Michael D. Maltz

A Note on the Use of County-Level UCR Data

County-level crime data have major gaps, and the imputation schemes for filling in the gaps are inadequate and inconsistent. Such data were used in a recent study of guns and crime without considering the errors resulting from imputation. This note describes the errors and how they may have affected this study. Until improved methods of imputing county-level crime data are developed, tested, and implemented, they should not be used, especially in policy studies.

The Mathematics of Behavioral Change: Recidivism and Construct Validity

This paper describes a new method of calculating the failure rate of programs involving behavioral change. The method overcomes a threat to construct validity implicit in the previous calculation method. Using it, one can estimate the failure rate as well as the fraction of program participants who are ultimately expected to remain successful. We illustrate the use of the method with data from a cohort of parolees. However, it should be of interest to those engaged in evaluating other programs that attempt to change individual behavior.

Deviating from the Mean: The Declining Significance of Significance

Most of the methods we use in criminology to infer relationships are based on mean values of distributions. This essay explores the historical origins of this issue and some counterproductive consequences: relying too heavily on sampling as a means of ensuring “statistical significance”; ignoring the implicit assumptions of regression modeling; and assuming that all data sets reflect a single mode of behavior for the entire population under study. The essay concludes by suggesting that we no longer “make do” with the standard methodologies used to study criminology and criminal justice, and recommends developing categories that more accurately reflect behavior and groupings than the ones we currently use; looking at alternative sources of data, including qualitative data such as narrative accounts; and developing alternative methods to extract and analyze the data from such sources.

Measurement and Other Errors in County-Level UCR Data: A Reply to Lott and Whitley

Lott and Whitley note that our analyses of the errors in the county-level UCR data used in More Guns, Less Crime (J. R. Lott, University of Chicago Press, Chicago, 1998, 2000) ignore the fact that all data have measurement error, that the largest errors were in counties with low populations, and that population-weighted regressions were used. We agree that this mitigates some of the effects of the errors, but does not take them fully into account. We also note that this is but one of the problems associated with the analysis. We therefore find no reason to alter our original conclusion, that in their current condition, county-level UCR crime statistics cannot be used for evaluating the effects of changes in policy.

Visualizing Homicide: A Research Note

This paper provides some examples of the utility of graphical methods in analyzing data. While such methods are not expected to supplant standard statistical techniques, they can help the researcher in understanding characteristics of the process in ways that cannot be replicated using the standard methods.

From Poisson to the present: Applying operations research to problems of crime and justice

In the 1830s Siméon-Denis Poisson developed the distribution that bears his name, basing it on the binomial distribution. He used it to show how the inherent variance in jury decisions affected the inferences that could be made about the probability of conviction in French courts. In recent years there have been a number of examples where researchers have either ignored or forgotten this inherent variance, and how operations research, in particular mathematical modeling, can be used to incorporate this variance in analyses. These are described in this paper, as well as other contributions made by operations research to the study of crime and criminal justice.

Visualizing Lives: New Pathways for Analyzing Life Course Trajectories

The goal of statistical analysis is to find patterns in data. Most statistical methods rely on analyzing the effect of the same set of variables on the population under study, i.e., nomothetic analysis. Therefore, when data are collected in the social sciences, most often they are put in a framework that resembles a spreadsheet: each row represents a separate individual, and each column represents a separate characteristic (or variable) that pertains to that individual.However, not all individuals in the study are affected by the same set of variables: each individual may have his/her own individual set of relevant variables, suggesting that methods be developed that consider them individually, i.e., idiographic analysis. Moreover, lives are lived chronologically, and are often best described in narrative form. These narratives usually have to be condensed, or abridged in other ways, in order to fit the data framework and permit what one might call ``algorithmic analysis”. Each set of methods has its advantage: nomothetic methods generate general laws that apply to all, while idiographic methods trace the putative causal relationships that are unique to each individual.This paper describes another data collection and analytic framework, one that (a) is chronological; (b) recognizes that different people may have experienced entirely different events and thus may need different ``variables” to understand their behavior; (c) recognizes that, even if people experience similar events, they may have entirely different reactions to them; and (d) can be studied (and patterns inferred) using an exploratory graphical analysis that is more free-form than algorithmic analysis. Examples of this type of analysis used in different medical and criminal justice contexts are given, and suggested directions of research in this area are described.

Artificial Inflation of a Delinquency Rate by a Selection Artifact

Cohorts of youths sentenced to a variety of correctional programs show substantial reductions in delinquent activity after leaving the programs compared to before sentencing. This paper develops models of delinquent activity and subsequent sentencing to a correctional program. We show how a population of youths, whose delinquent activity is represented by a stationary stochastic process, can be selected (using reasonable selection rules) to form a cohort which has an inflated rate of delinquent activity prior to selection. When the activity rate returns to its uninflated rate after the youths are released from the program, an apparent reduction results. Based on this analysis we conclude that the reductions noted in delinquent activity may be largely due to the way delinquents are selected for correction rather than to the effect of the programs.

Measures of Effectiveness for Crime Reduction Programs

This paper describes some measures commonly used to evaluate anticrime programs and proposes directions for research on improved measures. Since the police are usually seen as the main crime control agency, the paper first discusses the differences between evaluating the police and evaluating crime control programs. Five measures used to evaluate such programs are then analyzed: crime rate, clearance rate, arrest rate, police response time, and crime seriousness index. The last measure suggests the direction for the development of an improved measure of crime: the development of a more complete taxonomy of crime and the discrimination between and explication of the different types of harm caused by crime.

Crime statistics: A mathematical perspective

This paper compares the crime statistics generated by the FBIs Uniform Crime Reports (UCR) to those generated by the National Crime Survey (NCS), collected by the Census Bureau for the Law Enforcement Assistance Administration. The crime reporting process is described for both the UCR and NCS to determine the nature of their respective biases. Events within the processes are analyzed to develop a new estimate of crime incidence based on both the UCR and NCS. Numerical examples are then developed to point out the deficiencies in the NCS, and to make suggestions for the improvement of its accuracy.