\n Note that graphics included below are for illustration purposes only.
For actual data, visit the
\n Have questions?
askbjs@usdoj.gov \n
\n Compare estimates over time—across crime types or across groups within a demographic or incident characteristic.\n
\n\n Compare estimates for a single data year—of multiple crime types and a demographic or incident characteristic or of two different characteristics.\n
\n\n Compare estimates between two data years—for crime types with a demographic or incident characteristic or for two different characteristics.\n
\nThese crimes include violent crimes (rape/sexual assault, robbery, aggravated assault, and simple assault) and personal theft/larceny.
\nProperty crimes include burglary, trespassing, motor vehicle theft, and other theft.
\nThis is an estimation of the number of personal or property victimizations that U.S. residents age 12 or older or households experienced in the selected year.
\nVictimization rates are calculated by multiplying the number of victimizations by 1,000 and dividing that product by the number of persons age 12 or older. In the case of property victimizations, the number of victimizations is multiplied by 1,000 and the product is divided by the number of households.
\nPercentages show the portion of victimizations that occurred for a particular characteristic—that is, the number of victimizations for a characteristic divided by the total number of victimizations and multiplied by 100.
\nThe focus of comparison is on values between different crime types.
The focus of comparison is on different values within a characteristic of victimization.
\nA characteristic refers to information about the crime regarding either the demographics of the victim or household, such as the victim’s age, or details related to the incident, such as whether the crime was reported to the police. These are more broadly referred to as demographic or incident characteristics (see below for a full list).
\n\n This tool allows for comparisons between groups within a specific characteristic, or filtering information by a characteristic. To compare between groups, users can select a category under Comparison Characteristic in the Display Settings area, such as examining the rate of simple assaults between different victim race/Hispanic origins. To filter results by a particular characteristic, select Filter Characteristic under the Data Filter Settings area.\n
\n Once a category is selected, the associated selection value will auto-populate with a value (i.e., “65 or older”), which can then be changed, narrowing the data shown to the chosen group. Unit and Display Settings for charts will automatically update to those appropriate to the one of these two types selected.\n
\nIncludes: injury, location of incident, medical treatment for physical injuries, reporting to the police, victim-offender relationship, victim services use, and weapon category (presence and type of weapon).
\nBy default, incident characteristics are calculated as a percentage of the crime type.
\nIncludes: victim age, victim household income, victim household size, victim marital status, victim MSA (metropolitan statistical area) status, victim population size, victim race/Hispanic origin, victim region, and victim sex.
\nBy default, demographic characteristics are calculated as rates.
\nWhen not altered by other selections, a menu selection of “All” receives demographic-specific settings.
\n\n Sets personal versus property victimizations. When altered, available crime types will automatically populate with the associated values. For charts that display a single crime type, the default value applied is the aggregate (“Violent victimizations” or “Property victimizations”), which can then be changed through the “CRIME TYPE” menu.\n
\n\n When the characteristic selected is a demographic variable, this setting will default to “Rate” to account for each group’s representation within the population. When an incident characteristic is selected, this setting will default to “Percent” for percent of the crime type in which these incidents occurred. A toggle to change unit to “Number” is available for all settings.\n
\n\n A show/hide selection. Indicates “interpret data with caution.” See chart notes for symbol designations.\n
\n\n A show/hide selection for the 95% confidence interval.\n
\n\n A show/hide selection. Lines indicate the rate for “All” when making comparisons across demographics.\n
\nWhen a comparison characteristic or crime type selection is available in the Display Settings area, it determines the topic of comparison for the chart (rows or columns).
\n Default Setting Example:Menu items vary based on the chosen view. When a characteristic or crime type selection is available in the data filter settings area, the data used for the chart will be isolated to a selected crime type or characteristic value.
\nThe title and data values for a chart will change, but the chart’s structure will not.
\n Unfiltered Example:\n The\n respondent’s age on the last day of the month before the interview. The NCVS\n collects information on household members age 12 or older.\n
\n\n The N-DASH uses the following coding for age:\n
\n\n An\n attack or attempted attack with a weapon, regardless of whether the victim is\n injured, or an attack without a weapon when serious injury results.\n
\n\n The threat, attempt, or intentional infliction of bodily injury. Assault may be classified as aggravated or simple. Excludes rape, attempted rape, sexual assault, robbery, and attempted robbery. The severity of assault ranges from minor threats to near fatal incidents.\n
\nIncludes unlawful or forcible entry or attempted entry of places, including a permanent residence, other residence (e.g., a hotel room or vacation residence), or other structure (e.g., a garage or shed). Includes only crimes where the offender committed or attempted a theft.
\n\n Confidence intervals are another measure of the margin of error. For rates and counts, a confidence interval around the estimate can be generated by multiplying the standard errors by ±1.96 (the t-score of a normal, two-tailed distribution that excludes 2.5% at either end of the distribution). Therefore, the 95% confidence interval around an estimate is the estimate ± (the standard error X 1.96). In other words, if different samples using the same procedures were taken from the U.S. population, 95% of the time the estimate would fall within that confidence interval.\n\n For percentage estimates, the logit transformation is used to improve coverage and to ensure the confidence limits are on the interval [0,1]. The logit transformation confidence interval is calculated as\n
\n Refers to personal victimizations (rape/sexual assault, robbery, aggravated assault, simple assault, and personal theft/larceny) or property victimizations (burglary, trespassing, motor vehicle theft, and other theft).\n
\n\n The NCVS classifies victimizations and incidents based on detailed characteristics of the event provided by the respondent. Neither victims nor interviewers classify crimes at the time of interview. During data processing, a computer program classifies each event into one type of crime based on the entries for some items on the survey questionnaire. This ensures that similar events will be classified using a standard procedure. The definition for each crime indicates the major characteristics required for classification. If an event can be classified as more than one type of crime, the NCVS uses a hierarchy that classifies the crime according to the most serious event that occurred. The hierarchy is as follows:\n
\n\n A demographic characteristic refers to information about the demographics of the victim or household, such as the victim’s age. The N-DASH includes:\n
\n\n There are two types of estimate flags used in the N-DASH. One (▲) is used to flag estimates that are based on 10 or fewer sample cases or have a coefficient of variation of at least 50%. The other (✱) is used to flag estimates having a weighted number equal to 0 victimizations, rates that round to less than .05 per 1,000, or percentages that round to less than .5%.\n
\n\n A person who describes himself or herself as Mexican American, Chicano, Mexican, Mexicano, Puerto Rican, Cuban, Central American, South American, or from some other Spanish culture or origin, without considering race.\n
\n\n A person or group of people meeting either of the following criteria:\n
\n\n The total income of the household head and all members of the household for the 12 months preceding the interview. Includes wages, salaries, net income from businesses or farms, pensions, interest, dividends, rent, and any other form of monetary income. Income was imputed beginning in 2015, therefore, there is no Unknown category on the household income variable for 2015 or forward. For more information, see
\n The N-DASH\n uses this coding for household income:\n
\n\n The total number of people residing in the household. Includes household members under age 12.\n
\n\n\n The N-DASH\n uses this coding for household size:\n
\nAn incident characteristic refers to information about the crime regarding details related to the incident, such as whether the crime was reported to the police. The N-DASH includes:
\n\n\n A measure of whether bodily hurt or damage was sustained by a victim as a result of a criminal victimization. This applies only to personal victimization where there was contact between the victim and the offender. Injuries suffered from an attack during a crime incident include any and all physical (bodily) damage experienced by the victim (e.g., broken bones, bruises, cuts, and internal injuries). Emotional and psychological trauma are not included. The N-DASH uses this coding for injury:\n
\n\n\n Specifies where the victimization occurred.
\n The N-DASH uses this coding for location of incident:\n
\n The status of an individual in relation to marriage, classified as follows:\n
\n\n The N-DASH uses this coding for marital status:\n
\n\n Victims who were injured during an incident are asked whether they received any medical care for their injuries. Medical care includes treatment rendered by trained professionals, paraprofessionals, and nonprofessionals, and self-treatment.\n
\n\n\n The N-DASH\n uses this coding for medical treatment for physical injuries:\n
\n\n The\n unlawful taking or attempted taking of a self-propelled road vehicle owned by\n another, with the intent of permanently or temporarily depriving the owner of\n possession. Excludes vehicle parts.\n
\n\n Classification of respondent residence based on the Office of Management and Budget (OMB) definition of metropolitan statistical areas (MSAs). The OMB defines MSA as a population nucleus of 50,000 or more, generally consisting of a city and its immediate suburbs, along with adjacent communities having a high degree of economic and social integration with the nucleus. A principal city is the largest city in each MSA. Additional cities qualify if specified requirements are met concerning population size and employment.
\n The N-DASH uses this coding for MSA:\n
\n The\n perpetrator of a crime. This term usually applies to crimes involving direct\n contact between the victim and the offender.\n
\n\n Includes other unlawful taking or attempted unlawful taking of property or cash without personal contact with the victim.\n
\n\n Includes purse-snatching or pocket-picking. Theft or attempted theft of property or cash directly from the\n victim by stealth, without force or threat of force.\n
\n\n An\n offense category consisting of rape/sexual assault, personal robbery, aggravated assault and simple assault,\n and personal theft/larceny. Includes both attempted and completed\n crimes.\n
\n\n The size\n range for the place in which the housing unit is located. "Not a\n place" is a concentration of population that is either not legally bounded\n as an incorporated place having an active government or not delineated for\n statistical purposes as a census designated place with definite geographic\n boundaries, such as a city, town, or village. Population size is not available\n for years 1993-1995.\n
\n\n\n The N-DASH\n uses this coding for population size:\n
\n\n Includes\n burglary, trespassing, motor vehicle theft, or other theft. Includes both attempted\n and completed crimes. Property victimizations measure crimes against households. Each time a household is affected by a property crime, it is counted as a single victimization.\n
\n\n Beginning in 2003, BJS implemented methodological changes to reflect new guidelines from the Office of Management and Budget (OMB) for the collection and reporting of race and Hispanic origin data in government surveys. These implementations caused changes to the “Other” race category. Prior to 2003, the “Other” race category included American Indian/Aleut Eskimo, Asian/Pacific Islander, and other races. Since 2003, the “Other” race category has included American Indian/Alaska Native, Asian, Native Hawaiian/Other Pacific Islander, other races, and two or more races. For the National Crime Victimization Survey, respondents may self-identify with one or more racial categories. See also “
\n Racial categories are defined by the Office of Management and Budget (OMB).\n
In general, the N-DASH uses the following coding for race:\n
\n A classification based on Hispanic culture and origin, without considering race.\n
\nRace and Hispanic origin are combined into one variable in this platform, using the following categories for Custom Graphics:
\nRace and Hispanic origin are combined into one variable in this platform, using the following categories for Quick Graphics:
\n\n Unlawful penetration of a person against the will of the victim, with use or threatened use of force, or attempting such an act. Includes psychological coercion and physical force. Forced sexual intercourse means vaginal, anal, or oral penetration by the offender. Also includes incidents where penetration is from a foreign object, such as a bottle. Includes male and female victims, and heterosexual and same-sex rape. Attempted rape includes verbal threats of rape. Rape and sexual assault are combined into one victimization measure.\n
\n\n Region of respondent residence.
\n The states have been divided into the following four groups or census regions:\n
\n Specifies whether the crime was reported to police, whether by the respondent or someone else.\n
\n\n Reasons for reporting to police\nThe NCVS asks respondents about 12 potential reasons for reporting a victimization to police. For ease of presentation, those data are collapsed into the six categories presented in this tool.
\nThe NCVS asks respondents about 19 potential reasons for not reporting a victimization to police. For ease of presentation, those data are collapsed into the six categories presented in this tool.
\n\n The\n unlawful taking or attempted taking of property that is in the immediate\n possession of another, by force or threat of force, with or without a weapon,\n and with or without injury.\n
\n\n Series victimizations, or high-frequency repeat victimizations, are six or more similar but separate victimizations that occur with such frequency that the victim is unable to recall each individual event or describe each event in detail. BJS counts series victimizations using the victim’s estimate of the number of times victimizations occurred during the prior six months, up to a maximum of 10 victimizations. Including series victimizations in national estimates can substantially increase the number and rate of violent victimization; however, trends in violence are generally similar regardless of whether series victimizations are included. See
\n The\n respondent’s sex.\n
\n\n Sexual assault encompasses a wide range of victimizations, separate from rape or attempted rape. Includes attacks or attempted attacks generally involving unwanted sexual contact between victim and offender, with or without force. Includes grabbing or fondling and verbal threats. Rape and sexual assault are combined into one victimization measure.\n
\n\n An attack or attempted attack without a weapon that results in no injury, minor injury (e.g., bruises, black eyes, cuts, scratches, and swelling), or an undetermined injury requiring fewer than two days of hospitalization.\n
\n\n A\n measure of the sampling error associated with an estimate. The standard error\n of a sample estimate is a measure of the variation among a sample estimate and\n all of the possible sample estimates that could be obtained.\n
\n\n Also called “other theft”. The\n unlawful taking or attempted unlawful taking of property or cash without personal\n contact with the victim. Incidents involving theft of property from within a\n household are classified as theft if the offender has a legal right to be in\n the house (e.g., a maid, delivery person, or guest). The incident is classified\n as a burglary or trespassing if the offender has no legal right to be in the\n house.\n
\nIncludes unlawful or forcible entry or attempted entry of places, including a permanent residence, other residence (e.g., a hotel room or vacation residence), or other structure (e.g., a garage or shed). Includes only crimes where the offender did not commit or attempt a theft. Does not include trespassing on land.
\n\n The N-DASH can display three different types of estimates: number of victimizations, rate of victimization, or percentage of victimizations for the characteristics selected. See “
\n The\n recipient of a criminal act, usually used in relation to personal crimes but\n also applicable to households.\n
\n\n A single\n victim or household that experienced a criminal incident. Criminal incidents or\n crimes are distinguished from victimizations in that one criminal incident may\n have multiple victims or victimizations. For violent crimes (rape/sexual\n assault, robbery, aggravated assault, and simple assault) and for personal theft/larceny, the count of victimizations is the number of times individuals experienced a violent crime. For crimes against households\n (burglary, trespassing, other theft, and motor vehicle theft), each household affected\n by a crime is counted as a single victimization.\n
\n\n A\n measure of the occurrence of victimizations among a specified population group.\n For personal crimes, this is based on the number of victimizations per 1,000\n residents age 12 or older. For property crimes, the victimization rates are\n calculated using the number of incidents per 1,000 households.\n
\n\n\n Personal victimization\n rate
\n (Number of\n victimizations × 1,000) /\n Number of persons\n
\n Household victimization\n rate
\n (Number of\n victimizations × 1,000) /\n Number of households\n
\n A measure of whether victims received any help or advice from victim service agencies. This is asked of victims of both personal and property victimization. Victim service agencies are publicly or privately funded organizations that provide victims with support and services to aid their physical and emotional recovery, offer protection from future victimizations, guide them through the criminal justice system process, and assist them in obtaining restitution.\n
\n\n\n The N-DASH\n uses this coding for victim services use:\n
\n\n A classification of a crime victim’s relationship to the offender for crimes involving direct contact between the two. For the analysis tool, this applies to crimes that involve contact between the victim and the offender (personal victimization). This distinction is not made for property victimization.\n
\n\n\n The N-DASH\n uses this coding for the victim-offender relationship:\n
\n\n Traditionally called serious violent victimization. Rape/sexual assault, personal robbery, or aggravated assault. Includes attempted and completed crimes. Murder is not measured by the NCVS because of an inability to question the victim.\n
\n\n Rape/sexual assault, personal robbery, aggravated assault, or simple assault. Includes attempted and completed crimes. Excludes personal theft/larceny. Murder is not measured by the NCVS because of an inability to question the victim. Violent victimizations measure crimes against persons. Each time a person is affected by a violent crime, it is counted as a single victimization.\n
\n\n A\n measure of whether a weapon was present during the victimization. The\n respondent is asked whether the offender had a weapon. This applies only to personal victimizations where there was contact between the victim and the offender. Weapons include guns, knives, and other objects that are used as weapons (e.g., rocks\n and clubs). By definition, neither simple\n assault nor personal theft involves a weapon.\n
\n\n\n Users can obtain\n information about the type of weapon. The N-DASH uses this\n coding for weapon type:\n
\n\n This dashboard enables you to examine information from the National Crime Victimization Survey (NCVS), the nation’s primary source of data on nonfatal crimes reported and not reported to police. The NCVS collects information on nonfatal victimizations against persons age 12 or older from a nationally representative sample of U.S. households. These sample estimates are then weighted to represent the population.
The dashboard features data on nonfatal criminal victimization from 1993 to the most recent year that NCVS data are available, including:
\n\n\n You can analyze victimization counts, rates, and percentages by select victim and household characteristics, weapon category (presence and type of weapon), victim services use, medical treatment, victim-offender relationship, injury, location of incident, and crimes reported and not reported to police.\n
\n\n The site includes
Victimization is the basic unit of analysis used throughout this data visualization tool. A victimization refers to a single incident that a victim or household experienced. Crime incidents are distinguished from victimizations in that one crime may have multiple victims.
\nFor violent crimes (rape/sexual assault, robbery, aggravated assault, and simple assault) and for personal theft/larceny, the count of victimizations is the number of times individuals experienced a crime. For crimes against households (burglary, trespassing, motor vehicle theft, and other theft), each time a household is affected by a crime is counted as a single victimization.
\nFor violent victimizations and personal larceny, the victim, household, and incident characteristics included in this analysis tool are:
\n\n\n For property victimizations, the household\n characteristics included in the data visualization tool are:\n
\n\n\n In this tool, data on most important reasons for reporting and not reporting violent and property crime to police are only found in the
The N-DASH offers three types of weighted NCVS estimates: numbers (or counts), rates, and percentages. Within a given year, estimates can be produced as a total for all victimizations or for specific subgroup populations. Each type of estimate is calculated differently and should be interpreted accordingly. An explanation of each type is below:
\nNumber of victimizations
\n\n This is an estimation of the number of (personal or property) victimizations that U.S. residents age 12 or older or households experienced in the selected year. When interpreting estimates of the number of victimizations for subgroups, you must consider that these estimates do not account for different demographic population sizes, which may be related to the incidence of victimization. For more information on estimates that standardize across populations, see “Rates of victimization” below.\n
\nRates of victimization
\n\n Rates of victimization are calculated by multiplying the number of victimizations by 1,000 and dividing that product by the number of persons age 12 or older (in the case of property victimizations, the product is divided by the number of households). Rates provide a standard way to measure crime levels across population groups of different sizes. Populations can differ between subgroups and can vary over time for the same subgroup. Standardized rates are particularly helpful for making comparisons across demographic characteristics, like victim sex or race/Hispanic origin.\n
\n\n For example, the 2018 rate of violent victimization was 17.5 victimizations per 1,000 persons age 12 or older in the Northeast region of the United States and 19.3 per 1,000 in the South. These rates are similar in magnitude. However, looking at the number of victimizations for these two regions, the South has more than twice the number of victimizations compared with the Northeast (2,005,830 versus 843,660).\n
\nPercentages of victimization
\n\n Percentages show the portion of victimizations that occurred for a particular characteristic (i.e., the number of victimizations for a characteristic divided by the total number of victimizations and multiplied by 100). Percentages help illustrate the distribution of victimizations for a specific characteristic, particularly when comparing across incident characteristics, such as victim-offender relationship or reporting to police.\n
\n\n In cases where the number is equal to 0 victimizations, the rate rounds to less than 0.05 victimizations per 1,000, or the percentage rounds to less than 0.5 percent, the estimate is noted with a '✱' symbol.\"\n
\nNote that the N-DASH does not reflect tests of statistical significance. Differences that appear statistically significant may not be. Measures of the error around the estimate, including standard errors and confidence intervals, can be turned on or off for each graphic.
\n\n\n In
\n In
\n The
\n The
\n The
a. Compare across crime types
\n\n See “Download Options” and “Interacting with N-DASH Graphics” for information on the tooltip, toggle options, Show Table option, and Download Table options.\n
\nb. Compare across demographics or incident characteristics
\n\n See “Download Options” and “Interacting with N-DASH Graphics” for information on the tooltip, toggle, Show Table, and Download Table options.\n
\nThis tab allows for comparisons of counts, rates, or percentages between different years for a particular crime type or subpopulation.
\n\n See “Download Options” and “Interacting with N-DASH Graphics” for information on the tooltip, toggle options, Show Table option, and Download Table options.\n
\n\n For every graphic in the dashboard, you have the ability to download an image of the chart or download the data presented in the graphic in spreadsheet format (.csv file). In
a. Tooltip
\n\n b. Toggle options\n
\n\n Estimates derived from a sample survey such as the NCVS are subject to sampling error and nonsampling error. The accuracy of an estimate is a measure of its total error (i.e., the sum of all the errors affecting the estimate). For more information, see
\n To produce estimates for the NCVS, each year BJS selects a representative sample from the entire population. With sample surveys, the estimates derived reflect information from the sample, which is then weighted to represent the population of interest. Because of this, sample estimates are subject to sampling error. In addition, when selecting the sample, it is possible to obtain different samples that are also nationally representative even when using the same design and selection procedures. The estimates derived from any one of these samples would differ from one another due to sampling variability or sampling error. Sampling error is often quantified with the standard error as described below.\n
\n\n In addition to sampling error, the estimates in this analysis tool are subject to nonsampling error. Sources of nonsampling error include nonresponse error, respondent recall error, poorly worded questions, interviewer and mode effects on response, and data entry error. Although substantial care is taken in the NCVS to reduce the sources of nonsampling error throughout all of the survey operations by means of a quality assurance program, quality controls, operational controls, and error-correcting procedures, an unquantified amount of nonsampling error still remains. These nonsampling errors can be difficult to measure.\n
\n\nStandard error
\nAny time national estimates are derived from a sample rather than the entire population, as is the case with the NCVS, it is important to be cautious when drawing conclusions about the size of one population estimate in comparison with another or about whether the population estimates are changing over time.
\nAlthough one estimate may be larger than another, estimates based on responses from a sample of the population each have some degree of sampling error. The sampling error of an estimate depends on several factors, including the amount of variation in the responses, the size and representativeness of the sample, and the size of the subgroup for which the estimate is computed.
\n\n One measure of the sampling error associated with an estimate is the standard error, which measures the variation between a sample estimate and the average of all possible estimates that could be obtained by taking different samples from the same population.\n
\n\n In general, an estimate with a smaller standard error provides a more reliable approximation of the true value than an estimate with a higher standard error. Estimates with relatively large standard errors are associated with less precision and reliability and should be interpreted with caution.\n
\n\n For complex sample designs, several methods can be used to generate standard errors around point estimates (e.g., numbers, percentages, and rates). In this tool, generalized variance function (GVF) parameters were used for variance estimation. The U.S. Census Bureau produces GVF parameters for BJS, which account for aspects of the NCVS’s complex sample design. For more information on GVFs, see
\n The coefficient of variation (CV) is a measure of an estimate’s reliability. The CV is the ratio of the standard error to the estimate. In this data visualization tool, BJS calculated the CV for all estimates. In cases where the CV is greater than 50% or when the estimate is based on 10 or fewer sample cases, the estimate is noted with a \"▲\" symbol (i.e., interpret with caution, based on 10 or fewer sample cases, or the coefficient of variation is greater than 50%).\n
\nConfidence intervals
\n\n\n Confidence intervals are another measure of the margin of error. For rates and counts, a confidence interval around the estimate can be generated by multiplying the standard errors by ±1.96 (the t-score of a normal, two-tailed distribution that excludes 2.5% at either end of the distribution). Therefore, the 95% confidence interval around an estimate is the estimate ± (the standard error X 1.96). In other words, if different samples using the same procedures were taken from the U.S. population, 95% of the time the estimate would fall within that confidence interval.\n\n For percentage estimates, the logit transformation is used to improve coverage and to ensure the confidence limits are on the interval [0,1]. The logit transformation confidence interval is calculated as\n
BJS did not test whether differences in numbers, rates, or percentages in this data visualization tool are statistically significant. Please use caution and do not draw inferences when comparing estimates.
\n\n In 1997 the Office of Management and Budget introduced new guidelines for the collection and reporting of race/Hispanic origin data in government surveys. BJS implemented these methodological changes for all demographic surveys on January 1, 2003. As a result, BJS now allows individuals to choose more than one racial category; in prior years, individuals were asked to select a single primary race. BJS also now asks individuals whether they are of Hispanic origin before asking about their race, and now asks people directly if they are Spanish, Hispanic, or Latino. See
\n Because detailed NCVS data by race are not available for all years and because sample sizes for some race groups are small and subject to data reliability concerns,
\n NCVS data for 2006 are not comparable with NCVS data for other years. See
\n In October 2018, BJS released a revised set of 2016 NCVS data. See
The impact of COVID-19 on 2020 NCVS data
\n\n Due to increasing risks related to COVID-19, BJS, in coordination with the U.S. Census Bureau, modified typical NCVS interviewing operations for portions of the year 2020. To address the impact of these modified field operations, the 2020 data were examined to determine what adjustments were needed to ensure comparability with past and future years of NCVS data. Following this examination, it was determined that the 2020 NCVS data are comparable with other data years. For more information, see
Outlier weights in the NCVS
\n\n The NCVS uses analysis weights to account for differential probabilities of selecting sample members and varying response rates. This helps ensure that estimates produced from the sample are representative of the target population. While efforts are made during the NCVS weighting process to limit the size of weighting adjustment factors, it is still possible for some households or individuals to be assigned a large weight relative to other members of the sample. When these households or persons with large weights also report a large number of victimizations, they can have an undue, or “extreme,” influence on victimization estimates and negatively affect the precision of estimates, particularly for rare crime types or small subpopulations.\n Beginning with the 2020 datafile, BJS incorporated an additional factor to moderate the impact of extreme cases on NCVS estimates. After exploring several methods for identifying and adjusting outlier weights, BJS applied a framework that identifies extreme cases, applies an adjustment, and reallocates the excess weight to nonoutlier cases. For more information, see
The dashboard is best viewed with Google Chrome or Mozilla Firefox. Though the dashboard has been optimized for viewing on computers and mobile devices, one’s device size may affect the display of graphics.
\n\n Please direct any comments or questions about the about the NCVS dashboard to:\n
\n\n askbjs@usdoj.gov
\n
Please include “N-DASH” in the subject line of the e-mail.
\nInquiries can also be directed to:
\n\n Bureau of Justice Statistics
\n U.S. Department of Justice
\n 810 7th Street, NW
\n Washington, DC 20531
\n
\n (202) 307-0765\n
When publishing data from BJS’s N-DASH in any form, including graphics, the recommended citation is:
\n\n Bureau of Justice Statistics. (Insert table/chart title). Generated using the NCVS Dashboard (N-DASH) at www.bjs.ojp.gov. (Insert date generated).\n
\nPlease use the following information when citing the NCVS dashboard:
\n\n Bureau of Justice Statistics
\n Tool title: National Crime Victimization Survey Data Dashboard (N-DASH)
\n Data source: BJS, National Crime Victimization Survey
\n Date accessed (or generated): [Insert date here]\n
\n Site last updated: {{ dateLastUpdated }}\n
\n\n\n","import mod from \"-!../../node_modules/cache-loader/dist/cjs.js??ref--13-0!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js!../../node_modules/cache-loader/dist/cjs.js??ref--1-0!../../node_modules/vue-loader/lib/index.js??vue-loader-options!./dateLastUpdated.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../../node_modules/cache-loader/dist/cjs.js??ref--13-0!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js!../../node_modules/cache-loader/dist/cjs.js??ref--1-0!../../node_modules/vue-loader/lib/index.js??vue-loader-options!./dateLastUpdated.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./dateLastUpdated.vue?vue&type=template&id=39a8e06e\"\nimport script from \"./dateLastUpdated.vue?vue&type=script&lang=js\"\nexport * from \"./dateLastUpdated.vue?vue&type=script&lang=js\"\n\n\n/* normalize component */\nimport normalizer from \"!../../node_modules/vue-loader/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","\n \n\n\n","import mod from \"-!../../node_modules/cache-loader/dist/cjs.js??ref--13-0!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js!../../node_modules/cache-loader/dist/cjs.js??ref--1-0!../../node_modules/vue-loader/lib/index.js??vue-loader-options!./statNote.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../../node_modules/cache-loader/dist/cjs.js??ref--13-0!../../node_modules/thread-loader/dist/cjs.js!../../node_modules/babel-loader/lib/index.js!../../node_modules/cache-loader/dist/cjs.js??ref--1-0!../../node_modules/vue-loader/lib/index.js??vue-loader-options!./statNote.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./statNote.vue?vue&type=template&id=a8f6f52c\"\nimport script from \"./statNote.vue?vue&type=script&lang=js\"\nexport * from \"./statNote.vue?vue&type=script&lang=js\"\n\n\n/* normalize component */\nimport normalizer from \"!../../node_modules/vue-loader/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n script,\n render,\n staticRenderFns,\n false,\n null,\n null,\n null\n \n)\n\nexport default component.exports","export let unitFlagMixin = {\n computed: {\n checkForUnitFlags () {\n let chartIds = Object.keys(this.charts)\n for (let i = 0; i < chartIds.length; i++) {\n if (this.getChartData(chartIds[i]) && this.getChartData(chartIds[i]).hasFlags) {\n return ['unitFlag']\n }\n }\n return []\n }\n }\n}\n","export default {\n // HTML styling must exist inline so that it passes to svg export.\n confidenceInterval: '“95% C.I.”: 95% confidence interval.',\n dataFilterCrimePgWyear: 'Filter data by characteristic and characteristic value, by year(s). Use “Clear Filters” to reset.This Quick Graphic displays more detailed race/Hispanic origin data than the Custom Graphics in this tool, in which the ‘Asian/Native Hawaiian or Other Pacific Islander’ and ‘American Indian or Alaska Native and persons of two or more races’ categories seen here are merged into an “Other” category to enhance data reliability. Race categories exclude persons of Hispanic/Latino origin. See Terms & Definitions for more information. For more information on race/Hispanic origin and changes to categories over time, see “Collection and Reporting of Race/Hispanic origin Data” section in the User’s Guide.
`}})],1):_vm._e(),(_vm.selectedTab === 'propertyVictimizationHolder')?_c('div',{staticClass:\"col col-12 section show\",attrs:{\"id\":\"propertyVictimizationHolder\",\"role\":\"tabpanel\",\"aria-labelledby\":\"propertyVictimizationTab\"}},[_c('sectionHeader',{attrs:{\"title\":'Property victimization',\"header-id\":'propertyVictimizationHeader',\"page-name\":'quickGraphics'}}),_c('chartHolder',{attrs:{\"chart-ids\":['HouseholdPropertyVictimization1994to2015'],\"force-table\":_vm.isMobileView}}),_c('smallMultiplesHolder',{attrs:{\"title\":'Rate of burglary, trespassing, motor vehicle theft, and other theft victimizations',\"chart-ids\":['HouseholdBurglaryVictimization1994to2015', 'HouseholdTrespassingVictimization1994to2015', 'HouseholdMotorVehicleTheftVictimization1994to2015', 'HouseholdTheftVictimization1994to2015'],\"columns\":4,\"user-instruction\":'An individual larger chart view of any of these smaller comparison charts can be generated in Custom Graphics via the multi-year-trends/characteristic view, by selecting ‘Property’ under ‘Display Settings: CRIME CATEGORY,’ and under ‘Data Filter Settings:’ selecting the crime type of interest for ‘CRIME TYPE.’'}})],1):_vm._e(),(_vm.selectedTab === 'reportingPoliceHolder')?_c('div',{staticClass:\"col col-12 section show\",attrs:{\"id\":\"reportingPoliceHolder\",\"role\":\"tabpanel\",\"aria-labelledby\":\"reportingPoliceTab\"}},[_c('sectionHeader',{attrs:{\"title\":'Reporting to police',\"header-id\":'reportingPoliceHeader',\"page-name\":'quickGraphics'}}),_c('smallMultiplesHolder',{attrs:{\"title\":'Percent of violent victimizations by reporting to the police',\"chart-ids\":['PersonViolentReportPoliceYes1994to2015', 'PersonViolentReportPoliceNo1994to2015', 'PersonViolentReportPoliceDontKnow1994to2015'],\"columns\":3}}),_c('chartHolder',{attrs:{\"chart-ids\":['ViolentVictimizationByReasonforReportingToPolice2018'],\"force-table\":_vm.isMobileView}}),_c('chartHolder',{attrs:{\"chart-ids\":['ViolentCrimeExcludingSimpleAssaultByReasonforReportingToPolice2018'],\"force-table\":_vm.isMobileView}}),_c('chartHolder',{attrs:{\"chart-ids\":['SimpleAssaultByReasonforReportingToPolice2018'],\"force-table\":_vm.isMobileView}}),_c('chartHolder',{attrs:{\"chart-ids\":['ViolentVictimizationByReasonforNotReportingToPolice2018'],\"force-table\":_vm.isMobileView}}),_c('chartHolder',{attrs:{\"chart-ids\":['ViolentCrimeExcludingSimpleAssaultByReasonforNotReportingToPolice2018'],\"force-table\":_vm.isMobileView}}),_c('chartHolder',{attrs:{\"chart-ids\":['SimpleAssaultByReasonforNotReportingToPolice2018'],\"force-table\":_vm.isMobileView}}),_c('smallMultiplesHolder',{attrs:{\"title\":'Percent of property victimizations by reporting to the police',\"chart-ids\":['HouseholdPropertyReportPoliceYes1994to2015', 'HouseholdPropertyReportPoliceNo1994to2015', 'HouseholdPropertyReportPoliceDontKnow1994to2015'],\"columns\":3}}),_c('chartHolder',{attrs:{\"chart-ids\":['PropertyByReasonforReportingToPolice2018'],\"force-table\":_vm.isMobileView}}),_c('chartHolder',{attrs:{\"chart-ids\":['PropertyByReasonforNotReportingToPolice2018'],\"force-table\":_vm.isMobileView}})],1):_vm._e(),(_vm.selectedTab === 'victimOffenderHolder')?_c('div',{staticClass:\"col col-12 section show\",attrs:{\"id\":\"victimOffenderHolder\",\"role\":\"tabpanel\",\"aria-labelledby\":\"victimOffenderTab\"}},[_c('sectionHeader',{attrs:{\"title\":'Victim-offender relationship',\"header-id\":'victimOffenderHeader',\"page-name\":'quickGraphics'}}),_c('chartHolder',{attrs:{\"chart-ids\":['ViolentVictimizationByVictimOffenderRelationship2018'],\"force-table\":_vm.isMobileView}})],1):_vm._e(),(_vm.selectedTab === 'weaponUseHolder')?_c('div',{staticClass:\"col col-12 section show\",attrs:{\"id\":\"weaponUseHolder\",\"role\":\"tabpanel\",\"aria-labelledby\":\"weaponUseTab\"}},[_c('sectionHeader',{attrs:{\"title\":'Weapon Use',\"header-id\":'weaponUseHeader',\"page-name\":'quickGraphics'}}),_c('smallMultiplesHolder',{attrs:{\"title\":'Percent of violent victimizations by weapon category',\"chart-ids\":['ViolentWeaponUnknownIfOffenderHadWeapon1994To2015', 'ViolentWeaponFirearm1994To2015', 'ViolentWeaponKnife1994To2015',\n 'ViolentWeaponNoWeapon1994To2015', 'ViolentWeaponOtherWeaponType1994To2015', 'ViolentWeaponUnknownWeaponType1994To2015'],\"columns\":3}})],1):_vm._e(),(_vm.selectedTab === 'victimServiceUseHolder')?_c('div',{staticClass:\"col col-12 section show\",attrs:{\"id\":\"victimServiceUseHolder\",\"role\":\"tabpanel\",\"aria-labelledby\":\"victimServiceUseTab\"}},[_c('sectionHeader',{attrs:{\"title\":'Victim services use',\"header-id\":'victimServiceUseHeader',\"page-name\":'quickGraphics'}}),_c('smallMultiplesHolder',{attrs:{\"title\":'Percent of rape/sexual assault, robbery, aggravated assault, and simple assault victimizations with victim services use',\"chart-ids\":['PersonVictimServicesRapeAndSexualAssault1994To2015', 'PersonVictimServicesRobbery1994To2015', 'PersonVictimServicesAggravatedAssault1994To2015', 'PersonVictimServicesSimpleAssault1994To2015']}})],1):_vm._e()])]):_vm._e()])]),_c('div',{staticClass:\"row bt\"},[_c('div',{staticClass:\"container\"},[_c('div',{staticClass:\"row mt-5 mb-3\"},[_vm._m(3),_c('div',{staticClass:\"col col-12 col-md-4\"},[_c('div',{staticClass:\"row mb-4\",attrs:{\"id\":\"cg2\"}},[_c('div',{staticClass:\"col nav-item\"},[_c('div',{staticClass:\"col p-3 d-flex align-items-stretch\"},[_c('div',{staticClass:\"col col-12 pgbutton\"},[_c('router-link',{staticClass:\"nav-link navbar-link\",attrs:{\"to\":{name: 'customGraphics', hash: '#customgraphicstop'}}},[_c('h3',{staticClass:\"mt-5\"},[_vm._v(\" Custom Graphics \")])])],1)])])])]),_c('StatNote')],1)])]),_c('loadModal')],1)\n}\nvar staticRenderFns = [function (){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"col col-12 mb-lg-3\"},[_c('h1',{staticClass:\"sg\"},[_vm._v(\" Quick Graphics \")])])\n},function (){var _vm=this,_c=_vm._self._c;return _c('small',{staticClass:\"col col-12 nb pb-md-2\"},[_c('i',{},[_vm._v(\"(click to show section)\")])])\n},function (){var _vm=this,_c=_vm._self._c;return _c('small',{staticClass:\"d-block pb-3 statsignote pt-2\"},[_vm._v(\"BJS did not test whether estimate differences in the N-DASH are statistically significant; therefore, please do not draw inferences when comparing. \"),_c('a',{attrs:{\"href\":\"/terms#ci_description\",\"target\":\"/terms\"}},[_vm._v(\"Confidence intervals\")]),_vm._v(\" and \"),_c('a',{attrs:{\"href\":\"/terms#se_description\",\"target\":\"/terms\"}},[_vm._v(\"standard errors\")]),_vm._v(\" are two measures of the accuracy of an estimate. For more information, see \"),_c('a',{attrs:{\"href\":\"/methodology#methodology\",\"target\":\"/methodology\"}},[_vm._v(\"User’s Guide\")]),_vm._v(\". The dashboard is best viewed with Google Chrome or Mozilla Firefox. Though the dashboard has been optimized for viewing on computers and mobile devices, one’s device size may affect the display of graphics. \")])\n},function (){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"col col-12 col-md-8\"},[_c('p',{staticClass:\"text-right\"},[_vm._v(\" To investigate in more detail, see: \")])])\n}]\n\nexport { render, staticRenderFns }","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"row sectionheader mb-4\",attrs:{\"id\":_vm.headerId}},[_c('h4',{staticClass:\"col col-12 col-md-9\",attrs:{\"id\":_vm.headerId}},[_vm._v(\" \"+_vm._s(_vm.title)+\" \")])])\n}\nvar staticRenderFns = []\n\nexport { render, staticRenderFns }","\n\n To investigate in more detail, see:\n
\n\n Custom graphics allow you to analyze data on
\n On each custom graphics page, there are customizable Display Settings and Data Filter Settings. Use Display Settings to create data charts by comparison characteristic and type of crime. Use Data Filter Settings to narrow data by characteristic, crime type, and year(s). You can also download the charts and tables you create.\n
\n\n Select one of the six pages below to get started.\n
\n\n Compare estimates over time—across crime types or across groups within a demographic or incident characteristic.\n
\n \n\n Compare across:\n
\nEx. Examine violent victimization rates for Hispanic youth over time
\nEx. Examine the number of robberies by whether or not the victimization was reported to police for females over time
\n\n Compare estimates for a single data year—of multiple crime types and a demographic or incident characteristic or of two different characteristics.\n
\n \n\n Compare across:\n
\nEx. Compare percentages in 1993 of rape or sexual assault and simple assault victimizations by whether medical treatment was received for an injury
\nEx. Compare percentages in 2015 of household burglaries for which victim services were used by household income
\n\n Compare estimates between two data years—for crime types with a demographic or incident characteristic or for two different characteristics.\n
\n \n\n Compare across:\n
\nEx. Compare violent victimization rates by marital status from 2000 to 2018 where the offender was a stranger
\nEx. Compare the number of aggravated assaults from 1996 to 2005 by population size and whether or not a weapon was used
\n\n This dynamic analysis tool allows you to examine National Crime Victimization Survey (NCVS) data on both personal and property victimization, by select victim, household, and incident characteristics.\n
\nThe NCVS is the nation’s primary source of information on criminal victimization. It is an annual data collection sponsored by the Bureau of Justice Statistics. The NCVS collects information from a nationally representative sample of U.S. households on nonfatal crimes, reported and not reported to the police, against persons age 12 or older.
\n\n This tool presents estimates from the NCVS as interactive data visualizations, providing users with a variety of views and analysis types to investigate questions of interest. On the Quick Graphics and Custom Graphics pages, you can view or create and download charts and tables.\n
\n\n Select one of the three options below to get started.\n
\n\n Have questions?
askbjs@usdoj.gov \n
An introduction to this tool's use, terms, and functions
\nA selection of prefabricated interactive charts covering key topics of interest
Create custom interactive charts based on select topics of interest
\n (N-DASH)\n
\n