Stata has two built-in variables called _n and _N. _n is Stata notation for the current observation number. _n is 1 in the first observation, 2 in the second, 3 in the third, and so on.
stata 13 serial number 62
In this example we sort the observations by all of the variables. Then we use all of the variable in the by statement and set set n equal to the total number of observations that are identical. Finally, we list the observations for which _N is greater than 1, thereby identifying the duplicate observations.
The date data saved in tempdate are stored consistently, but the data are still stored as a string. We can use the date() function to convert tempdate to a number. The date(s1,s2) function returns a number based on two arguments, s1 and s2. The argument s1 is the string we wish to act upon and the argument s2 is the order of the day, month, and year in s1. Our tempdate variable is stored with the month first, the day second, and the year third. So we can type s2 as MDY, which indicates that Month is followed by Day, which is followed by Year. We can use the date() function below to convert the string date 03-23-2020 to a number.
NCVS data files include person, household, victimization, and incident weights. Person weights provide an estimate of the population represented by each person in the sample. Household weights provide an estimate of the U.S. household population represented by each household in the sample. After proper adjustment, both household and person weights are also typically used to form the denominator in calculations of crime rates. For personal crimes, the incident weight is derived by dividing the person weight of a victim by the total number of persons victimized during an incident as reported by the respondent. For property crimes, the incident weight and the household weight are the same because the victim of a property crime is considered to be the household as a whole. The incident weight is most frequently used to calculate estimates of the number of crimes committed against a particular class of victim.
Victimization weights used in these analyses account for the number of persons victimized during an incident and for high-frequency repeat victimizations (i.e., series victimizations). Series victimizations are similar in type but occur with such frequency that a victim is unable to recall each individual event or describe each event in detail. Survey procedures allow NCVS interviewers to identify and classify these similar victimizations as series victimizations and to collect detailed information on only the most recent incident in the series.
The weighting counts series victimizations as the actual number of victimizations reported by the victim, up to a maximum of 10. Doing so produces more reliable estimates of crime levels than only counting such victimizations once, while the cap at 10 minimizes the effect of extreme outliers on rates. According to the 2021 data, series victimizations accounted for 1.1% of all victimizations and 2.9% of all violent victimizations. Additional information on the enumeration of series victimizations is detailed in the report Methods for Counting High-Frequency Repeat Victimizations in the National Crime Victimization Survey (NCJ 237308, April 2012).
BJS conducts statistical tests to determine whether differences in estimated numbers, percentages, and rates in these reports were statistically significant once sampling error was taken into account. The primary test procedure BJS uses is the Student's t-statistic, which tests the difference between two sample estimates. Unless otherwise noted, the findings described in these reports as higher, lower, or different passed a test at the 0.05 level of statistical significance (95% confidence level) or at the 0.10 level of significance (90% confidence level). Readers should reference figures and tables in BJS reports for testing on specific findings. Caution is required when comparing estimates not explicitly discussed in BJS reports.
Senior law enforcement officials told ABC News that they also uncovered a number of social media posts and videos tied to James and are studying them closely to see if they are relevant to the subway attack.
The bloodshed came amid a surge in crime within New York City's transit system. The mayor said he has already doubled the number of police officers patrolling the city's subway stations and is also considering installing special metal detectors in the wake of Tuesday's shooting.
The previous article showed how to perform heteroscedasticity tests of time series data in STATA. It also showed how to apply a correction for heteroscedasticity so as not to violate the Ordinary Least Squares (OLS) assumption of constant variance of errors. This article shows a testing serial correlation of errors or time series autocorrelation in STATA. An autocorrelation problem arises when error terms in a regression model correlate over time or are dependent on each other.
However, STATA does not provide the corresponding p-value. To obtain the Durbin-Watson test statistics from the table conclude whether the serial correlation exists or not. Download the Durbin Watson D table here.
The Breusch-Godfrey LM test has an advantage over the classical Durbin-Watson D test. The Durbin-Watson test relies upon the assumption that the distribution of residuals is normal whereas the Breusch-Godfrey LM test is less sensitive to this assumption. Another advantage of this test is that it allows researchers to test for serial correlation through a number of lags besides one lag which is a correlation between the residuals between time t and t-k (where k is the number of lags). This is unlike the Durbin-Watson test which allows testing for only correlation between t and t-1. Therefore if k is 1, then the results of the Breusch-Godfrey test and Durbin-Watson test will be the same.
Since from the above table, chi2 is less than 0.05 or 5%, the null hypothesis can be rejected. In other words, there is a serial correlation between the residuals in the model. Therefore correct for the violation of the assumption of no serial correlation.
Police agencies in New York State collect data on the number of individuals victimized during domestic incidents involving members of the same family, including but not limited to parents, children and siblings, and intimate partners. These individuals may or may not live together at the time of the incident.
DCJS and OCA coordinated and compiled one comprehensive data file to meet the statutory reporting obligations because neither agency maintains all the data necessary to fulfill the requirements under the bail reform law. Data within the file include the sex, race, and ethnicity of the individual arrested; the most serious arrest charge; the number and type of charges the individual has faced previously; and if the individual failed to appear in court or was re-arrested while the case was pending, among other data. The information provided does not identify the individuals charged.
State law allows individuals who have remained crime-free for 10 years to request that certain New York State convictions be sealed. These data show the number of individuals who successfully petitioned the courts to seal a case(s), by the county in which the seal was granted.
This section presents the number of juvenile delinquency cases handled by probation departments and Family Courts for the most recent five-year period. Data are shown for the following case processing points: detention admissions, probation intake and adjustment, initial petitions filed in Family Court, probation supervision cases opened, and cases under probation supervision at the end of each year. These data are presented by county and region:
The number of sworn and civilian employees on each police agency's payroll, and those employees by sex, race and ethnicity, annually as of Oct. 31. These data are reported by each agency and excludes employees working in local correctional facilities.
Effect measures for dichotomous data are described in Chapter 6, Section 6.4.1. The effect of an intervention can be expressed as either a relative or an absolute effect. The risk ratio (relative risk) and odds ratio are relative measures, while the risk difference and number needed to treat for an additional beneficial outcome are absolute measures. A further complication is that there are, in fact, two risk ratios. We can calculate the risk ratio of an event occurring or the risk ratio of no event occurring. These give different summary results in a meta-analysis, sometimes dramatically so.
The selection of a summary statistic for use in meta-analysis depends on balancing three criteria (Deeks 2002). First, we desire a summary statistic that gives values that are similar for all the studies in the meta-analysis and subdivisions of the population to which the interventions will be applied. The more consistent the summary statistic, the greater is the justification for expressing the intervention effect as a single summary number. Second, the summary statistic must have the mathematical properties required to perform a valid meta-analysis. Third, the summary statistic would ideally be easily understood and applied by those using the review. The summary intervention effect should be presented in a way that helps readers to interpret and apply the results appropriately. Among effect measures for dichotomous data, no single measure is uniformly best, so the choice inevitably involves a compromise. 2ff7e9595c
Comments