By Michael D. Maltz

BJS Fellow

and

Marianne W. Zawitz

BJS Statistician

The National Crime 

Victimization Survey

Initiated in 1972, the National Crime

Victimization Survey (NCVS) is an

annual sample survey of households 

in the United States conducted by the

Bureau of Justice Statistics (BJS).

Currently, 45,000 households are in

the sample. All household members

age 12 or over, or approximately

94,000 residents, are interviewed twice

yearly about incidents in which they

were the victims of a crime during the

previous 6 months. 

The NCVS was created to obtain information on both crimes that are

reported to the police and those that

are not. It complements the policereported crime data found in the FBI’s

Uniform Crime Reports (UCR).

Because the UCR and NCVS

programs are conducted for different

purposes, use different methods, and

focus on somewhat different aspects

of crime, the information they produce

together provides a more comprehensive panorama of the Nation’s crime

problem than either could produce

alone. (For additional information

about the NCVS and the UCR, see

The Nation’s Two Crime Measures.)

The NCVS measures property crimes

and the violent crimes of rape and

sexual assault, robbery, aggravated

assault, and simple assault. Because

the NCVS data are the result of interviews with victims, the survey does not

provide data about the violent crime of

murder. The FBI’s UCR serves as the

source of murder data.

Sampling

Frequently it is not possible or practical to survey everyone in a population,

particularly if the population is the

Nation as a whole. Sampling, a

commonly used technique, gathers

information from a portion of a population and develops estimates that can

 Highlights

Intended for an audience that is not

trained in statistics, this report

presents statistical information in a

nontechnical format by using graphical displays of violent crime trends

which include the degree of precision

of these estimates. 

In addition, it discusses —

Z sampling

Z statistical significance

Z precision in the NCVS estimates

Z statistical inference

Z sample size and precision.

Trends in violent victimization, 1973-96

Note: Violent crimes included are rape and sexual assault, robbery, aggravated assault, and simple

assault. Data about murder are not included because they do not come from a sample survey where

precision can be measured.

Source: Bureau of Justice Statistics, National Crime Victimization Survey, 1973-96.

1980 1990

0

10

20

30

40

50

1973 1996

Violent victimizations per

1,000 population age 12 or over

Bureau of Justice Statistics

Technical Report

U.S. Department of Justice

Office of Justice Programs

Displaying Violent Crime Trends 

Using Estimates from the 

National Crime Victimization Survey

June 1998, NCJ 167881
be generalized to the whole population. Most reputable survey organizations select people to be interviewed at

random to ensure that their sample is

representative of the entire population.

The entire population need not be

examined to get a good reading of its

characteristics. A sample from the

population would do. For example, to

determine if there is enough salt in a

well-stirred bowl of soup, a teaspoonful

will suffice. Moreover, the sample size

is independent of the population size.

A single teaspoonful would also suffice

to check the salt in a large vat of (wellstirred) soup.

Sampling has a number of

advantages:

Z Information can be collected at a

fraction of the cost of interviewing

everyone in the population. 

Z The time to collect and process the

data is reduced.

Z The burden of being interviewed is

placed on fewer people. 

Samples result in estimates of the

occurrence of the behavior being

measured. By definition, estimates

are not exact. The precision of the

estimates derived from the sample is

determined by two factors. The first is

sample size; a larger sample produces

greater precision but at a greater cost.

The second is frequency of occurrence

of the behavior being measured. For

example, election-year polls are

sample surveys in which a small

sample of voters (usually about 2,000)

is asked about their candidate preferences. A sample of this size is

adequate for this purpose because

most people voice a preference. But a

much larger sample is needed to estimate violent crime rates such as that

for robbery because, despite their

frequency, the vast majority of people

do not experience a violent victimization in any 6-month period. Since the

sample used to estimate victimizations

in the NCVS must be large enough to

capture a sufficient number of incidents, the NCVS interviews 94,000

individuals.

The survey is administered to a

random sample of the population,

which results in two benefits: first, the

collected data will be more likely to

reflect the characteristics of the entire

population; and second, the precision

of the estimates generated by the

sample can be calculated, to determine how close they come to the true

numbers. This precision is important

in comparing two estimates from

sampled data. Without knowledge of

this precision, conclusions cannot be

drawn from sampled data.

Statistical significance

Statistical significance is a standard

applied to a comparison of two estimates. For example, surveys of voter

preferences are usually reported along

with their margin of error, such as

“plus or minus 3 percentage points.” If

the survey results in one candidate

receiving 46% of the expected vote

and another candidate receiving 44%,

it might be reported that “the election

is too close to call.” Given the 3

percentage point margin of error, a

statistician would say that “the difference between the two candidates is

not statistically significant.”

For the NCVS, the number of people

who report violent victimizations to

interviewers can be quite small, which

limits our ability to declare year-to-year

changes as statistically significant.

The figure on this page shows the estimated violent crime victimization rates

from 1973 to 1996. In the figure,

violent crime appears to be increasing

from 1992-93 and 1993-94 then

decreasing from 1994-95 and 1995-96.

By assessing the precision of these

estimates  as shown on the following

pages  conclusions can be drawn

that there were statistically significant

decreases from 1994 to 1995 and

1995 to 1996. However, the individual

year-to-year increases from 1992 to

1993 and 1993 to 1994 were not

statistically significant. Therefore, a

simple chart of the best estimate like

the one on this page is inadequate for

drawing conclusions about year-toyear changes.

Not all statistically significant findings

may be substantively significant 

important in the context of the subject

matter studied. For example, in 1996

the daytime theft rate was 39.7 per

1,000 households and the nighttime

theft rate was 37.5. The difference

between these rates is statistically

significant. Substantively, however,

there is really little difference since

households are about as likely to

experience a theft at either time of

day.

Sometimes people assume that large

differences are statistically significant

because they would be substantively

significant if they were true. For

example, the NCVS estimates that the

rate for rape and sexual assault

dropped considerably, about 17%,

from 1995-96. A drop of this magnitude would be substantively significant

if it were statistically significant.

However, because it is based on a

relatively small number of incidents, it

is not statistically significant. The

data do not provide sufficient evidence

that this decrease occurred. (See p. 5.)

Statistical significance only has meaning when referring to a random

sample, although it is often misused

with samples that are not random.

 

2 Displaying Violent Crime Trends Using Estimates from the NCVS

Trends in violent victimization 

rates, 1973-96

Note: The violent crimes included are rape and

sexual assault, robbery, aggravated assault, and

simple assault. The light gray area indicates that

because of changes made to the victimization

survey, data prior to 1992 are adjusted to make

them comparable to data collected under the

redesigned methodology. Data for 1995 and

beyond are based on collection year (see Criminal Victimization 1996: Changes 1995-96 with

Trends 1993-96 ).

Violent v ictimizations per 1,000

population age 12 or ov er

1980 1990

0

10

20

30

40

50

60

1973 1996
 Displaying Violent Crime Trends Using Estimates from the NCVS 3

How the precision of the estimates affects our assessment of crime trends

0

1980 1990

35

40

50

1973 1996

Violent victimizations per

1,000 population age 12 or over

Trends in the best estimate

1980 1990

35

40

50

0

Range of estimates

Violent victimizations per

1,000 population age 12 or over

1973 1996

Probability that the true

violent victimization rate

is within the range

Best

estimate

68% 90% 95%

1980 1990

35

40

50

0

1973 1996

The best estimate and range of estimates

Violent victimizations per

1,000 population age 12 or over

Source: BJS, National Crime Victimization Survey

Trends in violent victimization, 1973-96 These figures depict both the NCVS estimates of

violent crime and their precision. They display the

same trend data as on page 2 but the scale is

changed to highlight the trend line. The first figure

depicts only the best estimates based on the NCVS

sample and does not reflect the range of possible

values where the actual number could fall.

Each bar in the middle figure shows the range within

which the true victimization rate is likely to fall for

that year. Because the estimates are based on

samples, their precision depends on the sample size:

the larger the sample, the better the estimate and the

smaller the range bars. The samples were larger

before 1992, which is why the earlier range bars are

shorter than those after 1992. (The 1992 range bar is

very large due to the size of the sample that year, as

explained on page 8.)

The bars reflect the range within which the true rate

is likely to fall. When the bars are shorter, there is a

greater likelihood that the true rate will fall close to

the best estimate. There is a considerable likelihood

(68% probability) that the true victimization rate lies

within the range represented by the darkest segment

of the bar. There is a greater likelihood (90%) that

the true victimization rate lies within the expanded

range represented by the two darkest segments of

the bar. The full bar includes the range within which

the true value is highly likely (95%) to lie.

For example, while the best estimate in 1996 is 42

violent crime victimizations per 1,000 persons age

12 or over, there is a 95% likelihood that the true

value of the victimization rate lies between 40 and

44. 

Some year-to-year changes are so large that contiguous bars do not touch (1980-81, 1982-83, 1990-91,

1994-95, and 1995-96), suggesting statistically

significant increases and decreases. Where there is

a lot of overlap (1973-76 and 1986-90), the year-toyear changes may be too small to be statistically

significant. 

The bottom figure, which overlays the range bars

with the trend line, puts the trend line in context.

Even though the victimization rates have a range of

possible values, general trends are readily apparent.

Violent crime rates increased from the early 1970’s

to the early 1980’s, then fell until around 1986. For

several years in the late 1980’s, violent crime rates

were stable, but increased in the early 1990’s and fell

after 1994 through 1996. 
To determine whether a change from

year to year is statistically significant,

the precision of the year-to-year

changes must be assessed. The

figure above depicts the estimated

annual percent change in violent crime

rates from 1973-74 (the top bar)

through 1995-96 (the bottom bar). For

example, from 1990 to 1991 violent

crime increased by an estimated 9%

and from 1995 to 1996 it decreased by

an estimated 10%. 

The range bars represent the range

within which the true annual percent

change from year to year is likely to

fall. If a bar does not cross the No

change line, we are reasonably certain

a change occurred. If a bar crosses

the No change line, there is a possibility that there was no change. The

degree of certainty depends on how

much of the bar crosses the line.

BJS standards, based on general

social science practice, specify the

degree of certainty that is acceptable

with sampled data. For the most part, 

the BJS standard of confidence is

95%, meaning there is a possibility

that the difference may be due to

chance but that this possibility is less

than 5%. BJS terms findings in the

90% range as marginal or as indicating

some evidence of a change. The 68%

range is presented because it is a

standard used by statisticians. (See

Appendix I for further explanation.)

Year-to-year changes in the NCVS

violent crime estimates often are not

statistically significant. Based on the

95% standard, there was a statistically

significant change in the violent crime

rates for 8 of the 23 periods (1976-77,

1979-80, 1980-81, 1982-83, 1985-86,

1990-91, 1994-95, and 1995-96). To

highlight the significant increases and

decreases, the range bars are outlined

and the estimate is represented by a

large dot. 

There is some evidence that the

violent crime rate increased from 1986

to 1987, but the likelihood that this

change occurred is not as great as that

for the other years because the last

segment of the range bar (between

90% and 95%) crosses the No change

line rather than clearing the line. If the

range bar only extended to the 90%

level (the two darkest segments of the

bar), the bar would not cross the No

change line. For this year, the estimate

is marked with a black square.

The range bars for all of the other periods intersect the No change line at a

segment representing less than 90%

probability (within the two darkest

segments). Although a change may

have occurred, the probability of such

a change does not meet the BJS minimum standard of certainty. Therefore,

 these changes are not statistically

significant and these estimates are

marked with a small dot. 

 

4 Displaying Violent Crime Trends Using Estimates from the NCVS

Year-to-year changes in victimization rates and their precision

Source: BJS, National Crime Victimization Survey.

1995-96

1994-95

1993-94

1992-93

1991-92

1990-91

1989-90

1988-89

1987-88

1986-87

1985-86

1984-85

1983-84

1982-83

1981-82

1980-81

1979-80

1978-79

1977-78

1976-77

1975-76

1974-75

1973-74

Annual percent change in violent victimization, 1973-96

-25% 0% 25%

Percent change in violent victimization rates

Probability that the true percent

change in v iolent v ictimization

is within the range

Probability that a

change occurred

Greater than 95%

Greater than 90%

Less than 90%

8

8

8

8

8

8

8

8

8

8

8

8

8

8

0

8

8

8

8

8

8

8

8

Best estimate

68%

90%

95%

Decrease No Increase

change

8

0

8
The figure above shows the 1995-96

percent change in the victimization

rates for the category of total violent

crime and the types of crime that

comprise it: rape and sexual assault

(hereafter referred to as rape), aggravated assault, simple assault, robbery,

and murder. The crime categories are

displayed vertically according to their

1996 rates. The highest rate is total

violent crime (sum of all types).

Among the crime types, the rate for

simple assault is the highest and the

rate for murder is the lowest. 

As in the figure on page 4, the range

bars represent the range within which

the true percent change from year to

year is likely to fall. Murder is included

for comparison. The value for the

change in murder rates is given as a

point (represented by a diamond) and

not a range of estimates, because

murder rates are derived from

nonsampled data and consequently

their precision cannot be calculated.1

 

The range bar for simple assault is

clear of the No change line, so there is

a 95% likelihood that it declined from

1995 to 1996. The best estimate of

the decrease was 11%. Since the No

change line intersects the darker

segments of the range bars for rape,

robbery, and aggravated assault, the

BJS standards of certainty are not

met, so it cannot be concluded that a

change in these rates occurred from

1995 to 1996. 

The length of the range bars varies

considerably from crime to crime,

depending on the rarity of the event.

There are many fewer rapes than

simple assaults, and therefore the

range bar for rape is much longer than

that for simple assault, indicating less

certainty in the estimate.

Note that even though the rape rate

appears to have decreased the most

(over 17%), its range bar crosses the

No change line inside the 90% region.

Therefore, the evidence for a decrease

is too weak to accept, because there is

sufficient likelihood that there was

actually no change — or even a slim

possibility of an increase.

With the current rape rate and NCVS

sample size, it could not be concluded

that there was a year-to-year change

in the rape rate unless the change in

the rape rate was greater than 23%.

Conversely, for a 10% change in the

rape rate to be statistically significant,

a sample of over 500,000 U.S. residents, more than 5 times the current

sample size, would be needed.

 Displaying Violent Crime Trends Using Estimates from the NCVS 5

1

To account for the relatively few agencies that do

not provide complete data, homicide rates for the

Nation as a whole must be estimated. (See page

8.) 

Rape and sexual assault 1.4

Robbery 5.2

Simple assault 26.6

Aggravated assault 8.8

Total violent 42.0

Murder* .07

Violent crime categories

 ranked by 1996 rates

 per 1,000 population

 age 12 or ov er

Decrease Increase No

change

Probability that the true percent

change in v iolent v ictimization

is within the range

Best estimate

Probability that a

change occurred

Greater than 95%

Less than 90%

68%

90%

95%

-60% -40% -20% 0% 20%

Percent change in violent victimization rates

Crime types

Annual percent change in v iolent v ictimization by category, 1995-96

*The change in murder rates is presented as a point because the rates are not derived from sampled data 

and their precision cannot be calculated. Murder rates are for all ages.

Sources: BJS, National Crime Victimization Survey and FBI, Uniform Crime Reports
Appendix I. Additional information

on basic statistical principles

This appendix describes some of the

basic principles used in this report. It

is intended to be introductory rather

than exhaustive. 

Calculating precision in sampled

data

The ranges represented by the range

bars were calculated using methods

based on statistical inference. The

examples given below are based on a

simple random sample, in which every

member of the population being studied has the same probability of being

in the sample.2

 

Suppose a city had an equal number

of males and females. If 64 people are

 randomly selected, approximately 32

would be expected to be female. It

would not be surprising if the sample

actually contained between 27 and 37

females, but it would be extremely

unlikely if all 64 turned out to be

female: that would be equivalent to

tossing a coin 64 times and getting

tails each time.

A measure of this likelihood is the

standard error (SE), which reflects how

close to the true value (in this case,

32) the estimate is expected to be. It is

not expected that a sample will always

produce that number, but it should be

close. 

The probability that the estimate will

fall within a range can be calculated

using standard errors. According to

the methods of statistical inference, if

several samples were drawn from the

population, 68% of them would fall

within one standard error of the true

value. In this example, with a sample

size of 64, the standard error is 4, so

68% of the time the expected number

of females in the sample would be 

32 ± 4 or between 28 and 36.3

 

Another way of putting it is that if

1,000 different samples of size 64

were drawn from the city’s population,

in about 680 of them the number of

females would be between 28 and 36.

In addition, about 90% of the samples

would fall within 1.645 standard errors

of the true value and that 95% of the

samples would fall within 1.96 standard errors of the true value. Social

scientists generally report findings

based on the probability that the estimate occurred within a range defined

as the confidence interval. BJS

usually reports two confidence intervals, the 90% and 95% ranges, to

reflect its standards of confidence; in

this report, we also include the 68%

range to show one standard error. The

table below gives the ranges in terms

of the number of females expected in

samples of size 64, with different

levels of confidence.

Number of females expected in a

sample, for different confidence levels

95% 1.96 24.16 to 39.84

90% 1.645 25.42 to 38.58

68% 1.00 28 to 36

Expected range 

of females in the

sample

 Number 

 of SEs 

Confidence

level

Sample characteristics: 

 Size = 64

 Expected number of females = 32

 Standard error = 4

Population characteristic: 50% female

More often the value of interest is not

the expected number of occurrences

but the percent of occurrences. In this

example the most likely percent of

occurrences is 50% of the sample (32

of 64). Sixty-eight percent of the

samples are expected to be within 1

standard error (4/64, or 6.25%) of

50%.4

 The following table shows how

the expected range in frequency of

occurrence varies by confidence level.

Percentage of females expected in the

sample, for different confidence levels

95% 1.96 37.75% to 62.25%

90% 1.645 39.72% to 60.28%

68% 1.00 43.75% to 56.25%

Expected range 

of the percent of

females in the sample

 Number 

 of SEs

Confidence

level

Sample characteristics: 

 Size = 64 

 Expected percent of females = 50%

 Standard error = 6.25%

Population characteristic: 50% female

This table shows two percentages

simultaneously, but they are entirely

different: the first column refers to

confidence levels expressed as

percentages and the third column

refers to the percentages of females in

the sample. The first line in the table

indicates that 68% of the time a

random sample is drawn, it is

expected to contain between 43.75%

and 56.25% females. The table also

shows that 95% of such samples will

include between 37.75% and 62.25%

females; or conversely, that fewer than

5% of the samples will be outside this

range.

Usually the sample percentage is

close to the actual percentage in the

population; the larger the sample the

more accurate the estimate. This is as

true for measuring criminal victimization as it is for population characteristics. Even though the actual

percentage of people who were

victims is unknown, a sufficiently large

sample will produce a sample percentage quite close to the actual percentage. 

The example above shows that, given

the frequency of occurrence of a characteristic in the population, the

frequency of occurrence of a characteristic in the sample can be estimated. The more important problem is

the inverse problem of statistical inference: given the frequency of occurrence of a characteristic in the sample,

what is the frequency of occurrence of

that characteristic in the population?

The question answered by the NCVS

is, given the number of incidents

captured by the survey, what is the

crime victimization rate for the Nation

as a whole? 

 

6 Displaying Violent Crime Trends Using Estimates from the NCVS

2

The NCVS employs a very complex, random

stratified multistage cluster sample, but the general

principles discussed in this report apply to it as

well. See Appendix II of the BJS report, Criminal

Victimization in the United States, 1994, for a

description of the sample construction.

3

For a simple random sample, the standard error is

equal to where Np(1 − p) N is the sample size and

p is the probability of the occurrence. In this case

SE = = 4. (64  .5(1 − .5))

 4For a simple random sample, the standard error for

a percentage equals 

. p(1−p)

N
Again, the standard error is used to

gauge how close the estimate is to the

actual frequency of occurrence. For

example, if the people in the sample

experienced a rape rate of 1.4 incidents per 1,000 people, then the

actual value in the population is close

to this, or is at least within the relevant

confidence interval. In the 1996

NCVS, about 132 of the 94,000 people

sampled experienced a rape, which

translates into a rate of 1.4 per 1,000

and a standard error of .14 per 1,000

people.5

 Therefore, the rate of occurrence of rape in the U.S. population is

between about 1.26 and 1.54 per

1,000 at the 68% confidence level, as

shown below.

Expected rate of rape victimization, 

for different confidence levels

95% 1.96 1.13 to 1.67 per 1,000

90% 1.645 1.17 to 1.63 per 1,000

68% 1.00 1.26 to 1.54 per 1,000

Likely number 

of rape victims 

in the population

 Number 

 of SEs

Confidence

level

Sample size = 94,000

Expected frequency of rape victims = 

 1.4 per 1,000

Standard error = .14 per 1,000

Sample characteristic: 

 1.4 rape victims per 1,000

The same approach can be used in

analyzing year-to-year changes. The

estimated 1995-96 change in the incidence of rape was a 17.65% reduction

(or -17.65%), with a standard error of

11.94%. This would produce the

following confidence intervals:

Sample percent change in the rate 

of rape victimization, for different 

confidence levels

95% 1.96 -41.05% to +5.75%

90% 1.645 -37.29% to +1.99%

68% 1.00 -29.59% to -5.71%

Likely 1995-96 

change in rape

incidence

 Number 

 of SEs 

Confidence

level

Expected change in rape rates = -17.65%

Standard error = 11.94%

Sample characteristic: 

 17.65% reduction in rape rates

Note that at both 90% and 95% confidence levels, the ranges include zero.6

Therefore, the possibility that there

was no true change cannot be

excluded so these results are not

statistically significant at those confidence levels. The apparent reduction

of 17.65% in rape incidence may just

reflect normal variation between the

sample and the population. When the

confidence interval does not include

zero (as with simple assault in the

figure on page 5), the year-to-year

change is (statistically) distinguishable

from zero and therefore is statistically

significant. 

Sample size

In the above examples no mention

was made of the size of the population, because the sample size is not

based on the size of the population

from which it was drawn. Estimating

the victimization rate in the United

States requires the same sample size

as estimating the victimization rate (at

the same precision) in, say, Illinois.

To understand why this is so, suppose

a jar contained black and white beans

that were thoroughly mixed.7

 If a

cupful of beans (the sample) was

scooped from the jar, close to the

same color proportions would be found

in the cup as in the jar. Now suppose

there was a carload full of thoroughly

mixed black and white beans in the

same proportions as in the jar. A

cupful scooped from the carload would

have close to the same color proportions as the carload, and the proportions of the two cups would be

expected to be equally close to the

proportions of their “parent”

populations.

In other words, the size of the population does not affect the proportions in

the cup, as long as the beans are thoroughly mixed. Randomly sampling

people throughout the country is the

logical equivalent of thoroughly mixing

the beans; since people around the

country cannot be “mixed” to distribute

all different types of people according

to the needs of the survey, the survey

“moves around” to accomplish the

same objective.

If the size of the sample is close to the

size of the population, the precision

improves. For example, suppose a

sample of 94,000 (as for the NCVS) is

used, but the city being sampled has a

population of 94,000 as compared to

the 280 million population of the

United States. In this case, the

sample’s victimization rate would be

precisely the city’s true victimization

rate, and the standard error would be 0

(perfect precision). Most samples are

only a small fraction of the population.

The standard error starts to shrink

when that fraction approaches 20% of

the population.8

 

The NCVS sample size is sufficiently

large to permit a limited amount of

disaggregation, depending on the

frequency of the event; the larger the

sample size, the larger the number of

incidents and the greater the ability to

estimate the variation in rate by

subcategories. The approximately 132

rape victimizations in the 1996 sample

may be adequate to furnish statistically

significant differences by race, but is

too small to determine whether variation by race and age is statistically

significant. For simple assault,

however, its rate of 26.6 per 1,000 is

based on approximately 2,500 events,

which is sufficiently large to estimate

variation by sex, race, and age

categories.

The effect of the sample size on standard error can be noted in the size of

the range bars in the figures on page 3

and in the SEs given in table 1. The

SE increased gradually from 1973 to

1991 as a result of decreases in the

sample size for budgetary reasons. In

1992, when the NCVS was redesigned, the SE was dramatically larger

than in prior years because of the use

of a half-sample. For calibration

 Displaying Violent Crime Trends Using Estimates from the NCVS 7

6

This is also noted in the figure on the top of page

5, where two of the three segments of the range

bar for rape (90% and 95%) cross the No change

line.

 7 Deming, W. E. “Sample Surveys: The Field.” In

The International Encyclopedia of Statistics, Vol.

2. The Free Press, New York, 1978, pp. 867-885.

Cited in Wright, T., “Sampling and Census 2000:

The Concepts,” American Scientist, 86, 3 (MayJune 1998), pp. 245-253.

8

Blalock, H. M., Jr., Social Statistics, 2nd Edition,

McGraw-Hill, New York, 1972, p. 514.

5

The SE is larger than would be expected if the

NCVS was a simple random sample; see 

Appendix II of the BJS report, Criminal 

Victimization in the United States, 1994.
purposes the old and new surveys

were each administered to half the

sample; in this way the effect of the

redesign on victimization rates could

be gauged. The two samples could not

be combined, so the estimates were

based on that half of the sample given

the new survey and are less precise. 

Nonsampling error

Sampling is not the only source of

error in NCVS data; however, it is the

only one whose magnitude can be estimated. Other sources of error in the

NCVS include:

Z Recall error: Respondents are

unable (or may be unwilling) to recall

victimizations and report them to

survey interviewers.

Z Recalling the exact month in which

the incident took place: Respondents

may be unable to place the incident in

the right month (since they are interviewed every 6 months, they may

remember not reporting it during the

previous interview, but not remember

the exact date of its occurrence).

Z Misclassification: Some noncriminal

incidents may be classified as crimes,

and some crimes may be classified as

noncrimes (or as other types of

crimes).

The NCVS is designed to minimize

these types of error. It seeks to

reduce recall error by using a relatively

short (6-month) recall period, so that

incidents would be fresh in the respondents’ minds, and by selecting interviewers whose race and ethnicity

generally match those of the population in the area where the respondent

resides (so that respondents would be

more inclined to report accurately). It

seeks to reduce misclassification error

by performing edit checks on the data

and coding procedures and by using

quality control procedures such as

randomly selecting interviews for callback and verification.

Other types of data collection also

have error. For example, the FBI’s

Uniform Crime Reports (UCR) is a

voluntary system and not all jurisdictions report crime data. To provide

national estimates of reported crime,

the FBI must account for the jurisdictions that did not provide data. They

estimate the crime for the nonreporting

jurisdictions based on similar jurisdictions that have reported. Since the

reporting jurisdictions are likely to

have experienced more crime than

those that did not report, the estimated

crime rate may be biased upward, but

the magnitude of this bias is unknown.

Appendix II. Data used in the

graphics

Table 1 contains the data used in

preparing the figures on pages 1-3.

The standard errors (SEs) are calculated using the formulas and procedures described in Appendix II of

Criminal Victimization in the United

States 1994. The data for B-Value

and ρ (the Greek letter rho) are

included for archival purposes since

they were used in calculating both the

SE for the violent crime victimization

rate and for its year-to-year change.

Note that the procedure for calculating

the SE changed in 1993, the year after

the NCVS underwent a major change

in its collection procedures. Table 2

contains the data (calculated from the

data in table 1) used to plot the figure

on page 4. Table 3 contains the data

used to plot the figure on page 5.

Some of the numbers in these tables

may vary from other published

numbers because of differing collection periods and the use of summed

rather than aggregate data. For the

most part, the differences do not affect

the conclusions made about the data.

 

8 Displaying Violent Crime Trends Using Estimates from the NCVS

1996 217,234.3 42.0 1.1 2096 0.22 39.88 40.22 40.92 43.08 43.78 44.12

1995 215,080.7 46.6 1.1 2008 0.31 44.38 44.74 45.47 47.73 48.46 48.82

1994 213,135.9 51.8 1.2 2008 0.31 49.43 49.81 50.59 53.01 53.79 54.17

1993 210,906.9 49.9 1.5 3253 0.22 46.94 47.41 48.39 51.41 52.39 52.86

1992 206,414.5 47.8 1.7 4157 44.57 45.09 46.15 49.45 50.51 51.03

1991 204,280.1 48.0 1.0 3743 46.04 46.36 47.00 49.00 49.65 49.96

1990 203,273.9 44.0 0.9 3196 42.33 42.60 43.15 44.85 45.40 45.67

1989 201,375.6 43.4 0.9 2958 41.73 42.00 42.55 44.25 44.80 45.07

1988 199,412.5 44.2 0.8 2790 42.63 42.88 43.40 45.00 45.52 45.77

1987 197,727.0 43.7 0.8 2755 42.13 42.38 42.90 44.50 45.02 45.27

1986 196,160.2 41.9 0.8 3478 40.33 40.58 41.10 42.70 43.22 43.47

1985 194,096.7 44.7 0.8 2597 43.13 43.38 43.90 45.50 46.02 46.27

1984 191,962.2 46.2 0.8 2318 44.63 44.88 45.40 47.00 47.52 47.77

1983 190,504.0 46.2 0.7 2073 44.83 45.05 45.50 46.90 47.35 47.57

1982 188,496.6 51.0 0.8 2073 49.53 49.77 50.25 51.75 52.23 52.47

1981 186,336.0 52.6 0.8 2073 51.03 51.28 51.80 53.40 53.92 54.17

1980 184,324.0 49.4 0.8 2142 47.93 48.17 48.65 50.15 50.63 50.87

1979 178,284.5 51.5 0.8 2256 49.93 50.18 50.70 52.30 52.82 53.07

1978 176,214.6 50.2 0.8 1986 48.63 48.88 49.40 51.00 51.52 51.77

1977 174,092.7 50.5 0.7 1603 49.13 49.35 49.80 51.20 51.65 51.87

1976 171,900.5 48.5 0.7 1603 47.13 47.35 47.80 49.20 49.65 49.87

1975 169,671.5 48.9 0.7 1603 47.53 47.75 48.20 49.60 50.05 50.27

1974 167,058.4 49.1 0.7 1603 47.73 47.95 48.40 49.80 50.25 50.47

1973 164,362.9 48.5 0.7 1603 47.13 47.35 47.80 49.20 49.65 49.87

95% 90% 68% 68% 90% 95%

Lower bound for — Upper bound for —

 ρ

 B- 

 SE Value

Rate

estimate

Population,

age 12+ (in

Year thousands)

Bounds on confidence levels

Table 1. NCVS violent crime estimates
Bibliography

Criminal Victimization, 1973-95, 

NCJ 163069, 4/97.

Criminal Victimization 1996: Changes

1995-96 with Trends 1993-96, 

NCJ 165812, 11/97.

Criminal Victimization in the United

States 1994, NCJ 162126, 5/97.

The Nation's Two Crime Measures,

NCJ 122795, 11/95, also presented on

pages 399-400 of the FBI’s Crime in

the United States, 1996.

About the NCVS redesign

The Effects of the Redesign on Victimization Estimates, NCJ 164381, 4/97.

National Crime Victimization Survey

(NCVS) Redesign: Press Release,

NCJ 151169, 10/94. 

National Crime Victimization Survey

(NCVS) Redesign: Fact Sheet , 

NCJ 151170, 10/94. 

National Crime Victimization Survey

Redesign: Technical Background, 

NCJ 151172, 10/94.

National Crime Victimization Survey

(NCVS) Redesign: Questions &

Answers, NCJ 151171, 10/94.

 Displaying Violent Crime Trends Using Estimates from the NCVS 9

1.7 0.1524 1.4 0.1387 0.03 -17.65% 11.94%

Rape and sexual

 assault

Robbery 5.4 0.3009 5.2 0.2994 0.03 -3.70% 7.74%

Aggravated assault 9.5 0.4239 8.8 0.4118 0.06 -7.37% 6.03%

3.59% Simple assault 29.9 0.8607 26.6 0.8151 0.18 -11.04%

Total violent 46.6 1.1322 42.0 1.0815 0.22 -9.87% 2.97%

SE of

percent

change

Percent

Rate SE Rate SE ρ change

Percent change

1995 data 1996 data 1995-96

Table 3. Changes in violent crime rates, by crime type, 1995-96

-41.05% -37.29% -29.59% -5.71% 2.00% 5.76%

Rape and sexual

 assault

Robbery -18.88% -16.44% -11.45% 4.04% 9.03% 11.47%

Aggravated assault -19.19% -17.29% -13.40% -1.34% 2.55% 4.45%

Simple assault -18.07% -16.94% -14.63% -7.45% -5.13% -4.00%

Total violent -15.69% -14.75% -12.84% -6.90% -4.99% -4.05%

95% 90% 68% 68% 90% 95%

Lower bound for — Upper bound for —

Bounds on confidence levels

1995-96 -9.87% 2.97% -15.69% -14.75% -12.84% -6.90% -4.99% -4.05%

1994-95 -10.04% 2.66% -15.25% -14.42% -12.70% -7.38% -5.66% -4.82%

1993-94 3.81% 3.24% -2.55% -1.53% 0.56% 7.05% 9.14% 10.16%

1992-93 4.39% 4.14% -3.72% -2.41% 0.26% 8.53% 11.20% 12.50%

1991-92 -0.42% 2.83% -5.95% -5.06% -3.24% 2.41% 4.23% 5.12%

1990-91 9.09% 3.01% 3.18% 4.13% 6.08% 12.11% 14.05% 15.00%

1989-90 1.38% 2.67% -3.84% -3.00% -1.28% 4.05% 5.77% 6.61%

1988-89 -1.81% 2.49% -6.70% -5.91% -4.30% 0.68% 2.29% 3.08%

1987-88 1.14% 2.50% -3.76% -2.97% -1.36% 3.64% 5.26% 6.05%

1986-87 4.30% 2.61% -0.82% 0.00% 1.69% 6.91% 8.59% 9.41%

1985-86 -6.26% 2.63% -11.42% -10.59% -8.90% -3.63% -1.93% -1.11%

1984-85 -3.25% 2.30% -7.76% -7.03% -5.55% -0.95% 0.54% 1.26%

1983-84 0.00% 2.24% -4.38% -3.68% -2.24% 2.24% 3.68% 4.38%

1982-83 -9.41% 1.88% -13.09% -12.50% -11.29% -7.53% -6.32% -5.73%

1981-82 -3.04% 1.95% -6.87% -6.25% -4.99% -1.09% 0.17% 0.78%

1980-81 6.48% 2.17% 2.22% 2.90% 4.30% 8.65% 10.05% 10.74%

1979-80 -4.08% 2.02% -8.04% -7.41% -6.10% -2.05% -0.75% -0.11%

1978-79 2.59% 2.24% -1.79% -1.09% 0.35% 4.83% 6.27% 6.97%

1977-78 -0.59% 2.06% -4.62% -3.98% -2.65% 1.46% 2.79% 3.44%

1976-77 4.12% 1.96% 0.27% 0.89% 2.16% 6.09% 7.36% 7.97%

1975-76 -0.82% 1.90% -4.54% -3.94% -2.72% 1.08% 2.31% 2.90%

1974-75 -0.41% 1.91% -4.16% -3.56% -2.32% 1.51% 2.74% 3.35%

1973-74 1.24% 1.97% -2.62% -2.00% -0.73% 3.20% 4.47% 5.09%

95% 90% 68% 68% 90% 95%

Lower bound for — Upper bound for — Percent

SE

Estimated

Period change

Bounds on confidence levels

Table 2. Year-to-year changes in violent crime
 

10 Displaying Violent Crime Trends Using Estimates from the NCVS

The Bureau of Justice Statistics 

is the statistical agency of the 

U.S. Department of Justice. 

Jan M. Chaiken, Ph.D., is director.

Marianne W. Zawitz, BJS statistician, and Michael Maltz, BJS Visiting Fellow and Professor of Criminal

Justice, University of Illinois at

Chicago, wrote this report. It was

edited by Tom Hester. BJS is grateful for the thorough reviews and

thoughtful comments provided by

Alfred Blumstein, Heinz School of

Public Policy and Management,

Carnegie Mellon University; Michael

Maxfield, School of Criminal

Justice, Rutgers University; Stephen

Fienberg, Departments of Statistics

and Social Sciences, and Laura J.

Dugan, Heinz School of Public

Policy and Management, Carnegie

Mellon University; John Richters,

Developmental Psychopathology

Research Branch, National Institute

of Mental Health; James Nolan and

Yoshio Akiyama, Federal Bureau of

Investigation; Jay Hoover, Office of

Policy Development, and Stephen

Shandy, Criminal Division, U.S.

Department of Justice. Within BJS,

assistance was provided by

Lawrence Greenfeld, Michael Rand,

and Cheryl Ringel.

June 1998, NCJ 167881

This report belongs to the BJS 

Technical Report series. The previous publication in the series was

Effects of the Redesign on Victimization Estimates, April 1997,

NCJ164381. Readers who want

additional data, analyses, and

graphs about criminal victimization

in the United States should access

the BJS Internet Web site: 

http://www.ojp.usdoj.gov/bjs/.

Data presented in this report may be

obtained from the National Archive

of Criminal Justice Data at the

University of Michigan, 1-800-999-

0960. The archive may also be

accessed through the BJS Web site.

When at the archive site, search for

data sets ICPSR 6406, ICPSR 7635,

ICPSR 8608, and ICPSR 8864.