Predicting Violent Behavior and Classifying Violent Offenders

Jan Chaiken Marcia Chaiken and William Rhodes

INTRODUCTION

This paper discusses the classification of individuals as violent persons and the prediction of individual acts of violence. It is based on a review of research reports that implicitly or explicitly define violence as physically harmful behavior carried out by an individual and directed against others. Thus we exclude research on such topics as collective violence (e.g., riots and wars), self-injury (e.g., suicide), and psychological violence (e.g., verbal aggression).

We further focus on research whose explanatory factors were individual characteristics, thereby excluding studies of subcultural factors, ecological factors (such as density of the population), and situational factors (such as availability of firearms). Although such factors are pertinent for predicting the occurrence of violence, this paper focuses on predicting parameters of individual criminal careers, in particular:

  • the prevalence of violent persons in a study population (e.g., the percent of juveniles in the United States who have ever committed an act of violence) and the likelihood that any given person will be violent;

Jan Chaiken and William Rhodes were with Abt Associates in Cambridge, Massachusetts. Marcia Chaiken was at LINC, Lincoln, Massachusetts.



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Understanding and Preventing Violence: Volume 4 - Consequences and Control Predicting Violent Behavior and Classifying Violent Offenders Jan Chaiken Marcia Chaiken and William Rhodes INTRODUCTION This paper discusses the classification of individuals as violent persons and the prediction of individual acts of violence. It is based on a review of research reports that implicitly or explicitly define violence as physically harmful behavior carried out by an individual and directed against others. Thus we exclude research on such topics as collective violence (e.g., riots and wars), self-injury (e.g., suicide), and psychological violence (e.g., verbal aggression). We further focus on research whose explanatory factors were individual characteristics, thereby excluding studies of subcultural factors, ecological factors (such as density of the population), and situational factors (such as availability of firearms). Although such factors are pertinent for predicting the occurrence of violence, this paper focuses on predicting parameters of individual criminal careers, in particular: the prevalence of violent persons in a study population (e.g., the percent of juveniles in the United States who have ever committed an act of violence) and the likelihood that any given person will be violent; Jan Chaiken and William Rhodes were with Abt Associates in Cambridge, Massachusetts. Marcia Chaiken was at LINC, Lincoln, Massachusetts.

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Understanding and Preventing Violence: Volume 4 - Consequences and Control the rate at which violent persons commit violent acts (the number of violent acts committed by a person each year); the persistence (or duration) of violent persons' histories of committing violent acts (e.g., length in years from the commission of the first act of violence to the last act of violence); and the seriousness (or harmfulness) of the violent acts committed by individuals (e.g., extent of physical harm inflicted). Our review seeks to transcend the particular foci of the source studies (which typically concentrate on particular types of victims or forms of injury, such as spouse abuse or rape) and instead summarizes commonalities in the methods and findings of studies about violence. Considerable empirical evidence suggests that violent people frequently engage in a range of violent and other types of antisocial acts. Children who throw heavy or sharp objects at their parents are likely to hit their siblings or peers and to lie, set fires, and be truant from school (Lewis and Balla, 1976); prison inmates who are ''violent predators" (they committed robbery and assault and dealt drugs prior to incarceration) are just as likely to have an arrest history including rape as are fellow inmates who are actually serving time for rape (Chaiken and Chaiken, 1982, and unpublished analysis of the same data). Thus, a focus on a narrow range of violent acts would fail to identify what is commonly perceived as violence. Furthermore, the characteristics of victims and the forms of injury are less important for classification and prediction than are characteristics and past behavior of individuals. Although any behavioral outcome is dependent on an individual's response to the environment, including his or her access to specific classes of victims, certain biological, psychological, and social characteristics of individuals dramatically increase or decrease the probability that they will engage in specific forms of behavior, independent of environmental factors. This concept has been supported by such studies as Glaser (1964), Hare (1979), Irwin (1970), Mann et al. (1976), McCord and McCord (1959), and Robins and Wish (1977), and has guided our thinking in this review. To carry out the review, it was useful to distinguish between classification and prediction, even in situations where this distinction is unclear in the source document. The following section explains the basis for this distinction and summarizes those methodological issues that classification and prediction have in common; it also contains a brief summary of the correlates of violence. In the following section, we turn attention to classification. There we review the purposes of classification, types of

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Understanding and Preventing Violence: Volume 4 - Consequences and Control classifications, and congruent findings across studies. We then focus on prediction studies and summarize our findings about predicting violence. The last two sections present the implications for research and public policy. TECHNICAL ASPECTS OF PREDICTION AND CLASSIFICATION DISTINCTIONS BETWEEN PREDICTION AND CLASSIFICATION No definitive boundaries separate classification from prediction, and they cannot be clearly distinguished by their methodology or purposes. In fact, some researchers who know they are involved in classification nonetheless state that their major purpose is prediction. For this review, however, we found that a useful and practical separation can be made by defining a prediction study as one whose underlying analysis fundamentally requires longitudinal data—information about the same subjects' behavior at two or more points in time. For a work to be considered as prediction research here, the form or coefficients of the equations, models, or procedures for separating persons into subgroups must have been initially determined by a comparison of data at two different times. For this reason, an analysis that divides violent persons into groups by using cross-sectional data is considered to be classification, not prediction, even if the work was based on a theoretical expectation that the groups would behave differently in the future. Similarly, studies that examine subsequent data to determine outcomes of groups previously classified by use of cross-sectional data are considered classification studies (e.g., the work of Milner et al., 1984, discussed under "classification" below), as are studies in which the data collection instrument was tested for stability over time by using test-retest methods. We also considered studies called "postdiction" by their authors (e.g., see Chaiken and Chaiken, 1990b) as classification research. In postdiction studies, data are collected describing various stages in a person's life or criminal career, but the dependent variables being estimated in the statistical analysis (e.g., individual crime commission rates during the previous year) describe behavior that is contemporaneous with some or all of the data items used as independent variables. In short, the behavior being estimated does not lie in the future with respect to the "predictors." A typical purpose of postdiction research is to devise ways

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Understanding and Preventing Violence: Volume 4 - Consequences and Control of using officially or routinely recorded data for estimating concurrent behavior that cannot be routinely measured. On balance we felt it is helpful to limit the term "prediction" to future-oriented research. It is immaterial whether the data for the independent variables (predictors) are collected retrospectively at one time or longitudinally. Classification research tends to focus on dividing individuals into distinctive subgroups. In traditional classification research (e.g., Gibbons, 1975), all persons being classified are to fall in a group defined by the research, and no one is to be classified as belonging to several groups. In others, a residual group of unclassified individuals is permitted,1 or persons are classified along several dimensions rather than in a single group (for example, as in the Diagnostic and Statistical Manual of Mental Disorders, Third Edition—Revised (DSM-III-R), American Psychiatric Association, 1987, discussed below). By contrast, prediction research infrequently focuses on mutually exclusive subgroups, instead concentrating on estimating probabilities of future occurrences. In this framework, some or all persons could have probabilities greater than zero for several (or all) of the potential outcomes. The purposes for undertaking classification are varied and may or may not be forward looking, whereas prediction is always forward looking by our definition. In the context of violence research, classification may be undertaken to estimate the prevalence of violent persons, or categories of violent persons, in specific populations; to construct typologies that assist in understanding personal and social characteristics of categories of violent and nonviolent persons; to learn more about causes, correlates, and stability associated with categories of violent persons; to diagnose individuals for purposes of planning treatment; and to assign individuals to groups for purposes of case management.2 The purposes of prediction may be similar, but prediction involves future behavior. Predictions of future violence may be made to determine whether subjects pose a risk to the community when released from criminal justice or mental health restraints; to investigate the causal relationships between events at two or more points in time; or to project future demands on criminal justice and health care resources.

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Understanding and Preventing Violence: Volume 4 - Consequences and Control MEASURES OF QUALITY The standards applicable to judging the quality of classification and prediction research are basic textbook material in the psychometric literature, but they appear to have been applied unevenly, sporadically, or incompletely in criminal justice contexts. This section discusses three important measures of quality: validity, reliability, and accuracy.3 Validity Validity generally means that a variable, test, or system of equations actually measures or predicts the theoretical construct it purports to. Three dimensions of validity are considered important in the literature: content validity, construct validity, and criterion validity (Corcoran and Fischer, 1987). Content Validity Content validity determines whether the independently measured items are a representative sample of the content area to be covered by the instrument or data collection activity. There would be little need to introduce this limited concept of "validity" except as a reminder that truly shoddy classification and prediction research is occasionally carried out. Content validity is ascertained subjectively, either by examining the items and judging if they appear to represent the content ("face validity") or by examining the procedures used in the original research to select the independent variables ("logical content validity"). Examples of procedures that may be used to quantify the validity of self-reported data are measuring consistency among responses to logically identical or reversed questions, examining the extent or pattern of missing (blank) responses, developing "lie" scales (sets of questions whose responses can be combined into an index of untruthfulness), and specifically asking respondents whether or not they are telling the truth. Construct Validity Construct validity entails determining that the system's measured variables, or combinations of them, correlate with meaningfully related observable variables or actual behaviors, whereas dissimilar observables are not associated with scores, subgroupings, or data items in the same way. In research on violence, construct validity is often highly problematic. The variables that purport to represent the occurrence or extent of

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Understanding and Preventing Violence: Volume 4 - Consequences and Control violence itself, or causative factors related to violence, are in many cases ill-defined or not representative of the intended behavior. The reactions of other people and the institutional procedures that intervene between an occurrence of violence and a recorded instance of violence are often complex and extended in time, so that records related to violence are often difficult for researchers to interpret in the context of construct validity. For example, a past history of arrests for violent crimes is not synonymous with being a violent person, nor are recorded instances of "aggression," however defined. When histories of arrests for violent crimes are studied without addressing the question of what they represent, or by indirection leaving the implication that the data indicate whether or not the person is violent, construct validity is violated. Similarly, short follow-up periods for collecting data about persons predicted to be violent or not violent may not permit obtaining valid measures of the construct of interest. Inadequate construct validity also occurs in independent variables used in violence research. For example, a recorded history of psychiatric treatment does not signal or characterize any clearly articulable past pattern of behavior, so its relationship to contemporaneous or future violence, if any, is difficult at best to interpret. Even many variables that superficially appear to have good construct validity because they involve "hard scientific tests" may on closer inspection be inadequate. For example a positive urine test for opiates is not a valid measure of addiction. A more valid but "softer" measure of addiction is a police officer's notation that an arrestee possessed "works," had recent track marks and ulcers, and showed signs of withdrawal irritability. Criterion Validity Criterion validity deals with the existence of a relationship between test scores, subgroups, or independent variables and actual behavior, as represented by other measurements or observations (Golden et al., 1984). Criterion validity can be measured concurrently or predictively, whether or not the underlying study itself was predictive. Concurrent criterion validity involves showing that there is a relationship with an alternative method of measuring the same characteristic of interest at the same time, whereas predictive validity attempts to show a relationship with the behavior of interest at a future time. Both classification and prediction studies may be examined for their predictive validity.

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Understanding and Preventing Violence: Volume 4 - Consequences and Control Reliability Reliability refers to the method's trustworthiness, as indicated by stability over time or among different groups, or by consistency in application by different researchers or in different contexts. Theoretically, reliability quantifies the degree to which a constructed test overlaps a perfect measure of the characteristic of interest (Golden et al., 1984). Measures available for estimating the reliability of classification or prediction research include internal consistency, interrater reliability, and test-retest reliability. Internal Consistency Measuring internal consistency differs from a consistency check for validity of responses to logically equivalent or reversed items, mentioned above. Coefficient alpha (Cronbach, 1951), a common measure of internal consistency, is based on the average correlations among items purporting to be related to the same theoretical construct. An alpha coefficient exceeding .80 is generally deemed to show that a measurement device is internally consistent. The Kuder-Richardson 20 formula (KR20) (Kuder and Richardson, 1937) is a similar statistical measure designed for dichotomous items.4 Interrater Reliability Interrater reliability is ascertained by having multiple researchers or examiners score or code the identical set of raw data or the same observed behavior. The correlations among corresponding items are then calculated, with a typical standard of acceptance being average correlations exceeding .80 and preferably above .90. Test-Retest Reliability Test-retest reliability is determined by asking respondents the same questions twice in a single administration of a questionnaire or on two different occasions separated by days or weeks, and calculating correlations between corresponding items.5 Accuracy Accuracy refers to the discriminating power of the method: the magnitude of the distinctions among subgroups or the proportion of a subgroup actually displaying the outcome predicted for them. A prediction equation is valid if there is some statistically significant correlation between the predictors and the actual outcomes

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Understanding and Preventing Violence: Volume 4 - Consequences and Control of interest, but it is accurate if there is a strong or utilizable relationship, for example if a sizable proportion of persons predicted to commit violent acts turn out in fact to commit such acts, whereas a sizable proportion of the others turn out not to commit violence. A considerable body of literature on criminal justice prediction research bemoans the typically low levels of accuracy achieved by prediction models and the lack of standardized or commonly accepted statistics for comparing the relative quality of different prediction instruments or methodologies, especially when applied to different populations. See Gottfredson and Gottfredson (1988b:252) for a good review of this literature as related to violent criminal behavior, together with an extensive bibliography; they state that "reviewing the literature concerning the prediction of dangerousness and the propensity for violence shows that there is little evidence supporting our ability to make these predictions with acceptable accuracy. The prediction of violence is exceptionally difficult, and no one seems to have done it well." A common observation in recent literature is that predictions of violence are especially difficult due to a low base rate, namely, that in any naturally occurring population only a small proportion of individuals will commit acts of violence. However, a low base rate does not place limits on the ultimately achievable levels of prediction accuracy, which evidently can be as high as 100 percent. To illustrate the independence between base rate and prediction accuracy, consider the case of Tay-Sachs disease, whose incidence is well under 1 percent in the general population but whose occurrence can be predicted with 100 percent accuracy based on the results of appropriate tests. The persons who will develop Tay-Sachs disease (absent an intervening alternative cause of death) can all be predicted by physical tests for the absence of a specific vital enzyme, and anyone who has the enzyme will not develop the disease.6 Of course, before the role of this enzyme was discovered, prediction of future development of Tay-Sachs disease appeared to be formidably difficult. However, the disease's low base rate was neither an obstacle to developing an accurate prediction instrument nor a bar to practical application of the prediction methodology. A low base rate does, however, present a limitation on the usefulness of inaccurate predictions. If the base rate is low, the default "ignorant" hypothesis that no one in the study group will commit any acts of violence can be very close to the truth, but of course useless. Similarly, predictions of very low or very high

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Understanding and Preventing Violence: Volume 4 - Consequences and Control probabilities can be accurate but useless. For example, an exactly correct prediction that 97 percent of a specific subgroup of individuals will be nonviolent is not very helpful if the base rate for nonviolence in the entire study group is 96 percent. In the case of perfect predictions, the selection rate (proportion of the population predicted to have the characteristic in question) is the same as the base rate, but in the realm of violence classification and prediction, typical instruments have selection rates that differ from the base rate. Although often the original researcher can arrange for the selection rate to equal the base rate in the study sample, there is no guarantee that the two will be the same in subsequent applications. Comparing and making sense out of the accuracy levels of two instruments or methodologies that have different selection rates have proved problematic. Loeber and Dishion (1983) developed the Relative Improvement Over Chance (RIOC) statistic to help permit such comparisons; RIOC calibrates a prediction's improvement in accuracy over random accuracy with respect to the constrained range between maximum accuracy and random accuracy. Farrington and Loeber (1989) present simplified formulas for calculating RIOC and its variance. The formula for IOC used in this chapter is defined in a footnote to Table 1. CLASSIFICATION AND PREDICTION METHODS Formal and Informal Methods Almost all people routinely classify and predict the behavior of others on a day-to-day basis. Classification of violent behavior, as with any form of behavior, is an ongoing social process (Mead, 1934) learned early in life (Kagan, 1982). It depends on one's experience in anticipating future behavior, or behavior in related situations, based on observable acts (Weinstein, 1969) such as nonverbal gestures (Lindesmith and Strauss, 1968) and the individuals' appearance (Goffman, 1959, 1963). Some people classify persons as violent or predict their future violence based on informal processes, whereas others use highly structured statistical procedures. Informal processes of classification occur commonly. For example, people walking in a city street at night are likely to plan their routes based on their understanding of types of violent offenders and estimates of their prevalence in certain neighborhoods; they will react to strangers in the area by crossing the street or walking faster, based on their own informal classification of the

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Understanding and Preventing Violence: Volume 4 - Consequences and Control people they observe and their anticipation of possible violence from such people. The sections that follow describe formal procedures for classifying violent offenders as developed in specific research studies. However, it should be recognized that in practice the results of even the most rigorous classification and prediction methods are interpreted in the context of the possible political, economic, and social consequences of classification decisions.7 The informal, often hidden, methods used for classification depend as much on the organizational setting as on the characteristics of the individuals undergoing classification. Violent persons are classified by various agencies in the criminal justice system and the public mental health system, and by private psychiatric/psychological service providers. The populations served by these classifiers often overlap, but their classification methods are typically quite different, as are the factors they consider important in regard to violent behavior. A 17-year-old male who has been convicted of rape may, for example, be sentenced to prison, to a psychiatric facility, or to probation with a stipulation of private psychological treatment or counseling in a mental health agency. Depending on where he is sentenced, he will encounter different formal and informal classification methods, and the practitioners who classify him will differ regarding the consequences of making wrong predictions and the risks they are willing accept in making classification or prediction errors. Selection Bias A difficulty commonly experienced in quantitatively based classification or prediction studies of violence has been the lack of a clear understanding of the selection process that produced the group under study. In many instances, the research study sample is chosen for reasons of convenience and availability to the researchers. Examples of populations of this type include incarcerated offenders, persons in treatment, or juvenile court referrals. The sequences of events through which individuals came to be members of the study population may be so remote in time and place from the research project that there is little realistic possibility of obtaining descriptions of the processes or statistical probabilities associated with selection for the study population. Whenever there are no clear and replicable rules for selection of the study population, subsequent difficulties arise in interpreting and implementing the results of the research. Often it is not

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Understanding and Preventing Violence: Volume 4 - Consequences and Control possible to specify the appropriate groups to which the prediction equations, test score cutoffs, or other classification schemes will apply. For example, the category "convicted male felons" in one state is not necessarily closely similar to the same category in another state. Often, a research strategy is proposed in which tests, score cutoffs, or other methods are to be developed with one population and then subsequently tested on similar populations to demonstrate their robustness. However, the results to date of classification and prediction research on violence do not give much comfort to those who hope for the presence of robust findings. Instead, the sequential research strategy frequently demonstrates that the classification techniques applied to another group yield entirely different (unstable) categories, or that the classification or prediction equations are highly inaccurate when applied to different study populations. Determining the Importance of Potential Predictive Factors A related difficulty is that the original study population, unknown to the researchers, may differ from other populations in regard to its variance on variables strongly correlated with violence. Such variables will not emerge in the analysis as being significantly associated with violence, even if they are measured correctly in the study population. In addition, unmeasured variables pertinent to the selection process may also be predictors of violence. Such variables tend to be forgotten in interpreting the results of the analysis, especially if the researchers never had a genuine opportunity to observe or measure the variables in question. Two-stage prediction studies, in which the probability of selection is estimated first and then the outcome is estimated, can help reduce these types of ambiguities in research results. However, there are not many studies in the existing violence prediction and classification literature that control for potential selection biases in this way. The problem of classification and prediction instruments not transporting well from one setting to another is compounded by researchers' well-intentioned efforts to maximize prediction accuracy within a construct sample. Stepwise computing algorithms and test-retest procedures used on the same set of data will typically produce predictions that maximize sample-specific correlations; such predictions are prone to shrinkage (reduction in prediction accuracy) when used in a second sample that does not

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Understanding and Preventing Violence: Volume 4 - Consequences and Control 3.   Megargee (1977) includes these three qualities in a list of desirable features of a classification system. Also included in the list are comprehensiveness, cost-effectiveness, and potential to reflect changes over time. 4.   Dichotomous items are those having only two possible alternative responses. 5.   When the separation between test and retest is as long as months or years, the comparison of test with retest is more pertinent to the temporal stability of the behavior being measured than to the reliability of the measurement instruments. 6.   Tay-Sachs disease is a fatal genetic disorder that is not apparent by outward physical signs at birth. If both parents are carriers of the recessive gene, there is a 25 percent chance that a pregnancy will result in a Tay-Sachs baby. The blood level of the enzyme hexosaminidase A is depressed in Tay-Sachs carriers and zero in an affected person. By testing both parents' blood, it is possible to determine if both are carriers; if so, amniocentesis or other means of sampling fetal tissue permits determining with certainty whether the fetus will develop Tay-Sachs disease. 7.   For an example of prosecuting attorneys' classifying defendants in the context of the local district attorney's office policy, see Chaiken and Chaiken (1987, 1990b). 8.   There were an additional 15 reports of neglect and 16 reports of failure to thrive, but we do not consider these. 9.   We would like to thank Joel Garner, then at the National Institute of Justice, for preparing this literature search of titles related to violence. 10.   Alternatively, the prediction might be made conditional on a specific form of social constraint, such as intensive parole supervision or weekly reporting to a mental health clinic. 11.   In predicting violence, one is not uninterested in social responses and whether they control or fail to control violent behavior, but it is important to distinguish the behavior itself from the effectiveness of social control. One cannot judge the effectiveness of variations in social control without independent measures of the rate at which violence would have occurred in the absence of social controls. Furthermore, development of the theory of violent behavior is retarded if empirical studies of violence are muddied with the effects of social controls of criminal behavior. 12.   Goldstein (1989) advances three explanations for the relationship between drugs and violence. Drugs may have a pharmacological effect that precipitates violence; violence may be instrumental as part of property crimes committed to get funds for drug

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Understanding and Preventing Violence: Volume 4 - Consequences and Control     purchases; or violence may be a systemic part of the drug distribution business. 13.   From the context, we infer that the low glucose nadir indicates an abnormality in the physiological processes that keep blood sugar concentration in a normal steady state, for example, a defective system of glycogen conversion to glucose in the liver due to cirrhosis or other disease. In this case, cerebral function would be impaired in turn, because brain cells could not adequately store glucose or utilize other forms of nutrients for energy. 14.   The signs of the coefficients for rape were consistent with the hypothesis that the probability of an arrest for violence decreased with age. The failure to find the difference to be statistically significant might be explained partly by the small sample size—277 for rape compared to 1,104 for robbery and 2,083 for assault. 15.   The derivative of the logistic regression, when evaluated at the mean, indicates that subjects who suffered physical abuse as children were .08 more likely than others to commit violent acts, and that subjects who suffered neglect as children were .05 more likely than others to commit violent acts. 16.   Other drug use was not recorded for this 1979 cohort. 17.   The distribution of street time was reported as 20 or more months spent in the community (N = 110), 12 or more months in prison (N = 35), 12 or more months in a psychiatric hospital (N = 13), and a mixture of institutional conditions (N = 45). REFERENCES Ageton, G. 1983 Sexual Assault Among Adolescents. Lexington, Mass.: Heath. Altschuler, D., and P. Brounstein 1989 Patterns of Drug Use, Drug Trafficking, and Other Delinquency Among Inner City Adolescents in Washington, D.C. Paper presented at the annual meeting of the American Society of Criminology, Reno, Nevada, November. American Psychiatric Association 1987 Diagnostic and Statistical Manual of Mental Disorders, Third Edition-Revised. Washington, D.C.: American Psychiatric Association. Ayoub, C., M.M. Jacewitz, R.G. Gold, and J.S. Milner 1983 Assessment of a program's effectiveness in selecting individuals ''at risk" for problems in parenting. Journal of Clinical Psychology 39:334-339.

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Understanding and Preventing Violence: Volume 4 - Consequences and Control Baum, M.S. 1981 Effectiveness of the Megarege typology in predicting violent behavior. Doctoral dissertation, University of California, Santa Barbara. (Dissertation Abstracts International 42/04H:1804. University Microfilms No. DEN81-20741.) Belenko, S., K. Chin, and J. Fagan 1989 Typologies of Criminal Careers Among Crack Arrestees. Paper presented at the annual meeting of the American Society of Criminology, Reno, Nevada, November. Black, T., and P. Spinks 1985 Predicting outcomes of mentally disordered and dangerous offenders. In D. Farrington and R. Tarling, eds., Prediction in Criminology. Albany: State University of New York Press. Butcher, J.N., and L.S. Keller 1984 Objective personality assessment. Pp. 307-331 in G. Goldstein and M. Hersen, eds., Handbook of Psychological Assessment. New York: Pergamon Press. Chaiken, J., and M. Chaiken 1982 Varieties of Criminal Behavior. Santa Monica, Calif.: The RAND Corporation. 1990a Drug use and predatory crime. Pp. 203-209 in J.Q. Wilson and M. Tonry, eds., Drugs and Crime. Vol. 13 of Crime and Justice: A Review of Research. Chicago: University of Chicago Press. Chaiken, M. 1983 Crime Rates and Substance Abuse Among Types of Offenders. New York: The Interdisciplinary Research Center, Narcotic and Drug Research, Inc. 1990 Community or Individual Factors: What Matters More for Serious Criminal Behavior and Frequency of Arrest? Final report for grant 88-IJ-CX-0022 submitted to the National Institute of Justice. Chaiken, M., and J. Chaiken 1987 Selecting "Career Criminals" for Priority Prosecution. NCJ 106310. Washington, D.C.: National Institute of Justice. 1990b Redefining the Career Criminal: Priority Prosecution of High-Rate Dangerous Offenders. NCJ 124136. Washington, D.C.: National Institute of Justice. Chaiken, M.R., and B.D. Johnson 1988 Characteristics of Different Types of Drug-Involved Offenders . Washington, D.C.: National Institute of Justice. Clements, C.B. 1986 Offender Needs Assessment. College Park, Md.: American Correctional Association. Cocozza, J., and H. Steadman 1974 Some refinements in the measurement and prediction of dangerous behavior. American Journal of Psychiatry 131(9):1012-1014.

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Understanding and Preventing Violence: Volume 4 - Consequences and Control Cohen, J., and S.E. Zimmerman 1990 Improved Techniques for Assessing the Accuracy of Recidivism Prediction Scales. Pittsburgh, Pa.: School of Urban and Public Affairs, Carnegie-Mellon University. Collins, J. 1989 Alcohol and interpersonal violence: Less than meets the eye. In N. Weiner and M. Wolfgang, eds., Pathways to Criminal Violence. Newbury Park, Calif.: Sage Publications. Cook, P., and D. Nagin 1979 Does the Weapon Matter? Washington, D.C.: INSLAW. Copas, J.B. 1983 Regression prediction and shrinkage. Journal of the Royal Statistical Society 129:311-354. Copas, J., and R. Tarling 1986 Some methodological issues in making predictions. In J. Roth and C. Visher, eds., Criminal Careers and "Career" Criminals. Washington, D.C.: National Academy Press. Corcoran, K., and J. Fischer 1987 Measures for Clinical Practice: A Sourcebook. New York: Free Press. Cronbach, L.J. 1951 Coefficient alpha and the internal structure of tests. Psychometrika 16:297-334. Dembo, R., L. Williams, A. Getreu, L. Genung, J. Schmeidler, E. Berry, E. Wish, and L. LaVoie 1991 A longitudinal study of the relationship among marijuana/hashish use, cocaine use and delinquency in a cohort of high risk youths. Journal of Drug Issues 21(2):271-312. Elliott, D.S., D. Huiziuga, and S. Menard 1989 Multiple Problem Youth: Delinquency, Drugs, and Mental Health Problems. New York: Springer-Verlag. Fagan, J., and J.G. Weis 1990 Drug Use and Delinquency Among Inner City Youth. New York: Springer-Verlag. Fagan, J., E. Piper, and M. Moore 1986 Violent delinquents and urban youths. Criminology 24(3):439-470. Farrington, D. 1989 Early predictors of adolescent aggression and adult violence. Violence and Victims 4(2):79-100. Farrington, D., and R. Loeber 1989 Relative improvement over chance (RIOC) and phi as measures of predictive efficiency and strength of association in 2×2 tables. Journal of Quantitative Criminology 5(3):201-214. Flinn, C. 1986 Dynamic models of criminal careers. Pp. 356-379 in J. Roth and

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Understanding and Preventing Violence: Volume 4 - Consequences and Control Heckman, J., and B. Singer 1985 Longitudinal Analysis of Labor Market Data. Cambridge: Cambridge University Press. Holden, R. 1985 Failure time models for thinned crime commission data. Sociological Methods and Research 14(1):3-30. Holland, T., N. Holt, and G. Beckett 1982 Prediction of violent versus nonviolent recidivism from prior violent and nonviolent criminality. Journal of Abnormal Psychology 91(3):178-182. Holmes, R.M. 1989 Profiling Violent Crimes: An Investigative Tool. Newbury Park, Calif.: Sage. Howell, M., and K. Pugliesi 1988 Husbands who harm: Predicting spousal violence by men. Journal of Family Violence 3(1):15-27. Irwin, J. 1970 The Felon. Englewood Cliffs, N.J.: Prentice-Hall. Johnson, B.D., P. Goldstein, E. Preble, J. Schmeidler, D.S. Lipton, B. Spunt, and T. Miller 1985 Taking Care of Business: The Economics of Crime by Heroin Abusers . Lexington Mass.: Lexington Books. Kagan, J. 1982 Psychological Research on the Human Infant: An Evaluative Study . New York: William T. Grant Foundation. Kalbfleish, J., and R. Prentice 1980 The Statistical Analysis of Failure Time Data. New York: John Wiley & Sons. Kennedy, T. 1986 Trends in inmate classification: A status report of two computerized psychometric approaches. Criminal Justice and Behavior 13(2):165-184. King, G. 1988 Statistical models for political science event counts: Bias in conventional procedures and evidence for the Expontential Poisson Regression Model. American Journal of Political Science 32:838-863. Klassen, D., and W. O'Connor 1988a A prospective study of predictors of violence in adult male mental health admissions. Law and Human Behavior 12(2):143-158. 1988b Crime, inpatient admissions, and violence among male mental patients. International Journal of Law and Psychiatry 11:305-312. 1988c Predicting violence in schizophrenic and non-schizophrenic patients: A prospective study. Journal of Community Psychology 16:217-227. 1989a Predictors of Violence: A Review. Paper presented at the Study

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Understanding and Preventing Violence: Volume 4 - Consequences and Control Marquis, K.H., and P. Ebener 1981 Quality of Prisoner Self-Reports: Arrest and Conviction Response Errors. Santa Monica, Calif.: The RAND Corporation. McConaughy, S.H., T.M. Achenbach, and C.L. Gent 1988 Multiaxial empirically based assessment: Parent, teacher, observations, cognitive and personality correlates of child behavior profiles for 6-11-year old boys. Journal of Abnormal Child Psychology 11:485-509. McCord, W., and J. McCord, with I.K. Zola 1959 Origins of Crime. New York: Columbia University Press. Mead, G.H. 1934 Mind, Self and Society. Chicago: University of Chicago Press. Megargee, E.I. 1977 A classification system for male youthful offenders based on the MMPI. Pp. 35-60 in R.A. Keil, ed., Mental Health for the Convicted Offender, Patient, and Prisoner. Raleigh: North Carolina Department of Corrections. Megargee, E.I., and M.J. Bohn 1979 Classifying Criminal Behaviors. Newbury Park, Calif.: Sage Publications. Megargee, E.I., M.J. Bohn, and J.L. Carbonell 1988 A Cross-Validation and Test of the Generality of the MMPI-Based Offender Classification System. Unpublished manuscript, Florida State University, Tallahassee. Menzies, R., and C. Webster 1989 Mental disorder and violent crime. In N. Weiner and M. Wolfgang, eds. Pathways to Criminal Violence. Newbury Park, Calif.: Sage Publications. Menzies, R., C. Webster, and D. Sepajak 1985 The dimensions of dangerousness. Law and Human Behavior 9(1):49-70. Mercy, J.A., and P.W. O'Carroll 1988 New directions in violence prediction: The public health arena. Violence and Victims 3(4):285-302. Milner, J. 1980 The Child Abuse Potential Inventory: Manual. Webster, N.C.: Psytec Corporation. 1989 Additional cross-validation of the Child Abuse Potential Inventory. Psychological Assessment: A Journal of Consulting and Clinical Psychology 1:219-223. Milner, J.S., and R. Wimberley 1979 An inventory for the identification of child abusers. Journal of Clinical Psychology 35:95-100. 1980 Prediction and explanation of child abuse. Journal of Clinical Psychology 35:875-884. Milner, J., R. Gold, and R. Wimberley 1986 Prediction and explanation of child abuse: Cross validation of

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Understanding and Preventing Violence: Volume 4 - Consequences and Control the child abuse potential inventory. Journal of Consulting Psychology 54(6):865-866. Milner, J.S., K.R. Robertson, and D.L. Rogers 1988 Childhood History of Abuse and Adult Abuse Potential. Paper presented at the meeting of the Midwester Psychological Association, Chicago. Milner, J., R. Gold, C. Ayoub, and M. Jacewitz 1984 Predictive validity of the child abuse potential inventory. Journal of Consulting and Clinical Psychology 52(5):879-884. Monahan, J. 1981 Predicting Violent Behavior: An Assessment of Clinical Techniques . Newbury Park, Calif.: Sage Publications. 1984 Prediction of violent behavior—Toward a second generation of theory and policy. American Journal of Psychiatry 141(1):10-15. Moss, C., M. Johnson, and R. Hosford 1984 An assessment of the Megargee typology in lifelong criminal violence. Criminal Justice and Behavior 11(2):225-234. Nurco, D., T. Hanlon, T. Kinlock, and K. Duszynski 1988 Differential criminal patterns of narcotic addicts over an addiction career. Criminology 26(3):407-423. Rhodes, W. 1985 The adequacy of statistically derived prediction instruments in the face of sample selectivity. Evaluation Review 9:369-382. 1986 A survival model with dependent competing events and right-hand censoring: Probation and parole as an illustration. Journal of Quantitative Criminology 2(2):113-137. 1989 The criminal career: Estimates of the duration and frequency of crime commission. Journal of Quantitative Criminology 5(1):3-32. Rivera, B., and C. Spatz Widom 1991 Childhood victimization and violent offending. Violence and Victims 5:19-35. Robertson, K.R., and J.S. Milner 1985 Convergent and discriminant validity of the Child Abuse Potential Inventory. Journal of Personality Assessment 49:86-88. Robins, L.N., and E. Wish 1977 Childhood deviance as a developmental process: A study of 223 urban black men from birth to 18. Social Forces 56(2):448-471. Schmidt, P., and A. Witte 1988 Predicting Recidivism Using Survival Models. New York: Springer-Verlag. Shaffer, J., D. Nurco, and T. Kinlock 1984 A new classification of narcotic addicts based on type and extent of criminal activity. Comprehensive Psychiatry 25(3).

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Understanding and Preventing Violence: Volume 4 - Consequences and Control Simcha-Fagan, O., and J.E. Schwartz 1986 Neighborhood and delinquency: An assessment of contextual effects. Criminology 24(4):667-695. Snyder, H.N. 1988 Court Careers of Juvenile Offenders. Pittsburgh, Pa.: National Center for Juvenile Justice. Spitzer, R.L., and J.B.W. Williams, eds. 1987 Introduction. Pp. xvii-xxvii in Diagnostic and Statistical Manual of Mental Disorders, Third Edition—Revised. Washington, D.C.: American Psychiatric Association. Steadman, H. 1987 How well can we predict violence for adults? A review of the literature and some commentary. Pp. 5-17 in F. Dutile and C. Foust, eds., The Prediction of Criminal Violence. Springfield, Ill.: Charles C. Thomas. Steadman, H., and J. Morrissey 1982 Predicting violent behavior: A note on a cross-validation study. Social Forces 61(2). Toch, H., and K. Adams 1989 The Disturbed Violent Offender. New Haven, Conn.: Yale University Press. Tracy, P., M. Wolfgang, and R. Figlio 1990 Delinquency Careers in Two Birth Cohorts. New York: Plenum Press. Virkkunen, M., J. DeJong, J. Bartko, F. Goodwin, and M. Linnoila 1989a Relationship of psychobiological variables to recidivism in violence offenders and impulsive fire setters: A follow-up study. Archives of General Psychiatry 46:600-603. 1989b Psychobiological concomitants of history of suicide attempts among violent offenders and impulsive fire setters. Archives of General Psychiatry 46:604-606. Weiner, N 1989 Violent criminal careers and violent career criminals: An overview of the recent literature. Pp. 35-138 in Violent Crime, Violent Criminals . Newbury Park, Calif.: Sage Publications. n.d. Violent Recidivism Among the 1958 Philadelphia Cohort Boys, Vol. I. Unpublished manuscript, Center for the Interdisciplinary Study of Criminal Violence, Sellin Center for Studies in Criminology and Criminal Law, The Wharton School, University of Pennsylvania, Philadelphia. Weinstein, E. 1969 The development of interpersonal competence. Pp. 753-775 in D. Goslin, ed., Handbook of Socialization Theory and Research. Chicago: Rand McNally. Widom, C.S. 1989a The intergenerational transmission of violence. In N. Weiner

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Understanding and Preventing Violence: Volume 4 - Consequences and Control and M. Wolfgang, eds., Pathways to Criminal Violence. Newbury Park, Calif.: Sage Publications. 1989a The cycle of violence. Science 244:160-166. Williams, T.M., and W. Kornblum 1985 Growing Up Poor. Lexington, Mass.: Lexington Books. Wolfgang, M., R. Figlio, and T. Sellin 1972 Delinquency in a Birth Cohort. Chicago: University of Chicago Press.