbanicity ×

pop. 

size

Reg. ×

pop. 

size ×

urbanicity

Reg. ×

pop. 

size

Reg. ×

urbanicity

Urbanicity ×

pop. 

size

Reg. ×

pop. 

size ×

urbanicity

Reg. ×

Pop. 

Size

Reg. ×

urbanicity

Urbanicity ×

pop. 

size

Reg. ×

pop. 

size ×

urbanicity

All crime 9.7 2.8 0.0 17.5 0.0 0.0 0.0 5.0 0.0 0.0 0.0 0.8

Violent crime 45.8 16.7 20.0 56.7 20.8 8.3 0.0 33.3 9.7 2.8 0.0 21.7

Serious violent crime 79.2 52.8 23.3 90.0 27.8 8.3 3.3 42.5 18.1 8.3 0.0 30.8

Rape/sexual assault 100.0 100.0 100.0 100.0 98.6 83.3 96.7 100.0 93.1 66.7 86.7 97.5

Robbery 97.2 77.8 73.3 99.2 72.2 50.0 30.0 85.8 41.7 33.3 20.0 64.2

Assault 55.6 22.2 16.7 65.0 30.6 8.3 0.0 40.0 11.1 5.6 0.0 23.3

Aggravated 91.7 72.2 70.0 96.7 52.8 16.7 16.7 68.3 30.6 8.3 3.3 47.5

Simple 73.6 38.9 20.0 86.7 38.9 5.6 0.0 49.2 18.1 5.6 0.0 28.3

Personal theft 100.0 100.0 100.0 100.0 98.6 88.9 80.0 99.2 87.5 55.6 63.3 96.7

Property crime 11.1 0.0 0.0 19.2 2.8 0.0 0.0 5.8 0.0 0.0 0.0 0.8

Household burglary 36.1 16.7 3.3 55.8 9.7 0.0 0.0 16.7 8.3 0.0 0.0 12.5

Motor vehicle theft 73.6 44.4 36.7 85.0 23.6 16.7 0.0 48.3 5.6 13.9 0.0 27.5

Theft 15.3 2.8 0.0 25.8 6.9 0.0 0.0 10.0 2.8 0.0 0.0 5.8

Violent crimes involving a weapon 87.5 61.1 50.0 95.8 41.7 11.1 13.3 61.7 27.8 11.1 0.0 42.5

Violent crimes involving a firearm 100.0 86.1 90.0 99.2 79.2 52.8 33.3 90.8 51.4 27.8 13.3 78.3

Violent crimes committed by a

stranger

76.4 44.4 23.3 90.0 41.7 13.9 6.7 52.5 15.3 8.3 0.0 30.8

Violent crimes committed by an 

intimate

95.8 88.9 93.3 100.0 80.6 52.8 43.3 94.2 68.1 13.9 26.7 81.7

Violent crimes committed by other 

relative

100.0 97.2 96.7 100.0 86.1 66.7 70.0 96.7 77.8 44.4 46.7 87.5

Violent crimes committed by other 

known offender

87.5 72.2 56.7 95.8 55.6 25.0 16.7 65.8 41.7 22.2 6.7 55.8

Violent crimes occurring during the 

day

75.0 44.4 40.0 90.8 38.9 16.7 3.3 50.8 23.6 8.3 0.0 33.3

Violent crimes occurring at night 79.2 38.9 20.0 88.3 34.7 16.7 0.0 43.3 22.2 8.3 0.0 31.7

HH crimes occurring during the day 25.0 13.9 3.3 39.2 9.7 0.0 0.0 15.0 6.9 0.0 0.0 10.0

HH crimes occurring at night 18.1 11.1 0.0 32.5 5.6 0.0 0.0 11.7 2.8 0.0 0.0 7.5

Notes: Estimates based on 10 or fewer sample cases, or with relative standard errors greater than 30%, are flagged as unreliable. HH = household.
30

Table 3.5a. Percentage of victimization rates flagged as unreliable by type of crime: Option 2—crimes reported and not 

reported to police

1-year 3-year 5-year

Type of crime

Reg. ×

pop. 

size

Reg. ×

urbanicity

Urbanicity ×

pop. 

size

Reg. ×

pop. 

size ×

urbanicity

Reg. ×

pop. 

size

Reg. ×

urbanicity

Urbanicity ×

pop. 

size

Reg. ×

pop. 

size ×

urbanicity

Reg. ×

Pop. 

Size

Reg. ×

urbanicity

Urbanicity ×

pop. 

size

Reg. ×

pop. 

size ×

urbanicity

All crime 0.0 0.0 0.0 3.6 0.0 0.0 0.0 2.4 0.0 0.0 0.0 0.0

Violent crime 18.8 8.3 0.0 33.3 4.2 5.6 0.0 11.9 0.0 2.8 0.0 6.0

Serious violent crime 50.0 30.6 19.0 66.7 14.6 5.6 0.0 27.4 2.1 2.8 0.0 13.1

Rape/sexual assault 100.0 97.2 95.2 100.0 83.3 63.9 38.1 92.9 72.9 47.2 28.6 79.8

Robbery 79.2 69.4 33.3 90.5 35.4 30.6 19.0 54.8 22.9 22.2 14.3 44.0

Assault 25.0 5.6 0.0 36.9 8.3 5.6 0.0 15.5 2.1 2.8 0.0 7.1

Aggravated 75.0 55.6 23.8 82.1 33.3 8.3 0.0 42.9 14.6 11.1 0.0 25.0

Simple 31.3 11.1 0.0 42.9 12.5 8.3 0.0 17.9 4.2 2.8 0.0 10.7

Personal theft 100.0 97.2 85.7 100.0 66.7 44.4 38.1 85.7 50.0 36.1 23.8 69.0

Property crime 0.0 0.0 0.0 1.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Household burglary 14.6 2.8 0.0 23.8 2.1 0.0 0.0 10.7 0.0 0.0 0.0 2.4

Motor vehicle theft 54.2 36.1 28.6 72.6 10.4 11.1 0.0 32.1 2.1 11.1 0.0 15.5

Theft 2.1 0.0 0.0 1.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Violent crimes involving a weapon 64.6 52.8 23.8 78.6 20.8 16.7 0.0 34.5 12.5 13.9 0.0 23.8

Violent crimes involving a firearm 85.4 75.0 57.1 97.6 50.0 38.9 19.0 73.8 33.3 25.0 4.8 56.0

Violent crimes committed by a

stranger

45.8 27.8 0.0 59.5 10.4 8.3 0.0 23.8 2.1 2.8 0.0 9.5

Violent crimes committed by an 

intimate

85.4 88.9 66.7 98.8 58.3 30.6 9.5 73.8 35.4 8.3 0.0 40.5

Violent crimes committed by other 

relative

93.8 94.4 90.5 100.0 77.1 55.6 38.1 85.7 56.3 36.1 28.6 69.0

Violent crimes committed by other 

known offender

54.2 27.8 4.8 70.2 29.2 8.3 0.0 33.3 18.8 5.6 0.0 20.2

Violent crimes occurring during the 

day

31.3 19.4 0.0 53.6 12.5 8.3 0.0 22.6 4.2 8.3 0.0 15.5

Violent crimes occurring at night 35.4 19.4 4.8 45.2 12.5 8.3 0.0 19.0 4.2 2.8 0.0 14.3

HH crimes occurring during the day 4.2 5.6 0.0 11.9 0.0 0.0 0.0 1.2 0.0 0.0 0.0 1.2

HH crimes occurring at night 4.2 2.8 0.0 7.1 0.0 0.0 0.0 1.2 0.0 0.0 0.0 1.2

Notes: Estimates based on 10 or fewer sample cases, or with relative standard errors greater than 30%, are flagged as unreliable. HH = household.
31

Table 3.5b. Percentage of victimization rates flagged as unreliable by type of crime: Option 2—crimes reported to police

1-year 3-year 5-year

Type of crime

Reg. ×

pop. 

size

Reg. ×

urbanicity

Urbanicity ×

pop. 

size

Reg. ×

pop. 

size ×

urbanicity

Reg. ×

pop. 

size

Reg. ×

urbanicity

Urbanicity ×

pop. 

size

Reg. ×

pop. 

size ×

urbanicity

Reg. ×

Pop. 

Size

Reg. ×

urbanicity

Urbanicity ×

pop. 

size

Reg. ×

pop. 

size ×

urbanicity

All crime 2.1 2.8 0.0 8.3 0.0 0.0 0.0 3.6 0.0 0.0 0.0 0.0

Violent crime 33.3 16.7 9.5 44.0 4.2 8.3 0.0 19.0 2.1 2.8 0.0 13.1

Serious violent crime 70.8 52.8 14.3 79.8 18.8 8.3 0.0 32.1 10.4 8.3 0.0 20.2

Rape/sexual assault 100.0 100.0 100.0 100.0 89.6 83.3 81.0 98.8 85.4 66.7 61.9 96.4

Robbery 89.6 77.8 57.1 96.4 50.0 50.0 23.8 70.2 31.3 33.3 14.3 53.6

Assault 43.8 22.2 4.8 51.2 20.8 8.3 0.0 27.4 4.2 5.6 0.0 14.3

Aggravated 85.4 72.2 52.4 94.0 39.6 16.7 4.8 57.1 22.9 8.3 0.0 33.3

Simple 58.3 38.9 9.5 72.6 27.1 5.6 0.0 31.0 12.5 5.6 0.0 19.0

Personal theft 100.0 100.0 100.0 100.0 97.9 88.9 57.1 98.8 72.9 55.6 47.6 92.9

Property crime 6.3 0.0 0.0 10.7 0.0 0.0 0.0 1.2 0.0 0.0 0.0 0.0

Household burglary 25.0 16.7 0.0 40.5 6.3 0.0 0.0 10.7 2.1 0.0 0.0 3.6

Motor vehicle theft 58.3 44.4 23.8 73.8 14.6 16.7 0.0 39.3 2.1 13.9 0.0 20.2

Theft 8.3 2.8 0.0 16.7 0.0 0.0 0.0 2.4 0.0 0.0 0.0 1.2

Violent crimes involving a weapon 77.1 61.1 38.1 89.3 27.1 11.1 4.8 47.6 16.7 11.1 0.0 29.8

Violent crimes involving a firearm 97.9 86.1 57.1 98.8 58.3 52.8 28.6 83.3 41.7 27.8 4.8 66.7

Violent crimes committed by a

stranger

64.6 44.4 14.3 83.3 29.2 13.9 4.8 38.1 6.3 8.3 0.0 19.0

Violent crimes committed by an 

intimate

93.8 88.9 76.2 100.0 68.8 52.8 28.6 88.1 54.2 13.9 19.0 65.5

Violent crimes committed by other 

relative

97.9 97.2 95.2 100.0 79.2 66.7 52.4 92.9 66.7 44.4 33.3 78.6

Violent crimes committed by other 

known offender

79.2 72.2 42.9 91.7 41.7 25.0 4.8 51.2 35.4 22.2 0.0 41.7

Violent crimes occurring during the 

day

68.8 44.4 19.0 79.8 25.0 16.7 0.0 33.3 14.6 8.3 0.0 22.6

Violent crimes occurring at night 62.5 38.9 9.5 73.8 18.8 16.7 0.0 29.8 14.6 8.3 0.0 23.8

HH crimes occurring during the day 16.7 13.9 0.0 28.6 4.2 0.0 0.0 6.0 0.0 0.0 0.0 2.4

HH crimes occurring at night 10.4 11.1 0.0 21.4 2.1 0.0 0.0 4.8 0.0 0.0 0.0 1.2

Notes: Estimates based on 10 or fewer sample cases, or with relative standard errors greater than 30%, are flagged as unreliable. HH = household.
32

Table 3.6a. Percentage of victimization rates flagged as unreliable by type of crime: Option 3—crimes reported and not 

reported to police

1-year 3-year 5-year

Type of crime

Reg. ×

pop. 

size

Reg. ×

urbanicity

Urbanicity ×

pop. 

size

Reg. ×

pop. 

size ×

urbanicity

Reg. ×

pop. 

size

Reg. ×

urbanicity

Urbanicity ×

pop. 

size

Reg. ×

pop. 

size ×

urbanicity

Reg. ×

Pop. 

Size

Reg. ×

urbanicity

Urbanicity ×

pop. 

size

Reg. ×

pop. 

size ×

urbanicity

All crime 0.0 0.0 0.0 8.3 0.0 0.0 0.0 5.2 0.0 0.0 0.0 2.1

Violent crime 8.3 8.3 4.2 37.5 0.0 5.6 0.0 15.6 0.0 2.8 0.0 11.5

Serious violent crime 35.4 30.6 25.0 68.8 10.4 5.6 0.0 34.4 2.1 2.8 0.0 18.8

Rape/sexual assault 100.0 97.2 95.8 100.0 83.3 63.9 37.5 94.8 66.7 47.2 33.3 82.3

Robbery 72.9 69.4 41.7 93.8 29.2 30.6 25.0 61.5 20.8 22.2 12.5 53.1

Assault 16.7 5.6 4.2 42.7 2.1 5.6 0.0 18.8 0.0 2.8 0.0 12.5

Aggravated 66.7 55.6 33.3 84.4 22.9 8.3 0.0 47.9 8.3 11.1 4.2 29.2

Simple 20.8 11.1 4.2 50.0 4.2 8.3 0.0 20.8 0.0 2.8 0.0 14.6

Personal theft 100.0 97.2 79.2 100.0 66.7 44.4 58.3 87.5 39.6 36.1 41.7 71.9

Property crime 0.0 0.0 0.0 7.3 0.0 0.0 0.0 3.1 0.0 0.0 0.0 1.0

Household burglary 8.3 2.8 0.0 32.3 0.0 0.0 0.0 15.6 0.0 0.0 0.0 6.3

Motor vehicle theft 45.8 36.1 41.7 75.0 4.2 11.1 0.0 43.8 0.0 11.1 0.0 24.0

Theft 0.0 0.0 0.0 6.3 0.0 0.0 0.0 4.2 0.0 0.0 0.0 2.1

Violent crimes involving a weapon 62.5 52.8 29.2 83.3 14.6 16.7 0.0 40.6 4.2 13.9 0.0 26.0

Violent crimes involving a firearm 81.3 75.0 62.5 95.8 50.0 38.9 37.5 80.2 27.1 25.0 4.2 61.5

Violent crimes committed by a

stranger

29.2 27.8 0.0 58.3 6.3 8.3 0.0 31.3 0.0 2.8 0.0 17.7

Violent crimes committed by an 

intimate

85.4 88.9 62.5 99.0 56.3 30.6 16.7 78.1 29.2 8.3 4.2 50.0

Violent crimes committed by other 

relative

95.8 94.4 95.8 100.0 75.0 55.6 41.7 87.5 52.1 36.1 12.5 76.0

Violent crimes committed by other 

known offender

45.8 27.8 16.7 76.0 16.7 8.3 4.2 35.4 12.5 5.6 0.0 22.9

Violent crimes occurring during the 

day

25.0 19.4 4.2 57.3 6.3 8.3 0.0 27.1 2.1 8.3 0.0 20.8

Violent crimes occurring at night 22.9 19.4 12.5 51.0 8.3 8.3 0.0 27.1 0.0 2.8 0.0 20.8

HH crimes occurring during the day 0.0 5.6 0.0 16.7 0.0 0.0 0.0 4.2 0.0 0.0 0.0 4.2

HH crimes occurring at night 0.0 2.8 0.0 14.6 0.0 0.0 0.0 5.2 0.0 0.0 0.0 4.2

Notes: Estimates based on 10 or fewer sample cases, or with relative standard errors greater than 30%, are flagged as unreliable. HH = household.
33

Table 3.6b. Percentage of victimization rates flagged as unreliable by type of crime: Option 3—crimes reported to police

1-year 3-year 5-year

Type of crime

Reg. ×

pop. 

size

Reg. ×

urbanicity

Urbanicity ×

pop. 

size

Reg. ×

pop. 

size ×

urbanicity

Reg. ×

pop. 

size

Reg. ×

urbanicity

Urbanicity ×

pop. 

size

Reg. ×

pop. 

size ×

urbanicity

Reg. ×

Pop. 

Size

Reg. ×

urbanicity

Urbanicity ×

pop. 

size

Reg. ×

pop. 

size ×

urbanicity

All crime 0.0 2.8 0.0 14.6 0.0 0.0 0.0 6.3 0.0 0.0 0.0 1.0

Violent crime 25.0 16.7 20.8 50.0 4.2 8.3 0.0 28.1 0.0 2.8 0.0 19.8

Serious violent crime 62.5 52.8 29.2 81.3 14.6 8.3 4.2 39.6 6.3 8.3 0.0 27.1

Rape/sexual assault 100.0 100.0 100.0 100.0 97.9 83.3 79.2 100.0 89.6 66.7 62.5 96.9

Robbery 93.8 77.8 70.8 99.0 41.7 50.0 33.3 76.0 25.0 33.3 25.0 60.4

Assault 31.3 22.2 16.7 55.2 6.3 8.3 0.0 31.3 0.0 5.6 0.0 20.8

Aggravated 83.3 72.2 54.2 94.8 37.5 16.7 16.7 61.5 8.3 8.3 4.2 36.5

Simple 50.0 38.9 12.5 79.2 18.8 5.6 0.0 39.6 6.3 5.6 0.0 25.0

Personal theft 100.0 100.0 100.0 100.0 97.9 88.9 70.8 99.0 79.2 55.6 66.7 94.8

Property crime 0.0 0.0 0.0 15.6 0.0 0.0 0.0 5.2 0.0 0.0 0.0 1.0

Household burglary 20.8 16.7 4.2 51.0 0.0 0.0 0.0 14.6 0.0 0.0 0.0 9.4

Motor vehicle theft 54.2 44.4 41.7 77.1 10.4 16.7 0.0 47.9 0.0 13.9 0.0 29.2

Theft 2.1 2.8 0.0 21.9 0.0 0.0 0.0 7.3 0.0 0.0 0.0 5.2

Violent crimes involving a weapon 72.9 61.1 45.8 93.8 16.7 11.1 16.7 54.2 6.3 11.1 0.0 36.5

Violent crimes involving a firearm 100.0 86.1 70.8 99.0 60.4 52.8 41.7 85.4 35.4 27.8 16.7 71.9

Violent crimes committed by a

stranger

58.3 44.4 12.5 82.3 16.7 13.9 8.3 42.7 2.1 8.3 0.0 28.1

Violent crimes committed by an 

intimate

93.8 88.9 83.3 100.0 64.6 52.8 37.5 90.6 47.9 13.9 20.8 75.0

Violent crimes committed by other 

relative

100.0 97.2 95.8 100.0 79.2 66.7 54.2 95.8 68.8 44.4 33.3 84.4

Violent crimes committed by other 

known offender

79.2 72.2 29.2 93.8 33.3 25.0 16.7 58.3 20.8 22.2 8.3 45.8

Violent crimes occurring during the 

day

54.2 44.4 33.3 82.3 18.8 16.7 4.2 43.8 4.2 8.3 0.0 27.1

Violent crimes occurring at night 56.3 38.9 20.8 78.1 12.5 16.7 0.0 34.4 8.3 8.3 0.0 27.1

HH crimes occurring during the day 8.3 13.9 4.2 34.4 0.0 0.0 0.0 12.5 0.0 0.0 0.0 7.3

HH crimes occurring at night 2.1 11.1 0.0 26.0 0.0 0.0 0.0 10.4 0.0 0.0 0.0 7.3

Notes: Estimates based on 10 or fewer sample cases, or with relative standard errors greater than 30%, are flagged as unreliable. HH = household.
34

3.5 Weight Recommendations

On the basis of the results of the weighting evaluation, it is recommended that 

uncalibrated (i.e., original) weights be used in the analyses of these four types of generic areas. 

Although the coverage evaluation demonstrated some differences in the demographic 

composition of the generic areas and the NCVS weight sums for these demographic groups, 

there was no evidence that these demographic differences led to substantive changes in key 

estimates. The calibrated weights also reduced the precision of resulting estimates and would be 

more difficult for external researchers to replicate. For these reasons, it is preferable to use the

original, uncalibrated NCVS weights in the analyses of generic areas based on the three PUF 

subnational variables.

Because maintaining all levels of the population size variable can lead to low levels of 

precision, particularly within the Census region × population size × urbanicity generic areas, it is 

recommended that this variable be collapsed into four levels: not in a place, <100,000–249,999, 

250,000–999,999, and 1,000,000+. This collapsing option provided the most precision gains 

while maintaining reasonably distinct levels from a substantive perspective. Although this 

collapsing option does not ameliorate all precision concerns for these generic areas, it allows 

precise estimates to be calculated for the majority of areas and crime types.

When calculating estimates within generic areas, it is recommended that data be pooled 

across multiple years to ensure reasonable precision levels for key estimates. Depending on the 

generic areas included in an analysis and the crime type, pooling 3 or 5 years of data often 

provides adequate precision within generic areas.
35

SECTION 4. EVALUATION OF GENERALIZED VARIANCE FUNCTIONS IN 

SUBNATIONAL AREAS

4.1 Overview of Generalized Variance Function Evaluation

The objective of this analysis is to assess whether the GVFs created by the Census 

Bureau provide variance estimates consistent with direct variance estimation at the subnational 

level. Although the NVAT currently uses GVFs to calculate variances, GVFs were designed to 

provide variance estimates at the national level; their accuracy has not been evaluated at the 

subnational level. If the estimates in a subnational area deviate greatly from the national 

estimates, then the parameters developed for the national estimates may not be appropriate for 

the subnational area.

There is sufficient information available on the PUFs to account for the complex survey 

design and to accurately approximate variances using direct variance estimation. Of the several 

methods for calculating direct variance estimates, the two most common are Taylor series 

linearization (TSL) and balanced repeated replication (BRR). For this analysis, TSL was used as 

the direct variance method, as recommended by the NCVS direct variance evaluation report 

(Williams et al., 2014).

Both direct and GVF variances were calculated for key NCVS estimates within the four 

generic area types, and the resulting precision estimates were compared for an evaluation of 

whether or not the GVFs produced accurate variance estimates at the subnational level. This 

section presents the results of these comparisons and provides recommendations for calculating 

variances within generic areas for BJS reports and within the NVAT.

4.2 Comparison of Direct Estimates to GVFs in Subnational Areas

Within each generic area, victimization totals, rates, and their accompanying variance 

estimates were computed for the same crime types (both overall and reported to police) that were 

included in the weighting evaluation (listed in Section 3.3). Variance estimates were computed 

using direct variance estimation and GVFs. GVF estimates were based on the series-adjusted 
36

GVF parameters and were aggregated for pooled-year estimates using Census-provided 

correlations based on the crime type. The following years were included in the analysis:

• 1-year estimates: 2002, 2008, 2012

• 3-year estimates: 2000–2002, 2006–2008, 2010–2012

• 5-year estimates: 1998–2002, 2004–2008, 2008–2012

For each number of pooled years, 8,694 estimates were computed for both rates and totals 

across the analysis years, crime types, and generic area types. The two estimation approaches 

produce the same point estimates, but variance estimates will be consistent only if the GVFs 

produce valid estimates at the subnational level. Differences in precision estimates for the two 

methods may be evaluated by comparing the GVF and direct variance RSEs for rates and totals 

(Figures 4.1 and 4.2, respectively) for all generic areas and 1-, 3-, and 5-year periods.

Figure 4.1. Comparison of GVF to direct variance RSEs: 1-, 3-, and 5-year victimization 

rates

Notes: GVF = generalized variance function; RSE = relative standard error.
37

Figure 4.2. Comparison of GVF to direct variance RSEs: 1-, 3-, and 5-year victimization 

totals

Notes: GVF = generalized variance function; RSE = relative standard error.

From Figures 4.1 and 4.2, it is clear that the GVFs and direct variances do not track well, 

given the high levels of deviation from the 45-degree line of equality. For the 1-year 

victimization rates and totals, the majority of points fall above the 45-degree line, indicating that 

the GVF approach yielded smaller standard errors than the direct estimation approach. Because 

the direct estimation approach has been validated with the GVFs at the national level (see 

Williams et al., 2014) and GVFs were not designed to produce subnational estimates, it is 

reasonable to assume that the GVF method tends to underestimate the variances of 1-year 

estimates, whereas the direct variance approach produces accurate variance estimates. Therefore, 

the use of the GVF for 1-year generic estimates will lead to an increase in the Type I error rate,

whereby more comparisons will be deemed significantly different than should be.

The reverse is true for 3- and 5-year victimization rates and totals, where the majority of 

points fall below the 45-degree line. This indicates that GVF standard errors are larger than 

direct estimation standard errors, and thus GVFs are overestimating the variances. The use of the 
38

GVF for 3- and 5-year generic estimates will lead to an increase in the Type II error rate,

whereby more comparisons will be deemed statistically similar than should be. The same 

patterns held when concentrating only on reliable estimates (those with GVF and direct variance 

RSEs of less than 30 percent; results not shown).

Table 4.1 further compares differences across 1-, 3-, and 5-year estimates by displaying 

the percentage of estimates for which the GVF standard error is less than the direct estimation 

standard error (i.e., the percentage of estimates for which the GVF is underestimating the 

variance). Because GVF estimates are dependent upon the GVF parameters, which vary by year, 

Table 4.1 also splits out these estimates by the analysis year. In addition, Table 4.1 allows for the 

comparison of estimates across generic area types.

Table 4.1. Percentage of estimates for which GVF standard error is less than direct

variance standard error: Differences by year and generic area type

Generic area Year

1-year 3-year 5-year

Totals Rates Totals Rates Totals Rates

Region × population size × 

urbanicity

2002 66.8 61.5 43.4 36.5 28.6 16.4

2008 54.2 51.3 26.2 15.3 12.7 5.4

2012 48.4 42.8 25.9 13.8 16.8 5.8

Region × population size 2002 61.1 60.3 30.0 28.3 11.3 8.7

2008 55.6 55.8 16.6 13.2 3.3 2.6

2012 48.6 48.8 13.5 12.1 8.2 5.6

Region × urbanicity 2002 75.2 66.8 43.3 31.9 29.9 17.8

2008 61.6 57.6 33.0 15.8 14.7 4.0

2012 60.3 53.3 30.6 15.9 21.6 6.3

Urbanicity × population size 2002 68.9 66.5 34.8 25.2 21.1 11.8

2008 62.1 59.9 18.0 6.8 5.6 1.2

2012 60.2 57.8 18.6 8.7 13.0 3.1

Note: GVF = generalized variance function.

If the GVF and direct estimation approaches provided consistent results, then entries in 

the table would track right around 50 percent. That is, half the time GVF estimates would track 

slightly above direct estimates and the other half the time GVF estimates would track slightly 

below direct estimates. However, Table 4.1 demonstrates the same patterns that were present in 

Figures 4.1 and 4.2: 1-year GVF estimates tend to underestimate variances (percentages are 

greater than 50 percent), whereas 3- and 5-year estimates tend to overestimate variances 
39

(percentages are less than 50 percent). This result is true for both totals and rates and is 

independent of the generic area type.

Clear differences can be seen across analysis years, as GVF variance estimates for 2002 

are smaller than direct estimates more frequently than those for 2008 and 2012 for 1, 3, and 

5 years (i.e., the entries in Table 4.1 are greater for 2002 than for 2008 and 2012). These findings

are in line with something that was noted during the 2002 analysis. When GVFs were calculated 

for the 3-year period of 2000–2002, a total of 146 estimates for rates and totals (84 rates and 62 

totals) had negative GVF estimates, and all of them were associated with property crimes. For 

example, the 2000–2002 estimated rate of motor vehicle theft in Western/rural areas was 

calculated to be negative on the basis of the GVFs. These negative variance estimates are due to

negative GVF parameters in 2000 and 2001 for overall property crime estimates. Although this 

situation is rare (negative variance estimates were computed for only 2.9 percent of victimization 

rates and 2.1 percent of victimization totals in the 2000–2002 period), negative variances can be 

reported when using the GVFs for an estimate type they were not designed to accommodate 

(e.g., a subnational level estimate). Even though GVF estimates are rarely negative, 2002 GVFs 

underestimated the true variances more frequently than did the other two analysis years.

Another pattern that is clear from Table 4.1 is that the more years that are pooled, the 

more GVFs tend to overestimate the true variance. The percentages in Table 4.1 are much lower 

for 3- than for 1-year and for 5- than for 3-year estimates, indicating that GVFs overestimate 

variances more the more years that are pooled. This pattern held for all years and all generic area 

types analyzed.

In addition to assessing differences across years, comparisons were made to assess 

differences between GVF and direct variance estimates across crime types. Tables 4.2a–4.2d

present the percentage of estimates for totals and rates for which the GVF standard error is less 

than the direct variance standard error by generic area type, number of years pooled, and type of 

crime. Table 4.2a includes the Census region × population size × urbanicity generic areas, 

Table 4.2b includes the Census region × population size generic areas, Table 4.2c includes the 

Census region × urbanicity generic areas, and Table 4.2d includes the urbanicity × population

size generic areas.
40

Table 4.2a. Percentage of estimates for which GVF standard error is less than direct

variance standard error: Differences by crime type (region × population size 

× urbanicity)

Type of crime

1-year 3-year 5-year

Totals Rates Totals Rates Totals Rates

Reported and not reported to police

All crime 95.2 94.0 78.6 44.0 66.7 27.4

Violent crime 82.1 73.8 59.5 39.3 38.1 14.3

Serious violent crime 53.6 46.4 27.4 20.2 11.9 7.1

Rape/sexual assault 36.9 38.1 25.0 23.8 17.9 14.3

Robbery 35.7 29.8 20.2 17.9 8.3 6.0

Assault 77.4 72.6 58.3 36.9 34.5 9.5

Aggravated 34.5 34.5 31.0 22.6 15.5 10.7

Simple 78.6 70.2 48.8 33.3 33.3 14.3

Personal theft 9.5 8.3 4.8 2.4 2.4 0.0

Property crime 91.7 94.0 61.9 34.5 47.6 20.2

Household burglary 95.2 86.9 61.9 41.7 46.4 28.6

Motor vehicle theft 67.9 66.7 36.9 36.9 20.2 16.7

Theft 91.7 88.1 59.5 32.1 42.9 15.5

Violent crimes involving a weapon 34.5 29.8 22.6 16.7 7.1 4.8

Violent crimes involving a firearm 20.2 20.2 10.7 13.1 4.8 3.6

Violent crimes committed by a stranger 56.0 50.0 33.3 21.4 15.5 4.8

Violent crimes committed by an intimate 56.0 53.6 35.7 29.8 13.1 8.3

Violent crimes committed by other relative 31.0 32.1 16.7 15.5 11.9 13.1

Violent crimes committed by other known offender 64.3 56.0 31.0 20.2 19.0 11.9

Violent crimes occurring during the day 77.4 64.3 41.7 26.2 22.6 11.9

Violent crimes occurring at night 59.5 50.0 36.9 26.2 23.8 10.7

HH crimes occurring during the day 90.5 79.8 42.9 27.4 29.8 9.5

HH crimes occurring at night 85.7 81.0 42.9 26.2 34.5 7.1

(continued)
41

Table 4.2a. Percentage of estimates for which GVF standard error is less than direct 

variance standard error: Differences by crime type (region × population size 

× urbanicity) (continued)

Type of crime

1-year 3-year 5-year

Totals Rates Totals Rates Totals Rates

Reported to police

All crime 90.5 89.3 61.9 32.1 42.9 11.9

Violent crime 66.7 59.5 33.3 19.0 25.0 10.7

Serious violent crime 42.9 34.5 14.3 11.9 4.8 6.0

Rape/sexual assault 20.2 21.4 15.5 14.3 8.3 7.1

Robbery 28.6 21.4 14.3 9.5 3.6 4.8

Assault 60.7 53.6 33.3 19.0 21.4 9.5

Aggravated 33.3 34.5 19.0 15.5 6.0 6.0

Simple 56.0 48.8 31.0 17.9 23.8 10.7

Personal theft 6.0 6.0 1.2 0.0 1.2 0.0

Property crime 83.3 75.0 38.1 15.5 23.8 6.0

Household burglary 83.3 79.8 45.2 38.1 28.6 15.5

Motor vehicle theft 65.5 65.5 36.9 36.9 21.4 19.0

Theft 83.3 67.9 35.7 20.2 19.0 2.4

Violent crimes involving a weapon 28.6 28.6 11.9 9.5 1.2 2.4

Violent crimes involving a firearm 14.3 15.5 4.8 6.0 4.8 2.4

Violent crimes committed by a stranger 41.7 41.7 19.0 14.3 11.9 3.6

Violent crimes committed by an intimate 32.1 28.6 26.2 22.6 8.3 4.8

Violent crimes committed by other relative 25.0 23.8 9.5 7.1 7.1 7.1

Violent crimes committed by other known offender 44.0 35.7 14.3 9.5 8.3 6.0

Violent crimes occurring during the day 52.4 41.7 22.6 13.1 8.3 6.0

Violent crimes occurring at night 47.6 42.9 25.0 15.5 14.3 6.0

HH crimes occurring during the day 82.1 73.8 33.3 25.0 13.1 2.4

HH crimes occurring at night 83.3 76.2 31.0 25.0 15.5 3.6

Notes: GVF = generalized variance function; HH = household.
42

Table 4.2b. Percentage of estimates for which GVF standard error is less than direct

variance standard error: Differences by crime type (region × population size)

Type of crime

1-year 3-year 5-year

Totals Rates Totals Rates Totals Rates

Reported and not reported to police

All crime 91.7 91.7 64.6 45.8 39.6 20.8

Violent crime 77.1 77.1 31.3 31.3 8.3 8.3

Serious violent crime 50.0 50.0 14.6 12.5 2.1 0.0

Rape/sexual assault 45.8 47.9 27.1 25.0 16.7 14.6

Robbery 39.6 37.5 10.4 14.6 4.2 4.2

Assault 72.9 70.8 37.5 29.2 10.4 6.3

Aggravated 35.4 35.4 27.1 25.0 6.3 4.2

Simple 75.0 75.0 29.2 25.0 14.6 12.5

Personal theft 12.5 14.6 2.1 0.0 2.1 0.0

Property crime 83.3 93.8 31.3 29.2 18.8 8.3

Household burglary 93.8 89.6 43.8 33.3 31.3 22.9

Motor vehicle theft 70.8 77.1 35.4 35.4 12.5 10.4

Theft 83.3 85.4 33.3 29.2 14.6 10.4

Violent crimes involving a weapon 39.6 37.5 16.7 12.5 2.1 2.1

Violent crimes involving a firearm 22.9 22.9 8.3 8.3 4.2 4.2

Violent crimes committed by a stranger 58.3 56.3 20.8 20.8 4.2 2.1

Violent crimes committed by an intimate 56.3 56.3 27.1 27.1 4.2 2.1

Violent crimes committed by other relative 31.3 33.3 16.7 16.7 16.7 18.8

Violent crimes committed by other known offender 58.3 58.3 16.7 14.6 10.4 8.3

Violent crimes occurring during the day 75.0 64.6 16.7 20.8 6.3 4.2

Violent crimes occurring at night 56.3 52.1 22.9 20.8 8.3 6.3

HH crimes occurring during the day 85.4 87.5 20.8 20.8 4.2 2.1

HH crimes occurring at night 75.0 72.9 18.8 16.7 4.2 2.1

(continued)
43

Table 4.2b. Percentage of estimates for which GVF standard error is less than direct 

variance standard error: Differences by crime type (region × population size) 

(continued)

Type of crime

1-year 3-year 5-year

Totals Rates Totals Rates Totals Rates

Reported to police

All crime 85.4 87.5 31.3 14.6 12.5 2.1

Violent crime 62.5 64.6 10.4 10.4 4.2 2.1

Serious violent crime 43.8 41.7 8.3 6.3 0.0 0.0

Rape/sexual assault 25.0 27.1 18.8 18.8 10.4 10.4

Robbery 31.3 31.3 10.4 12.5 2.1 2.1

Assault 54.2 56.3 14.6 16.7 4.2 2.1

Aggravated 39.6 39.6 16.7 14.6 2.1 2.1

Simple 58.3 54.2 14.6 14.6 6.3 4.2

Personal theft 6.3 6.3 2.1 0.0 2.1 0.0

Property crime 70.8 72.9 10.4 4.2 0.0 2.1

Household burglary 79.2 79.2 33.3 31.3 12.5 10.4

Motor vehicle theft 72.9 75.0 35.4 35.4 12.5 14.6

Theft 68.8 64.6 14.6 10.4 0.0 0.0

Violent crimes involving a weapon 31.3 31.3 6.3 6.3 2.1 4.2

Violent crimes involving a firearm 14.6 16.7 6.3 6.3 2.1 2.1

Violent crimes committed by a stranger 43.8 45.8 10.4 10.4 4.2 2.1

Violent crimes committed by an intimate 35.4 35.4 20.8 18.8 0.0 0.0

Violent crimes committed by other relative 29.2 25.0 10.4 10.4 10.4 10.4

Violent crimes committed by other known offender 45.8 41.7 6.3 6.3 2.1 2.1

Violent crimes occurring during the day 56.3 54.2 8.3 8.3 6.3 4.2

Violent crimes occurring at night 50.0 52.1 8.3 8.3 6.3 6.3

HH crimes occurring during the day 70.8 66.7 25.0 20.8 0.0 0.0

HH crimes occurring at night 70.8 72.9 25.0 20.8 0.0 0.0

Notes: GVF = generalized variance function; HH = household.
44

Table 4.2c. Percentage of estimates for which GVF standard error is less than direct

variance standard error: Differences by crime type (region × urbanicity)

Type of crime

1-year 3-year 5-year

Totals Rates Totals Rates Totals Rates

Reported and not reported to police

All crime 100.0 97.2 91.7 38.9 75.0 27.8

Violent crime 88.9 83.3 55.6 30.6 44.4 16.7

Serious violent crime 55.6 55.6 22.2 11.1 13.9 5.6

Rape/sexual assault 61.1 52.8 36.1 33.3 13.9 11.1

Robbery 38.9 33.3 16.7 11.1 11.1 11.1

Assault 91.7 80.6 61.1 33.3 36.1 16.7

Aggravated 47.2 41.7 27.8 19.4 11.1 8.3

Simple 86.1 80.6 58.3 33.3 41.7 16.7

Personal theft 8.3 5.6 5.6 2.8 2.8 0.0

Property crime 97.2 91.7 75.0 30.6 58.3 25.0

Household burglary 100.0 94.4 69.4 36.1 55.6 19.4

Motor vehicle theft 66.7 66.7 36.1 33.3 13.9 8.3

Theft 100.0 86.1 61.1 25.0 50.0 16.7

Violent crimes involving a weapon 50.0 44.4 27.8 11.1 11.1 2.8

Violent crimes involving a firearm 25.0 25.0 19.4 16.7 2.8 2.8

Violent crimes committed by a stranger 61.1 58.3 33.3 16.7 19.4 5.6

Violent crimes committed by an intimate 77.8 77.8 44.4 36.1 11.1 0.0

Violent crimes committed by other relative 47.2 41.7 30.6 27.8 16.7 13.9

Violent crimes committed by other known offender 75.0 61.1 41.7 22.2 22.2 11.1

Violent crimes occurring during the day 83.3 77.8 47.2 25.0 30.6 13.9

Violent crimes occurring at night 69.4 58.3 41.7 33.3 22.2 5.6

HH crimes occurring during the day 97.2 86.1 44.4 16.7 33.3 11.1

HH crimes occurring at night 97.2 91.7 44.4 19.4 36.1 8.3

(continued)
45

Table 4.2c. Percentage of estimates for which GVF standard error is less than direct 

variance standard error: Differences by crime type (region × urbanicity) 

(continued)

Type of crime

1-year 3-year 5-year

Totals Rates Totals Rates Totals Rates

Reported to police

All crime 97.2 97.2 66.7 25.0 47.2 16.7

Violent crime 77.8 72.2 38.9 25.0 25.0 16.7

Serious violent crime 52.8 50.0 19.4 13.9 5.6 8.3

Rape/sexual assault 36.1 33.3 22.2 27.8 8.3 11.1

Robbery 30.6 25.0 16.7 13.9 11.1 8.3

Assault 80.6 69.4 38.9 22.2 30.6 13.9

Aggravated 44.4 33.3 19.4 11.1 5.6 2.8

Simple 66.7 66.7 38.9 25.0 25.0 11.1

Personal theft 5.6 5.6 2.8 2.8 0.0 0.0

Property crime 88.9 69.4 47.2 8.3 30.6 8.3

Household burglary 94.4 91.7 55.6 33.3 38.9 11.1

Motor vehicle theft 58.3 66.7 36.1 33.3 13.9 11.1

Theft 94.4 66.7 33.3 13.9 22.2 5.6

Violent crimes involving a weapon 38.9 33.3 11.1 11.1 0.0 0.0

Violent crimes involving a firearm 22.2 11.1 8.3 8.3 2.8 0.0

Violent crimes committed by a stranger 47.2 47.2 25.0 13.9 11.1 2.8

Violent crimes committed by an intimate 58.3 55.6 27.8 25.0 8.3 0.0

Violent crimes committed by other relative 38.9 38.9 13.9 13.9 13.9 13.9

Violent crimes committed by other known offender 66.7 55.6 13.9 11.1 13.9 13.9

Violent crimes occurring during the day 63.9 63.9 22.2 16.7 13.9 5.6

Violent crimes occurring at night 55.6 44.4 30.6 22.2 19.4 2.8

HH crimes occurring during the day 86.1 63.9 27.8 16.7 13.9 5.6

HH crimes occurring at night 91.7 72.2 30.6 16.7 19.4 2.8

Notes: GVF = generalized variance function; HH = household.
46

Table 4.2d. Percentage of estimates for which GVF standard error is less than direct

variance standard error: Differences by crime type (urbanicity × population

size)

Type of crime

1-year 3-year 5-year

Totals Rates Totals Rates Totals Rates

Reported and not reported to police

All crime 90.5 85.7 66.7 38.1 57.1 14.3

Violent crime 81.0 76.2 47.6 14.3 28.6 14.3

Serious violent crime 57.1 61.9 9.5 4.8 0.0 0.0

Rape/sexual assault 61.9 61.9 38.1 38.1 19.0 19.0

Robbery 47.6 42.9 14.3 9.5 4.8 4.8

Assault 81.0 81.0 38.1 14.3 19.0 9.5

Aggravated 57.1 52.4 23.8 23.8 9.5 0.0

Simple 90.5 85.7 38.1 19.0 28.6 14.3

Personal theft 19.0 19.0 4.8 0.0 0.0 0.0

Property crime 81.0 90.5 57.1 19.0 47.6 9.5

Household burglary 100.0 95.2 52.4 19.0 33.3 14.3

Motor vehicle theft 76.2 76.2 28.6 28.6 14.3 9.5

Theft 81.0 85.7 42.9 14.3 38.1 9.5

Violent crimes involving a weapon 57.1 52.4 14.3 4.8 0.0 0.0

Violent crimes involving a firearm 33.3 33.3 9.5 9.5 4.8 4.8

Violent crimes committed by a stranger 57.1 52.4 14.3 4.8 4.8 0.0

Violent crimes committed by an intimate 85.7 85.7 23.8 23.8 0.0 0.0

Violent crimes committed by other relative 57.1 57.1 28.6 28.6 14.3 9.5

Violent crimes committed by other known offender 71.4 61.9 28.6 23.8 14.3 14.3

Violent crimes occurring during the day 71.4 66.7 19.0 19.0 19.0 9.5

Violent crimes occurring at night 76.2 57.1 23.8 9.5 4.8 0.0

HH crimes occurring during the day 95.2 90.5 33.3 9.5 23.8 9.5

HH crimes occurring at night 90.5 85.7 33.3 9.5 23.8 4.8

(continued)
47

Table 4.2d. Percentage of estimates for which GVF standard error is less than direct 

variance standard error: Differences by crime type (urbanicity × population

size) (continued)

Type of crime

1-year 3-year 5-year

Totals Rates Totals Rates Totals Rates

Reported to police

All crime 95.2 85.7 57.1 4.8 33.3 4.8

Violent crime 76.2 66.7 23.8 14.3 9.5 0.0

Serious violent crime 47.6 47.6 4.8 4.8 0.0 0.0

Rape/sexual assault 38.1 33.3 38.1 38.1 14.3 14.3

Robbery 28.6 23.8 0.0 4.8 4.8 4.8

Assault 81.0 71.4 23.8 14.3 4.8 0.0

Aggravated 42.9 42.9 14.3 9.5 0.0 0.0

Simple 76.2 76.2 23.8 9.5 14.3 0.0

Personal theft 9.5 9.5 0.0 0.0 0.0 0.0

Property crime 71.4 76.2 28.6 4.8 19.0 4.8

Household burglary 95.2 90.5 28.6 19.0 23.8 4.8

Motor vehicle theft 61.9 61.9 28.6 33.3 14.3 14.3

Theft 61.9 52.4 23.8 9.5 9.5 0.0

Violent crimes involving a weapon 52.4 52.4 4.8 4.8 0.0 0.0

Violent crimes involving a firearm 23.8 19.0 4.8 4.8 0.0 0.0

Violent crimes committed by a stranger 52.4 52.4 14.3 0.0 0.0 0.0

Violent crimes committed by an intimate 52.4 52.4 9.5 9.5 4.8 0.0

Violent crimes committed by other relative 47.6 47.6 19.0 19.0 19.0 19.0

Violent crimes committed by other known offender 47.6 42.9 4.8 4.8 4.8 4.8

Violent crimes occurring during the day 61.9 61.9 14.3 14.3 4.8 4.8

Violent crimes occurring at night 47.6 52.4 9.5 0.0 0.0 0.0

HH crimes occurring during the day 66.7 66.7 19.0 9.5 9.5 0.0

HH crimes occurring at night 76.2 81.0 9.5 4.8 9.5 0.0

Notes: GVF = generalized variance function; HH = household .
48

These tables show that the GVFs tend to overestimate the variance for some crime types, 

whereas for others they tend to underestimate the true variance. As previously noted, 1-year 

GVFs tend to underestimate variances, but this pattern does not hold for all crime types. For 

example, GVFs tend to overestimate variances for robbery, personal theft, and violent crimes 

involving a firearm. One example of a crime type for which the GVFs frequently overestimates

the variance is personal theft, for which the GVFs are too high for more than 4 out of 5 estimates 

for all generic area types, any number of pooled years, and for both overall crimes and crimes 

reported to the police. Although some crime types exhibit clear patterns, the majority of crime 

types vary in whether they overestimate or underestimate the variance depending on the number 

of pooled years and the generic area type. Tables 4.2a–4.2d also support the conclusions made 

from Table 4.1 that the more years that are pooled, the more the GVFs overestimate variances 

(as is evident by the decreasing percentages as more years are pooled within each generic area).

4.3 Variance Estimation Recommendations

On the basis of this analysis, it is clear that the direct estimation and GVF approaches do 

not provide consistent variance estimates at the generic area level. Because GVFs were designed 

only to produce national estimates and direct estimation has been validated for use on the NCVS 

previously, GVFs are evidently not accurate at the subnational level. The use of direct variance 

estimation within generic areas is recommended for all analysis years and formations of generic 

areas. This recommendation is based on the following observations:

• GVFs do not track well with direct variance estimates. They tend to underestimate the 

variances of 1-year estimates and overestimate the variances of pooled-year estimates 

for both totals and rates, regardless of the generic area type.

• Although rare, negative variances can be reported by using GVFs for subnational 

estimates.

• Differences in the validity of GVF estimates vary widely based on the analytic year 

and crime type.

Whenever possible (e.g., for BJS publications), it is recommended that analysts use direct 

variance estimation, rather than GVFs, for calculating variances within generic areas. Because of 

the inaccuracies in GVF estimates within generic areas, it is recommended that the three-variable 

generic area type (Census region × population size × urbanicity) not be added to the NVAT.
49

SECTION 5. FINDINGS AND RECOMMENDATIONS

Table 5.1 summarizes the findings and recommendations of this report.

Table 5.1. Findings and recommendations regarding the coverage and reliability of 

NCVS public-use file subnational geographic identifiers

Findings Recommendations

Coverage assessment

▪ Assessed the coverage of the National Crime Victimization 

Survey (NCVS) sample in the generic areas formed from 

crossing the three subnational identifiers available on the 

NCVS public-use file: Census region, population size, and 

urbanicity.

▪ Developed comparable Census estimates within the four 

types of generic areas.

▪ Compared the Census estimates to NCVS weight sums 

within generic areas for key person- and household-level 

demographic characteristics for three comparison years: 

2002, 2008, and 2012.

▪ Although the estimates tracked well, there were a 

nonnegligible number of significant differences.

▪ Further evaluation was recommended to 

assess whether differences in demographic 

coverage rates translated to substantive 

differences in point estimates.

Weighting assessment

▪ The weights of the NCVS sample were calibrated to Census 

control totals at the generic area level.

▪ Point estimates and estimated precision levels (percent 

relative standard errors, or RSEs) for the uncalibrated and 

calibrated weights were compared.

▪ Calibrated and uncalibrated estimates tracked well, 

indicating that there was no evidence of bias resulting 

from use of the original (i.e., uncalibrated) NCVS 

weights.

▪ Calibrated weights had lower precision levels than

uncalibrated weights.

▪ It was recommended that uncalibrated 

weights be used for calculations of key 

estimates within generic areas formed from 

the public-use file subnational variables.

Variance estimation

▪ Two methods for calculating design-consistent variances for 

the NCVS are direct variance estimation and generalized 

variance functions (GVFs).

▪ Direct variance estimation has been validated to produce 

accurate estimates for the NCVS.1

▪ GVFs were not designed to produce estimates at the 

subnational level.

▪ GVF estimates do not track well with direct variance 

estimates, indicating that they will lead to biased variance 

estimates within generic areas.

▪ It is recommended that direct variance 

estimation be used within generic areas 

whenever possible.

▪ Because the NCVS Victimization Analysis 

Tool (NVAT) produces variance estimates 

based on GVFs, it is recommended that the 

generic areas based on the three-variable 

cross of Census region, population size, and 

urbanicity not be added to the NVAT.

1 Williams, R., Heller, D., Couzens, G. L., Shook-Sa, B., Berzofsky, M., Smiley-McDonald, H., & Krebs, C. (2014). 

Evaluation of direct variance estimation, estimate reliability, and confidence intervals for the National 

Crime Victimization Survey. Prepared for the Bureau of Justice Statistics, Washington, DC.
50

SECTION 6. REFERENCES

Planty, M. (2012). Approaches to estimating subnational victimization estimates. In Federal 

Committee on Statistical Methodology Proceedings, pp. 1–10. Retrieved from 

http://fcsm.sites.usa.gov/files/2014/05/Planty_2012FCSM_I-B.pdf

Williams, R., Heller, D., Couzens, G. L., Shook-Sa, B., Berzofsky, M., Smiley-McDonald, H., & 

Krebs, C. (2014). Evaluation of direct variance estimation, estimate reliability, and 

confidence intervals for the National Crime Victimization Survey. Prepared for the 

Bureau of Justice Statistics, Washington, DC.
The Bureau of Justice Statistics of the U.S. Department of Justice is the principal 

federal agency responsible for measuring crime, criminal victimization, criminal 

offenders, victims of crime, correlates of crime, and the operation of criminal 

and civil justice systems at the federal, state, tribal, and local levels. BJS collects, 

analyzes, and disseminates reliable and valid statistics on crime and justice 

systems in the United States, supports improvements to state and local criminal 

justice information systems, and participates with national and international 

organizations to develop and recommend national standards for justice statistics. 

William J. Sabol is director.

Office of Justice Programs

Innovation • Partnerships • Safer Neighborhoods

www.ojp.usdoj.gov

NCJ249467