Methods for Understanding and Analyzing NIBRS Data

The National Incident-Based Reporting System (NIBRS) is an incident-basedcrime reporting program for local, state, and federal law enforcementagencies. Within each criminal incident, NIBRS captures information onoffenses, victims, offenders, property, and persons arrested, as well asinformation about the incident itself. The ability to link and analyze thisdetailed information is a significant improvement to the existing UniformCrime Reporting (UCR) summary reporting system. As one might expect,however, this increase in crime data significantly complicates the life ofthe data analyst, particularly when cross tabulating the NIBRS data. To dealwith the complexity of NIBRS data, one must understand its structure. Thisarticle provides an overview of the NIBRS structure and methods formaneuvering within it to present and interpret correctly cross tabulationsof the NIBRS data.     

A Comparison of Histomorphometric Data Collection Methods*

Abstract: Although many variables that skeletal biologists examine have been standardized, the actual techniques used to collect these data from bone thin sections vary. This project compares different methods of obtaining data (relative cortical area values) for histomorphometric research. One visual and three digital methods of histomorphometric data collection are compared: (i) Merz microscopic eyepiece counting reticule, (ii) flatbed scanner, (iii) overlaying multiple images of a thin section, and (iv) digital SLR camera with macro settings. The discussion includes a comparison of usability factors such as cost, time, user‐experience, and ease‐of‐use, which vary for each method. Values from the different methods are compared using ANOVA tests to evaluate inter‐method, inter‐observer, and intra‐observer variability. Intra‐observer error was greater for the microscopic method, although the error values are concomitant with experience. We found no statistically significant differences between the four methods examined, but certain caveats must be addressed when these methods are used.     

Assessing Stability and Change in Criminal Offending: A Comparison of Random Effects, Semiparametric, and Fixed Effects Modeling Strategies

An important theoretical problem for criminologists is an explanation forthe robust positive correlation between prior and future criminaloffending. Nagin and Paternoster (1991) have suggested that the correlationcould be due to time-stable population differences in the underlyingproneness to commit crimes (population heterogeneity) and/or thecriminogenic effect that crime has on social bonds, conventionalattachments, and the like (state dependence). Because of data andmeasurement limitations, the disentangling of population heterogeneityand state dependence requires that researchers control for unmeasuredpersistent heterogeneity. Frequently, random effects probit models havebeen employed, which, while user-friendly, make a strong parametricassumption that the unobserved heterogeneity in the population follows anormal distribution. Although semiparametric alternatives to the randomeffects probit model have recently appeared in the literature to avoid thisproblem, in this paper we return to reconsider the fully parametric model. Viasimulation evidence, we first show that the random effects probit modelproduces biased estimates as the departure of heterogeneity from normalitybecomes more substantial. Using the 1958 Philadelphia cohort data, we thencompare the results from a random effects probit model with a semiparametricprobit model and a fixed effects logit model that makes no assumptions aboutthe distribution of unobserved heterogeneity. We found that with this dataset all three models converged on the same substantive result—evenafter controlling for unobserved persistent heterogeneity, with models thattreat the unobserved heterogeneity very differently, prior conduct had apronounced effect on subsequent offending. These results are inconsistentwith a model that attributes all of the positive correlation between priorand future offending to differences in criminal propensity. Sinceresearchers will often be completely blind with respect to the tenabilityof the normality assumption, we conclude that different estimationstrategies should be brought to bear on the data.     

Reconsidering the Relationship Between Welfare Spending and Serious Crime: A Panel Data Analysis with Implications for Social Support Theory

Several researchers have found an inverse relationship between welfare spending and serious crime. With few exceptions, these findings have been based on cross‐sectional designs, single measures of welfare spending, and few indicators of crime. In response to these limitations, the relationship between welfare spending and crime was reconsidered using panel data from California counties. Fixed‐effects regressions revealed virtually no relationship between several measures of welfare spending and five types of serious crime: (1) homicide; (2) robbery; (3) assault; (4) burglary; and (5) larceny. The estimates were resistant to numerous robustness checks and alternative specifications. The most plausible explanation for the findings is that heterogeneity was not controlled for in previous studies. Implications for social support theory are discussed.     

Using Booking Data to Model Drug User Arrest Rates: A Preliminary to Estimating the Prevalence of Chronic Drug Use

Public policy is often concerned with the size and characteristics of special populations that are difficult to reach in household surveys. Chronic drug users, who often live outside conventional households, provide the illustration motivating this paper. An alternative to household surveys is to question chronic drug users where they congregate—jails, treatment programs, and shelters, for example. Using such opportunistic data for prevalence estimation raises difficult problems for statistical inference: Study subjects who arrive at the collection points cannot be deemed a random sample of the general population. However, if we could estimate the rates at which chronic drug users arrive at the collection points, then we could use those estimates to weight the sample to represent the population. This paper presents a modified Poisson mixture model used to estimate the stochastic process that accounts for how chronic drug users get arrested. It uses that model to estimate arrest rates for 38 counties using up to sixteen quarters of data from the Arrestee Drug Abuse Monitoring survey.

A Test of Two Methods of Recall for Violent Events

This project took advantage of an opportunity to test the comparability of two different methods for collecting self-reports of violent incidents. Using a life events calendar (LEC) approach, we collected data from individuals about violent incidents that occurred within a 1–3-year prior time period. These individuals had been research participants in a previous study that collected information about violent incidents using prospective, weekly interviews. Results using the LEC method were compared with the weekly self-reports of violence for an overlapping recall period. This allowed us to see how well the recall of violent incidents at a later date mapped onto reports obtained within seven days of any incidents. Overall results show a significant amount of under-reporting using the life-event calendar methodology compared to the weekly interview approach, but some higher concordance of reporting was found for serious rather than minor violence.     

A Two‐Level Model for Evidence Evaluation in the Presence of Zeros*

Abstract: Likelihood ratios (LRs) provide a natural way of computing the value of evidence under competing propositions. We propose LR models for classification and comparison that extend the ideas of Aitken, Zadora, and Lucy and Aitken and Lucy to include consideration of zeros. Instead of substituting zeros by a small value, we view the presence of zeros as informative and model it using Bernoulli distributions. The proposed models are used for evaluation of forensic glass (comparison and classification problem) and paint data (comparison problem). Two hundred and sixty‐four glass samples were analyzed by scanning electron microscopy, coupled with an energy dispersive X‐ray spectrometer method and 36 acrylic topcoat paint samples by pyrolysis gas chromatography hyphened with mass spectrometer method. The proposed LR model gave very satisfactory results for the glass comparison problem and for most of the classification tasks for glass. Results of comparison of paints were also highly satisfactory, with only 3.0% false positive answers and 2.8% false negative answers.