Are Late/Difficult Cases in Demographic Survey Interviews 

Worth the Effort?

 A Review of Several Federal Surveys 

Detis Duhart, Bureau of Justice Statistics, Nancy Bates, Barbara T. Williams, and 

Gregg Diffendal, Bureau of the Census1, and Pei Lu Chiu, National Center for Health Statistics 

Abstract 

Government statistical agencies currently expend a great deal of time and resources to keep survey nonresponse at or 

below current levels. This paper uses data from the American Housing Survey - National (AHS-N), the Current 

Population Survey (CPS), the National Crime Victimization Survey (NCVS) and the National Health Interview Survey 

(NHIS) to explore the characteristics of late or ‘difficult’ cases that comprise the last few percentage points of survey 

response rates. It also examines the household and person-level demographic characteristics of late and early cases. In 

addition, to determine if critical survey estimates are affected by late cases, we run the estimates with and without these 

data. Bates and Creighton (2000), in their examination of the NCVS and CPS, found that a portion of late cases are 

similar to nonrespondents. They also found the magnitude of the difference between estimates with and without these 

cases is usually small; however, in some cases the estimates are significantly different without the late cases. This study 

extends their research by examining results from several federal surveys. 

1. Introduction 

Federal statistical organizations strive to conduct household surveys with high response rates in a 

timely fashion. However, the task is becoming increasingly difficult in the wake of a less

cooperative public, the popularity of access impediments (e.g. caller ID and answering machines), 

and other societal and environmental factors contributing to lower response rates. Considering this 

trend, it is becoming increasingly important to understand the impact that lower response rates may 

have on official surveys and whether it is worth the time and effort to pursue the last few cases. 

Recent studies by Keeter et al. (2000) and Curtin et al. (2001) have called into question the generally 

held notion that the higher the nonresponse rates, the more biased the survey data. The Keeter 

(2000) study found surprisingly few differences between data gathered from identical phone 

questionnaires -- one using a less rigorous method that achieved a 36 percent response rate and the 

other a more rigorous method with a 60.6 percent response rate. The Curtin et al. study examined 

both cross-sectional and time series estimates from the Survey of Consumer Attitudes between 1979 

and 1996. Again, somewhat surprisingly, the authors found that one measure, the Index of 

Consumer Sentiment, changed very little after the initial refusers and higher contact cases were 

removed from the datasets (thereby allowing comparisons of estimates based on lower response 

rates). 

Our paper explores late interviews across four federal household surveys. We attempt to answer two 

basic questions surrounding these cases. First, are there demographic similarities among the late 

1

This paper reports the results of research an analysis undertaken by Census Bureau staff. It has undergone 

a Census Bureau review more limited in scope than that given to official Census Bureau publications. This report is 

released to inform interested parties of ongoing research and to encourage discussion of work in progress. 
 

 

 

 

 

 

 

 

 

 

 

 

interview households across surveys and are they different from the earlier (and presumably easier) 

cases? Second, would critical survey estimates be impacted if these cases were not represented in 

the data? That is, what would be the result if we reduced the time and resources to go after these 

households, and consequently, allowed nonresponse to climb slightly? If we find that these 

interviews exhibit unique demographic traits, their absence would intrinsically seem to adversely 

bias certain estimates. For example, if difficult/late interviews tend to overrepresent younger 

persons, we might see a shift in employment rates or certain health statistics if they are no longer 

included. 

The four surveys studied here include the May 1999 Current Population Survey (CPS) , two quarters 

of the 1999 National Crime Victimization Survey (NCVS) , the 1999 American Housing Survey -

National (AHS-N), and the 1998 National Health Interview Survey (NHIS). Results from the CPS 

and NCVS draw froma previous paper byBates and Creighton (2000). Newly documented findings 

from the NHIS (see Chiu, Riddick, and Hardy, 2001) and AHS (Williams, 2001) are summarized 

along with findings from the previous two analyses. 

2. Survey Descriptions 

The four surveys studied differ significantly in sponsors, subject matter, and design features. One 

commonality is that the U.S. Census Bureau acts as the data collection agent in all four surveys. The 

AHS-N is a longitudinal survey of housing units (both occupied and vacant) sponsored by the U.S. 

Department of Housing and Urban Development (HUD). The survey asks questions such as home 

ownership, housing/rent value, number of rooms, and utility costs. The survey typically interviews 

one adult resident per unit and interviews are conducted by both personal visit and telephone. 

Interviews are conducted biennially with approximately 46,600 households and the survey is in the 

field for 16 weeks. The response rate for the 1999 AHS-N was 90.8 percent. 

The CPS provides monthly labor force estimates and annual data on poverty, income, and work 

experience. It is conducted monthly with approximately 50,000 households – interviews are carried 

out by personal visit and telephone. Most interviews are conducted during a one-week period 

(Sunday to Saturday) that includes the 19th of the month. Like the AHS-N, the interview is typically 

conducted with a single household respondent who provides information for other household 

members. In 1999, the annual average response rate for the CPS was 93 percent. 

The NCVS is sponsored by the Bureau of Justice Statistics, is conducted semiannually, and is used 

to provide estimates of crime victimization. Interviews are conducted at 56,000 households and 

within a household each person aged 12 and over is interviewed. The survey uses both personal visit 

and telephone interviews. The annual average response rate in 1999 was 93.6 percent. 

The NHIS is sponsored by the National Center for Health Statistics and collects data of general 

health information annually from the U.S. civilian household population. One adult is interviewed 

for household questions while other selected adults respond to specific sections of the questionnaire. 

Interviews are generally conducted by personal visit. In 1998, the household response rate was 

90.0 percent. The survey is conducted over a two week span. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

3. Operationalizing “Late/Difficult” Interviews 

Each of the four surveys maintains different degrees of history information regarding number of 

contact attempts, number of contacts made, and outcome details of each interim contact. 

Unfortunately, in most cases this type of information is extremely limited or is not kept in electronic 

format along with the interview data. This, coupled with the fact that each survey varies in terms 

of field length, means that the definitions used to categorize a case as difficult or late also varied 

across the four surveys. Table 1 contains a summary of definitions used for each. 

The AHS-N is somewhat unique in that it has a long field period (16 weeks from August to midNovember). Interviewers receive their entire assignment for the survey at one time. The instrument 

captures date of interview but does not collect detailed information on the number of attempts prior 

to making contact nor does it store interim outcomes of prior contacts (e.g., soft refusal, 

appointments set for a later date). Consequently, the AHS-N cases could only isolate late interviews 

based on date of interview. November 4th was the cutoff date used to define the last five percent of 

interviews – interviews completed after this date were labeled late interviews and essentially cover 

interviews completed during the last two weeks of data collection. We cannot assume, however, that 

these were necessarily “difficult” or required an extraordinary number of contacts. However, 

anecdotal comments from interviewing staff along with monthly cost figures, suggest that interviews 

conducted during the last month are the most costly, presumably because they required a greater 

number of trips due to inability to contact or required refusal conversions. Late cases (as defined 

here) accounted for 4.9 percent of the completed interviews. 

Whenconstructing the definition for CPS, we had access to one additional variable: number ofactual 

and attempted personal contacts. However, the instrument only records this information for personal 

visits (CPS allows telephone interviews for certain months in sample). Additionally, for the counter 

to function properly, interviewers must access and open the case from the laptop. We have no way 

to know how often interviewers may visit an address, fail to make contact, but never open the laptop. 

Such cases will underestimate the number of contacts. Late cases for CPS were defined as those 

conducted during the Tuesday or Wednesday of the second week of data collection. Difficult cases 

were those requiring four or more personal contacts. After combining these two criteria, 4.6 percent 

of the May 1999 CPS interviews met the definition of a late or high contact (difficult) case. 

The NCVS routinely collects information on the record of personal visits and telephone calls made 

for each case. However, this information is stored separately from the electronic survey data. 

Consequently, like the AHS-N, the NCVS definition was limited to date of interview. Late 

interviews were defined as those completed fromthe 17th ofthe month until regional office closeout 

(approximately the last seven days of data collection). These cases comprised 5.2 percent of the 

person-level interviews for the two quarters of 1999 data examined. 

The NHIS used a combination of late and difficult interviews. Late interviews were those requiring 

additional time beyond the standard two week field period (because of refusal conversion efforts or 

prolonged non-contact cases). Difficult-to-contact interviews were defined based on the number of 

interviewer-reported personal visits required to complete the case. Cases requiring 10 or more 

personal visits were considered difficult to contact. Late cases combined with difficult cases 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

accounted for approximately 7 percent of the 1998 NHIS interviews. It should be noted that there 

was surprisingly little overlap between the two categories – only around 12 percent of the cases 

completed beyond the normal two week period also required more than 10 interviews. 

4. Limitations 

Before a discussion of findings, we caution of four serious limitations to the study data and analysis.

 First, the lack of consistent and detailed interview case-history information results in ambiguities 

surrounding the conceptual and operational definition of late/difficult interviews. For example, 

without knowledge of the number of contacts (attempted and made) and without interim attempt 

outcome codes (no one home vs. soft refusal vs. appointment made), how does one go about 

differentiating the truly difficult interviews from those that were merely hard to reach? Since we 

lack such information, our definitions are admittedly open to question. There are many reasons why 

late interviews and those requiring multiple contacts are not necessarily the same as reluctant or 

resistant interviews. For example, how far into the field period the interviewer makes the first call, 

the number of calls by day, the size of the household, interviewer workload and number of refusal 

conversions in the interviewers’ assignment all contribute to how early or late a case may be 

completed. And, in fact, Groves and Couper’s work (1998) heavily reinforces the notion that 

separating out different types of survey nonresponse (in particular, noncontacts and refusals) is 

crucial to understanding the phenomenon. 

Unfortunately, having only date of interview (and in a few instances number of personal visits) 

precludes us from isolating prolonged noncontact groups from initial refusal groups, both of whom 

may end up in our definition of a late interview. Nonetheless, since these are the real conditions our 

surveys are currently subject to, our only alternative is to combine them under the somewhat 

ambiguous and undifferentiated label of “late.” We concede this weakness but stop short of 

declaring it a fatal flaw to our study. 

A second limitation stems from the high response rate all four surveys routinely achieved for the 

periods studied. Compared to non-government surveys, the four surveys have above average 

response rates (in the low 90's). Consequently, the removal of 5-7 percent of the late/difficult 

interviews still yields a high response rate by industry standards. Readers are cautioned that the last 

few interviews froma survey with a response rate in the 90's may be very different fromthe lastfew 

from a survey with an initial response rate of say, 60-70 percent. 

Third, we explore later in the paper whether survey estimates might be different with and without 

late/difficult interviews included. Our method for doing so is to compare selected estimates based 

on all the interviews to those re-calculated without the late/difficult cases. Since the percentage of 

late cases is small, the overlap of cases used to produce both estimates is very large making for a 

high correlation between estimates. Consequently, the variances are close to zero and therefore even 

slight differences between estimates will test as significantly different. Our intent is not to make 

concrete inferences regarding estimates in the absence of late interviews but rather to take a broad 

view across all four surveys studies to see if any meaningful patterns of differences are detected. We 

also make a point of interpreting the differences from a practical or meaningful standpoint, not 

merely from a statistical one. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

5. Results 

Table 2 summarizes relationships between selected household demographic characteristics and the 

propensity of being a late/difficult interview. Findings reported for the AHS-N are the result of 

bivariate crosstabulations while those from the CPS, NCVS and NHIS are the result of multivariate 

models predicting the likelihood of being in a late/difficult interview controlling for other 

independent variables. For a more detailed discussion of the variable descriptions and methods, see 

Bates and Creighton (2000), Chiu, Riddick and Hardy (2001) and Williams (2001). 

In all four surveys, it is clear that late/difficult interviews are less likely to contain persons aged 55+ 

(or 65+ as defined in the NHIS). This is probably related to ease of contact where interviewers have 

greater success finding someone at home in households containing older persons. Topic saliency 

of the NHIS may also explain earlier cooperation rates among seniors with older persons have more 

health-related incidents to report and therefore greater interest the survey. 

Conversely, single person households were more likely to fall into the late/difficult category for both 

the AHS-N and the NHIS (the CPS and NCVS study did not include this variable). This is consistent 

with the survey nonresponse literature that suggests non-contact rates are much higher for single 

person households (Groves and Couper, 1998). This premise is also supported by additional 

breakouts of the late versus difficult interviews in the NHIS. Chiu et al. (2001) report that single 

person households comprise about 36 percent of the interviews requiring 10 or more visits (difficult 

interviews) compared to 28 percent of the interviews conducted after formal closeout (late 

interviews). 

We see a positive association between renting and being a late/difficult interview in all four surveys. 

This may be a reflection of the types of people who reside in rented apartments and other multi-unit 

structures, that is, younger people or single person households. Such structures may also have a 

greater incidence of access impediments such as doormen or locked entry ways requiring access 

codes. 

The relationship between race/ethnicity and the likelihood of being a late/difficult interview is less 

obvious than the previous characteristics in Table 2. For three of the four surveys, Hispanic origin 

was not found to be significant but in the AHS-N, CPS and NCVS, Blacks were disproportionately 

found more often in the late/difficult interview category. For the NHIS, more Hispanics and Blacks 

were found in the late/difficult interviews, however the effects of race and ethnicity became nonsignificant once other geographic and social environmental factors are controlled. We should also 

mention that while race was significant in the models run for the CPS and NCVS, these models were 

run at the person-level and did not control for variables such as household size and urbanicity. Thus, 

our findings are somewhat inconsistent regarding the degree to which race is associated with 

late/difficult interviews, but as a whole, suggest race may be a weaker predictor than others. 

Previous research by Curtin, Pressor, and Singer (2000) suggests that lower socioeconomic 

households may disproportionately comprise initial refusals while more affluent households are 

disproportionately non-contacts. All four surveys contained some measure of wealth, albeit they 

were quite different. The AHS-N examined categories of household income as well as housing value 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

of owner occupied units. For both measures, affluent households were over-represented in late 

interviews. Likewise, the CPS and NCVS both found positive relationships between higher levels 

of household income and the likelihood of being of late/difficult interview. For the NHIS, the effect 

of income was not a significant predictor once other factors were held constant. However, it is 

interesting to note that in the CPS, NCVS and NHIS, the proclivity to refuse income information 

(or answer DK) was higher in late/difficult interviews. This correlation between item nonresponse 

and “lateness” could reflect some degree of reluctance or reduced cooperation in these interviews. 

Three of the surveys included urbanicity in their investigation of late/difficult interviews. All three 

found that households located in urban areas are more likely to fall into the late/difficult category. 

There are many explanations for this finding including fear of crime, population density, and the 

types of households that populate urban areas (e.g., single person households, childless households 

and younger households). 

Based on Table 2, can we draw any conclusions whether the late/difficult interviews resemble 

nonrespondents? We revisit the question posed at the beginning of the paper, that is, are there 

demographic similarities among the late/difficult interview households across surveys and are their 

characteristics different from the non-late (and presumably easier) cases? With the exception of 

ethnicity, we found evidence of significant differences between the characteristics of late/difficult 

cases and the non-late/non-difficult cases. Further, the surveys exhibited similarities in their pattern 

of differences. 

Late/difficult interviews are lesslikelyto contain older household members but more likely to reflect 

single person households, households that rent, are more affluent, have Black household members, 

and/or are located in an urban area. With the exception of race and tenure, these characteristics have 

been documented by Groves and Couper (1998) as predictors of nonresponse in household surveys. 

But, given the somewhat arbitrary nature of our definitions and other data limitations, we refrain 

from making the assumption that our late/difficult interviews are indicative of noninterviews. More 

likely, our late/difficult interviews represent some combination of hard-to-contact and initial refuser 

households that eventually do cooperate if field resources permit the extra efforts required to 

complete them. Therefore, we can more safely assume they represent potential non-respondents, but 

not necessarily those who never participate. 

6. Impact on Estimates 

In this section, we address the second question of interest – would survey estimates be impacted 

significantly if late/difficult interviews are not included? We begin illustrating the potential 

differences by presenting distributions of critical survey estimates by non-late/non-difficult cases 

versus the late/difficult cases (see Table 3). For the AHS-N we present homeownership rates and 

housing stress rates. Housing stress is defined as a high level of housing costs as a percent of current 

income. We use HUD’s definition of high levels as being 31 percent or greater. For the CPS, we 

include a distribution of labor force participation which is used to calculate the unemployment rate. 

For the NCVS we examine personal and property crime victimization incidents (number of reports 

and type of crime). For the NHIS we examine health insurance coverage rates and limitation of 

physical activity. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The distributions illustrate various degrees of difference between non-late and late interviews. For 

example, late cases exhibit lower homeownership rates but a slightly higher housing stress rate. 

Differences in number of reported crimes and type of crime by type of interview are slight, but larger 

disparities are evident in both labor force participationand the health-related items. Late interviews 

reflect a higher percentage ofpersons currentlyinthe laborforce and fewer retirees. Late interviews 

also reflect a lower rate of insured persons but also a lower rate of persons with some type of healthrelated physical limitation. These findings follow logically from the age disparities noted previously 

between early and late interviews. 

Our last step was to compare estimates of population characteristics from Table 3 calculated with 

and without the late/difficult interviews. In order to check for significant differences, late cases were 

converted to noninterviews and the data were re-weighted for nonresponse. Tests for significant 

differences between estimates with and without late cases were calculated using the standard error 

for late cases only since the overlapping cases from both sets add nothing to the variance. Survey 

design effects were taken into account when testing for differences. To reduce the possibility of 

Type I error and compensate for the high correlation between estimates, we used an alpha of .01 as 

the threshold for determining statistical significance between estimates (as opposed to the Census 

Bureau standard of .10). 

Across all estimates, the magnitude of difference is very small between estimates with and without 

late/difficult cases. This is somewhat expected considering both sets contain anywhere from a 93-95 

percent overlap. Thus, the practical impact of excluding these 5-7 percent of cases appears minimal. 

However, for some measures (notably the unemployment rate), a change in the magnitude of onetenth of one percent can still be meaningful and impact policy-making. Additionally, the direction 

of differences are interesting to note. For example, housing stress rates are lower without late cases 

while unemployment rates are higher. Crime rates are consistently higher without late cases. And 

finally, physical limitation estimates of the population are slightly higher without late cases 

represented. Differences in non-late and late interview characteristics such as age may explain these 

patterns. 

7. Practical Implications 

Despite differences in operational definitions across surveys, we discovered some commonalities 

in the household characteristics of late/difficult interviews. These discoveries hold some practical 

implications when trying to keep nonresponse in check. For example, interviewers should determine 

attributes about the sample household as early as possible. Cases having characteristics of 

late/difficult interviews such as urban addresses, multi-unit structures, and efficiency apartment 

complexes, should be assigned early on since they may house a disproportionate number of single 

person households, young households, and renters. If interviewers observe proxy indicators of easy 

contactability and cooperation (suchas neighborhoods or enclaves of older residents), theymaywish 

to start these later in the interview period, thus allowing more time for the hard to contact. 

A second practical implication is to study the correlates of late/difficult interviews and consider the 

bias they may have on certain survey estimates. For example, the impact of losing a 

disproportionate number of urban interviews will have different repercussions on a housing survey 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

versus a crime survey. Likewise the loss of younger people may effect an employment survey 

differently than a health survey. Knowing what kinds of households are likely to be late/difficult 

cases also helps us understand how to design better nonresponse adjustments in the absence of these 

cases. They also suggest how an extra 5 percent nonresponse may effect the degrees of bias for 

different age and race subpopulation estimates. 

Finally, a critical recommendation became obvious as a result of preparing this paper -- agencies 

should begin to routinely collect and retain case history information of interviewer contact attempts. 

This should include number of contacts and attempted contacts, interim outcomes that differentiate 

non-contacts from initial refusers, and final outcome codes that differentiate the same. Admittedly, 

this information has been difficult to capture under the DOS-based CAPI environment currently used 

at the Census Bureau. However, as Census migrates its CAPI surveys to the Windows-based Blaise 

software, the task will hopefully become easier. Such information will provide a wealth of data 

currently unavailable and allow us to disentangle the truly difficult interviews from the harder to 

contact. If this can be achieved, we suggest repeating the analysis presented here to more accurately 

assess the impact that growing nonresponse rates may have on survey quality, nonresponse bias, and 

official survey estimates. 

References 

Bates, N. and Creighton, K. (2000). “The Last Five Percent: What Can We Learn From 

Difficult/Late Interviews?” Proceedings of the Section on Government Statistics and Section on 

Social Statistics, American Statistical Association, pp. 120-125, Alexandria: VA. 

Chiu, P., Riddick, H. and Hardy, A. (2001). “A Comparison of Characteristics between 

Late/Difficult and Non-Late/Difficult Interviews in the National Health Interview Survey.” A paper 

to be presented at the American Statistical Association Joint Statistical Meetings, August 5-9, 

Atlanta, GA. 

Curtin, R., Presser, S. and Singer, E. (2000). The Effects of Response Rate Changes on the 

Index of Consumer Sentiment. Public Opinion Quarterly, Vol 64, 4, pp. 413-428. 

Groves, R. and Couper, M. (1998). Nonresponse in Household Interview Surveys. New 

York: Wiley. 

Keeter, S., Miller, C., Kohut, A., Groves, R. and Presser, S. (2000). Consequences of 

Reducing Nonresponse in a National Telephone Survey, Public Opinion Quarterly, Vol. 64, 2, pp. 

125-148. 

Williams, B. (2001). “Comparison of Early and Late Interviews from the 1999 American 

Housing Survey,” U.S. Census Bureau internal memorandum. 
Table 1. Definition of Difficult/Late Cases 

Survey 

Number of 

Contacts Cutoff Date Cutoff 

% “Late/Difficult” 

Cases 

AHS -N not available Nov. 4th (last 2 weeks of data collection) 4.9% 

CPS 4+ personal visits Tues.& Wed. of 2nd week 4.6% 

NCVS not available Approx. 17th of the month (last week of data collection) 5.2% 

NHIS 10+ contacts 16+ days after assignment 7.0% 

Table 2. Demographic/Household Characteristics and How They Relate to Late/Difficult Interviews 

 

Survey 

Characteristic: AHS-Na

 CPSb NCVSb NHISb

 Older person(s) in household — — — — 

1-person household ++ ++

 Renters ++ ++ ++ ++

 Hispanic member(s) ++ n/s n/s n/s

 Black member(s) ++ ++ ++ n/s

 High Income/Housing Value ++ ++ ++ n/s

 In central cities ++ + + ++ 

a

 associations based on bivariate analysis. b associations based on multivariate analysis. 

— = significant, negative relationship, ++ = significant, positive relationship, n/s = non-significant relationship 

Table 3. Distribution of Selected Survey Items by Non-late/Non-difficult and Late/Difficult Interviews 

AHS-N CPS NCVS NHIS 

pNot Late Not Late Not Late Not Late value 

Survey Measure late late late late 

Homeownership:

 Owner 67% 58% 

Renter 31% 40%

 No cash rent 2% 1% <.001 

High Housing Stress:

 Yes 27% 30% 

No 73% 70% <.01 
 

(Table 3 con’t.) Not 

late 

Late Not 

late 

Late Not 

late 

Late p-value 

Labor Force Status:

 Employed -at work 62% 69%

 Employed -absent 2% 2%

 Unemployed 3% 3%

 Retired 16% 8%

 Disabled 4% 3% 

Not working -other 13% 15% <.001 

# Crime Incidents:

 None 89% 89%

 One 9% 9%

 Two 1% 2%

 Three or more 

Type of Crime:

 1% 0% >.01

 Personal 24% 23% 

Property 76% 77% >.01 

Health Insurance:

 Yes 85% 80% 

No 14% 17%

 DK/Refused 

Activity limitation:

 1% 3% <.001 

Yes 13% 8% 

No 87% 92% <.001 

Table 4. Estimates with and without late/difficult interviews 

Estimates for Total Population

 All Cases Without Late Cases

 Homeownership Rate

 Housing Stress Rate

 66.92%

 27.25%

 66.90%

 27.10%*

 Unemployment Rate 4.05% 4.10%* 

Person Crime Ratea 

Property Crime Rate 

Violent Crime Rate

 36.8

 209.1

 35.8

 37.2

 212.0**

 36.2 

Health Insurance Rate 

Activity Limitation Rate

 84.33%

 12.65%

 84.44%

 12.80%** 

a

 Crime rates reflect victimization rates per 1,000 people

 Difference significant a the *.01, **.001 level.