UNIVERSITY OF CINCINNATI. hereby submit this as part of the requirements for the degree of: Approved by: Edward Latessa, Ph.D. Lawrence Travis, Ph.D.

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1 UNIVERSITY OF CINCINNATI DATE: July 19, 2002 I, Dena Hanley hereby submit this as part of the requirements for the degree of:, in: Doctorate of Philosophy (Ph.D.) Criminal Justice It is entitled: Risk Differentiation and Intensive Supervision: A Meaningful Union? Approved by: Edward Latessa, Ph.D. Lawrence Travis, Ph.D. Patricia Van Voorhis, Ph.D. Deborah Wilson, Ph.D.

2 RISK DIFFERENTIATION AND INTENSIVE SUPERVISION: A MEANINGFUL UNION? A dissertation submitted to the Division of Research and Advanced Studies of the University of Cincinnati In partial fulfillment of the requirements for the degree of DOCTORATE OF PHILOSPHY (Ph.D.) in the Division of Criminal Justice of the College of Education 2002 by Dena Hanley B.S., University of Louisville, 1994 M.S., University of Cincinnati, 1995 Committee Chair: Edward Latessa, Ph.D.

3 Abstract Martinson s (1974) statement that nothing works landed a blow to correctional rehabilitation, and researchers have since worked diligently to dispel the notion that correctional rehabilitation and treatment are not worthy endeavors. The result of this work has provided the field with a wealth of knowledge about effective correctional practices. Among these practices is the risk principle. The risk principle contains two parts. First, the risk principle holds that behavior can be predicted. Second, the risk principle maintains that intensive services should be reserved for high-risk offenders. This dissertation examines the latter aspect, the case classification principle. Using a secondary analysis of data collected for several evaluations of intensive supervision programs (ISPs), this dissertation analyzes whether appropriate service delivery impacts recidivism rates of offenders. This dissertation finds that offenders who receive appropriate services (the intensity of service delivery corresponding with an offender s risk level) are significantly more likely to be associated with at least one technical violation, but less likely to be re-arrested. More specifically, the results of this dissertation show that high-risk offenders who receive appropriate services have significantly lower re-arrest rates than do those offenders who do not receive appropriate services. This relationship, however, does not hold true for low risk offenders.

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5 Acknowledgements There are many people who helped make this document and this degree a reality and I have appreciated all of their efforts. The following individuals, however, have contributed greatly to this accomplishment and deserve special recognition. I would like to start by expressing my gratitude to the offenders of the District of Columbia. My years working with you were the most fulfilling and challenging of my career thus far. You gave me motivation to continue working on this project. I would also like to thank my committee. Without each of you, this document would have never been finished. I thank Dr. Latessa for all that you have shown and taught me over these years. Your foresight allowed me to be and do what I love. Dr. Travis, I have actually grown to appreciate both your red pen and your foot. Without both, I would not be done. Dr. Van Voorhis, I thank you for appreciating my strengths, although they may not be typical of most graduate students. Your support helped me more than you know. Dr. Wilson, you have encouraged me all of these years and showed me how finishing can open up the world. Thank you both for paving the way for the rest of us. Please know that your efforts are appreciated. I am forever grateful to many of my colleagues, specifically, Melissa Winston- Burek, Martha Henderson, and Kim Sperber, without whom I would have never made it through the comps. I would also like to thank Bob Kaminski and Cyndy Mamalian for being my DC cohort. I also acknowledge John Jarvis, Meredith Krause, Simone Engelhardt and Randy Greer for their continued support and unconditional friendship.

6 A debt of gratitude goes to my family and extends far beyond this project. I would not be who or where I am without my family. To my father, Patrick, and my brother, Todd, you always gave me inspiration and encouragement, although you may not have understood what I was doing or why. I am especially indebted to my mother, Susan McClain. I am forever grateful for your patience and support. For so many years, you brought me coffee and sandwiches at three in the morning. You quizzed me until you knew the answers and you hand wrote finals with me when the computer crashed. I only hope that my children experience the unconditional love, support and acceptance that I have. There are no words to express my unending love to my daughter, Hanley Raquel and my husband, Eric. You are the reason for everything. Hanley, I started breathing the day you did. I am constantly in awe of you. Eric, I can not put into words what you mean to me. You have always put me first, but now I finally realize it. You are my dream come true and I am taken aback every day when I look into your eyes. I am honored to share this journey and my life with you. Finally, I would like to dedicate this dissertation to three of my heroes, one professional and two personal. First, to the late David Dillingham, you were there when I needed a professional friend and guide. You quietly started a revolution and it has taken hold, they are listening. You are deeply missed. To my grandparents, the epitome of a lady and a gentleman. My grandfather, Ray Raque, you have been a source of strength and comfort in my life. Finally, to the strongest woman who ever lived, my grandmother, the late Beulah Ivanna Raque. I know you can see me, but I wish you were here. I miss you constantly.

7 Table of Contents Chapter I: Introduction 1 Chapter II: Literature Review 7 History of Development of Risk and Needs 8 Meta-Analyses 10 Effective Elements of Correctional Treatment 15 Classification 15 Risk 16 Prediction of Antisocial Behavior 24 Methodological Issues with Prediction 26 Case Classification Risk Principle 27 Specific Risk Elements 29 Summary of Risk 33 Summary of Effective Interventions 34 Intensive Supervision Programs 34 Risk and ISP 43 Conclusion 46 Chapter III: Methods 48 Description of Rand Evaluation Data 48 Methods of Data Collection 50 Sampling Design 51 Measures of Core Concepts 57 Independent Variables 57 Risk: Static Factors 57 Risk: Dynamic Factors 61 Data Manipulation 63 Static Multicollinearity 67 Collapsing of the Factors and Bivariate Analyses 69 Risk Index Reliabilities and Selection of the Sample 71 Supervision/Program Activities 76 i

8 Direct Contacts 79 Treatment Contacts 81 Appropriateness 82 Independent Demographic Variables 86 Control Variables 86 Dependent Variable: Recidivism 87 Statistical Analyses 90 Conclusion 90 Chapter IV: Analyses and Results 92 Sample 92 Demographic Characteristics 93 Criminal History 94 Needs Factors 97 Program Intensity: Contacts 101 Outcome: Recidivism 105 Bivariate Analyses 107 Logistic Regression Models 112 Conclusion 123 Chapter V: Discussion and Conclusion 127 Major Findings 128 Limitations 135 Strengths 138 Implications for Policy Development and Future Research 139 Conclusion 140 Bibliography 142 ii

9 List of Tables Table Page 2.1 Summary of Meta-Analytic Research Results of Gendreau et al. (1996b) Meta-Analysis Summary of ISP Evaluations Site Specific Program Characteristics Missing Values Analysis Results Risk Index Data Elements (Static and Dynamic Factors) Significance Levels between Possible Risk Index Variables 74 (Collapsed) and Measure of Recidivism 3.5 Measurement of Risk Indicators Contract Level Data (Direct Contacts and Treatment Contacts) Distribution of Direct and Treatment Contacts Appropriateness by Risk Level Measurement of Demographic and Background Data Elements Dependent Variable: Recidivism Demographic Characteristics Criminal History Variables Needs Factors Type of Contacts Recidivism Appropriateness and Arrest, by Risk Level Bivariate Analyses Results Logistic Regression Predicting Any Recidivism Failure 117 (Failure=1) 4.9 Logistic Regression Predicting Any Technical Violation 121 (Failure=1) 4.10 Logistic Regression Predicting Any Arrest (Failure=1) 125 iii

10 List of Figures Figure Page 3.1 Appropriateness Variable 84 iv

11 Chapter I. Introduction For many years, there has been debate within the criminal justice system regarding the treatment of offenders. A primary goal of the criminal justice system is to protect the public from crime. One primary method of achieving this goal has been to reduce recidivism rates of offenders once they have come into contact with the system. However, the manner is which this reduction can be achieved has been a source of debate for some time. Recently, research has uncovered a possible solution to the recidivism dilemma: the principles of effective intervention. One of the most salient of these principles is effective classification. Effective classification maintains that services should be allocated to offenders based on those offenders individual risk, need and responsivity factors (Andrews and Bonta 1994; 1998; Kennedy 1999; McGuire 2000). However, much of what is known about effective classification is very theoretical (e.g., meta-analytic research) or selective in its sampling. While examinations of the relationship between classification and interventions have produced support for the risk principle (e.g., Andrews and Kiessling 1980; Andrews and Bonta 1994; Bonta, Wallace -Capretta, and Rooney 2000), a good deal of this literature has not included random assignment and has often examined one site that used a small sample size. This dissertation examines whether or not appropriate services, if dispensed according to risk and needs, produce lower recidivism rates, using data from a large, randomly assigned, multi-site intensive supervision program evaluation. Effective classification encompasses four principles: risk, need, responsivity and professional discretion (Andrews and Bonta 1998). While all of these principles are crucial to effective case management, this study is concerned primarily with the impact of the first principle, risk. 1

12 Specifically, this dissertation examines one primary question. This dissertation asks if the allocation of appropriate services, based on risk levels of offenders, decreases recidivism rates of offenders. The answer to this question will shed light on whether supervision programs, are viable supervision options for high risk offenders. While the ability to accurately predict criminal behavior has been questioned (Cullen and Gilbert 1982; Gove, Hughes and Geerken 1985; Lab and Whitehead 1990), evidence suggests that criminal behavior is predictable (Gottfredson and Hirschi 1990; Loeber and LeBlanc 1990; Sampson and Laub 1993; Moffitt 1993; Farrington 1994). Research further indicates that actuarial predictions are more reliable than clinical assessments (Gottfredson and Gottfredson 1979; 1987; Gendreau, Goggin and Paparozzi 1996a). The ability to predict future behavior is crucial when differentiating offenders into risk categories and in determining what levels of supervision the offender requires. Identification of risk level not only involves static risk factors, such as criminal history, but also dynamic risk factors, such as association with antisocial associates. Dynamic risk factors are also considered criminogenic needs factors (Andrews and Bonta 1994). Needs factors are grouped into two categories: criminogenic and non-criminogenic needs. Criminogenic needs are those issues that have been found to have a significant impact on recidivism (e.g., antisocial peers and antisocial personality). Non-criminogenic needs are those factors that do not correlate with an increased likelihood of criminal behavior. Factors such as lack of housing and mental illness are examples of noncriminogenic needs. These needs may be addressed during the course of supervision, but there is no empirical expectation that addressing these needs will translate into a reduction in the likelihood of recidivism. The needs principle asserts that when correctional practices target criminogenic needs, the likelihood of recidivism decreases. 2

13 The role that individual characteristics, such as risk and needs, play in the success of offenders on community supervision was articulated by Latessa and Gordon (1994: 63): It is essential to evaluate these types of programs, not only in terms of outcome, but also to identify the type of offender who is successful and unsuccessful. The classification principles are becoming increasingly more important to identifying different types of offenders. The utilization of risk and needs elements is critical to understanding the interaction between an individual offender, his/her situation and the supervision/treatment activities that occur while the offender is under supervision. The second aspect of the risk principle entails matching offenders to the appropriate intensity of services based on risk. That is, more intensive services being reserved for higher risk offenders (Andrews et al. 1990a; Andrews and Bonta 1994; Van Voorhis 1997). Empirical research has supported this aspect of the risk principle. For example, Bonta, Wallace -Capretta, and Rooney (2000) found that while recidivism rates were not significantly different between two groups of treated offenders (32.4% for Intensive Rehabilitation Supervision Program (IRSP) 1 offenders and 31 percent for offenders supervised on routine probation), these results changed when the risk level of the offender was considered. Recidivism rates for high-risk offenders supervised under the IRSP and routine probation were vastly different (31.6% and 51.1%, respectively). Support for the principle was also found prior to Bonta et al. s (2000) study. Andrews and Kiessling (1980) observed that providing inappropriate levels of services to offenders actually increased their likelihood of recidivism. Andrews and Kiessling (1980) found that 12 percent of low risk offenders who received minimal services recidivated. This number increased to 17 percent when these low risk offenders were placed in intensive services. 1 Similar to Intensive Supervision Programs, however the emphasis in Intensive Rehabilitation Supervision programs is on treatment. See Gendreau, Cullen and Bonta (1994). 3

14 Conversely, high-risk offenders placed in intensive services recidivated at a rate of 31 percent. This number increased to 58 percent when high-risk offenders were placed in minimal intensity services. While the field of corrections has made significant advances in the areas of risk and needs, appropriate interventions for offenders may not be readily available. Appropriate levels of both treatment and supervision are required to make reductions in the probability of recidivism a reality. Intensive supervision programs (ISPs) may be able to fill part of the appropriate service gap, providing a level of control between probation and prison (Morris and Tonry 1990). In the mid to late 1980s, the corrections field reached a critical point in terms of prison populations. The corrections field was forced to develop an answer to prison crowding. The response was intermediate sanctions, and at the forefront, intensive supervision programs (Byrne 1990; Fulton, Stone and Gendreau 1994; Latessa and Allen 1997; Lurigio 1990; Petersilia 1998). Intensive Supervision Programs (ISPs) were designed to increase control through contacts with the criminal justice system (along with other conditions), at a lower cost than incarceration (Latessa and Allen 1997). While ISPs were implemented across the country, evaluations of these programs produced mixed results. Evaluations of the Georgia model and other early ISPs were very positive (Conrad 1985; Erwin 1986; Pearson 1988), showing reductions in recidivism and substantial reductions in costs. However, many of these early evaluations were methodologically flawed for various reasons, including the lack of control groups and failure to include cost estimates of recidivistic instances. These methodological weaknesses may have biased the 4

15 evaluation results (General Accounting Office 1990), increasing the likelihood of positive findings. Many of the later evaluations found little difference or negative effects between ISPs and routine supervision. For example, Baird and Wagner (1990) found that incarceration rates in Florida increased after the implementation of an ISP, while Turner and Petersilia (1991) found that Texas parolees released to ISPs had incarceration rates 1.7 times higher than those released to routine probation. The General Accounting Office (1990) examined 28 different ISP evaluations and concluded that these programs had little effect on prison crowding due to the small numbers of participants in these programs. Higher incarceration rates also complicated ISPs ability to reduce prison costs. While some research suggested that ISPs are among the least costly correctional sanctions (Erwin 1986; Latessa 1986; Pearson 1988; Austin, Quigley and Cuvelier 1989), many of these evaluations did not include the costs of revocations and other system responses to violations by offenders (GAO 1990; Petersilia, Turner and Deschenes 1992b). These system responses may have increased the cost estimates of ISPs. While there is no shortage of literature concerning ISPs, there is significant ambiguity regarding the ability of ISPs to reduce recidivism. However, elements of ISPs have shown promise in reducing recidivism. Evidence suggests ISPs with a treatment component are more successful than programs without treatment (Petersilia and Turner 1990a; 1990b; Turner and Petersilia 1992; Petersilia 1998). This dissertation will shed light on this issue by examining the relationship between risk, program activities (including treatment services) and recidivism. Specifically, the research questions addressed in this study are: 1) Are services and the number of contacts allocated appropriately based on risk and needs factors? 5

16 2) Are success rates higher for those offenders who are given appropriate services and supervision than the rates of success for those offenders who are not appropriately serviced and/or supervised? 3) Based on the results of the above questions, should special supervision practices, such as ISPs, be advocated as supervision option for high risk offenders? The Bureau of Justice (BJA) allocated funding for a demonstration project to evaluate the effectiveness of ISPs in The Rand Corporation was awarded a grant to evaluate 14 selected sites 2 across the United States. This data set provides a unique context in which to examine risk and needs because it utilized random assignment for the samples involved and incorporated samples from multiple sites across the United States. In addition, these data contain background information on offenders, recidivism information on each offender (at six and twelve month intervals), and criminal justice contact information (including the type of treatment services provided). Using these data, it will be possible to examine whether the combination of risk and needs considerations with the allocation of appropriate services increased the rate of success for individuals under the supervision of intensive supervision programs. 2 The sites were Contra Costa, Ventura and Los Angeles counties, California; Dallas and Houston, Texas; Seattle, Washington; Santa Fe, New Mexico; Des Moines, Iowa; Winchester, Virginia; Atlanta, Macon and Waycross, Georgia; Marion county, Oregon; and Milwaukee, Wisconsin. 6

17 Chapter II. Literature Review The prediction of criminal behavior is not a new endeavor for the field of corrections. Attempts have been made to develop instruments that identify those offenders at risk to recidivate since the 1920s (Bonta 1996). While these attempts have been numerous, it is only recently that risk and other assessments have been used for the planning of supervision activities. Stemming from almost two decades of research, the connection between classification elements, interventions and recidivism is now evident. Many of the works that led to the identification of this empirical relationship began in response to Martinson s (1974) nothing works declaration. Most of this research has uncovered trends of positive results for treatment and interventions with the stipulation that not all treatment if effective all of the time. It is the disentanglement and identification of factors that are effective at reducing recidivism that has compelled academics and practitioners alike to begin this exploration. The result of their efforts is a body of literature that outlines the elements of effective classification (including risk identification) and effective intervention practices. Much of the meta-analytic literature has focused on treatment issues. For example, this literature revealed that programs with a sound theoretical base are more effective in reducing recidivism than programs not based on a particular theory. Further, this research has determined that program characteristics, such as the integrity of the program, dosage of the program and whether or not a relapse prevention component exists may impact the program s ability to assist offenders in maintaining a crime free lifestyle. Despite the amount of empirical evidence now available in the field of corrections regarding the relationship between risk, treatment and recidivism, little is known about the impact of appropriate supervision services on recidivism. However, one of the most consistent results in this literature is that effective programs adhere to 7

18 the risk principle, that intensive services should be reserved for higher risk offenders. The relationship between risk, supervision and recidivism is of primary concern for the current inquiry. It is unclear whether intensive supervision programs have subscribed to the risk principle and high risk offenders in the past. Although these programs were designed for the supervision of high risk offenders, evaluations of these programs have shown that this did not always occur. Whether high risk or low risk offenders have reduced recidivism rates under intensive supervision remains to be seen. This chapter begins by tracing the development of risk and needs into the findings from the meta-analyses. The results of these meta-analyses, the principles of effective intervention, are then discussed. Within this discussion, the classification principle, specifically the risk principle is reviewed. Following this discussion, the history of intensive supervision programs and evaluations of these programs are presented. History of Development of Risk and Needs In 1974, Robert Martinson (1974: 25) concluded rehabilitative efforts that have been reported so far have had no appreciable effect on recidivism. Martinson reached this conclusion by superficially examining evidence provided by 231 evaluations of treatment programs conducted between 1945 and While Martinson (1974) was not the first nor last to assert that treatment programs had failed in reducing recidivism (Kirby 1954; Logan 1972; Whitehead and Lab 1989), his report was embraced by practitioners, politicians, the media, and perhaps most damaging, the general public. 8

19 Martinson failed to find the panacea of correctional treatment and therefore, claimed that all treatment was ineffective. Other researchers have asserted the same conclusion due to the lack of a one answer fits all result when examining treatment literature (e.g., Whitehead and Lab 1989). Even Palmer (1994), a supporter of rehabilitation, contends that research has failed to identify one category of treatment that works with most (categorized as more than 2/3) of offenders. In fact, Palmer (1994) explains that sizable reductions in recidivism (greater than 25%) did not occur in even 1/3 of the studies he examined, although reductions of percent were widespread. However, the search for, and subsequent failure to find the treatment panacea has suppressed promising and valuable trends in the research results. Many felt that Martinson s conclusion was not only harsh, but that the methodology behind the conclusion was flawed. Palmer (1976) re-examined Martinson s study and discovered that while absent from the conclusion of the paper, several trends were evident in the body of Martinson s report. Palmer (1976) concluded that Martinson s data reflected that middle risk offenders (not the highest risk nor the lowest risk offenders) perform better under community supervision (probation and/or parole) than in institutions. Palmer also suggested that the statement made by Martinson did not necessarily reflect an accurate picture of these data. While re-analyzing 82 of the studies included in Martinson s sample, Palmer found that 39 of them, or 48 percent, showed at least some positive results. Palmer suggested that these 48 percent exceed the few exceptions indicated by Martinson in his conclusion. Although Palmer s (1976) work was a powerful start, it has taken over twenty years to begin to dispel the notion that Martinson embedded in so many associates of the criminal justice system. Many have diligently researched the area of effective correctional treatment and practices. Through those efforts, we today have a virtual checklist of what works in 9

20 correctional treatment. Much of this research has been accumulated from three primary sources: literature reviews of treatment research, meta-analyses, and individual evaluations and personal experiences (Gendreau 1996). One of these sources, meta-analyses, has contributed greatly to this body of knowledge. Meta-Analyses Methodological problems have plagued research studies. For example, assumptions of many sophisticated statistical techniques require large sample sizes and fail to show any significance with smaller samples. Small sample sizes may have compromised results of studies by hiding a treatment effect when, in actuality, one was present (Lipsey 1992b). Similarly, studies can suffer from methodological difficulties such as sampling limitations and unreliable measures, prompting great variation in results from study to study (Lipsey 1995). These factors are common and may impact results making the body of literature on a specific topic seem inconsistent and confusing, even when examining studies of interventions that are known to be effective (Lipsey 1995). Meta-analysis, a technique gaining popularity in the criminal justice field, is widely accepted as a method for summarizing results from a number of studies into one conclusion (Robey and Dalebout 1998; Lipsey 1999b). A meta-analysis is a statistical analysis of a collection of studies that aggregates the magnitude of a relationship between two or more variables. It can estimate the statistical significance of the experimental effect, its magnitude, its variability, as well as explore the nature of moderating variables (Latimer 2001: 241). The meta-analysis technique addresses many of the methodological issues of single studies by compiling results. For example, small sample sizes are not a major obstacle for meta-analysis. 10

21 This technique manages small sample sizes by pooling the samples from many studies, producing large sample sizes. This technique then utilizes the results found in those studies (typically differences between the experimental and control groups) and compiles these results into a single outcome, which is based on a large number of cases. Meta-analyses produce an effect size statistic which allows for an aggregate result of the original studies. Lipsey (1999a: 145) explains the effect size index can be read as a statement of how much better the recidivism was for juveniles receiving intervention than for those in the control group, measured in standard deviation units. Different statistics are used for effect sizes (e.g., Pearsons Product Moment Correlation, Cohen s d) and each has a different interpretation. However, effect sizes are very useful in examining the differences in success rates between experimental and control groups Meta-Analytic Contributions to Corrections Meta-analyses have advanced the correctional field in two crucial ways. First, metaanalyses have assessed whether interventions are effective in reducing recidivism. Second, meta-analyses identify those characteristics and interventions of programs that are most promising in the reduction of recidivism. Most meta-analytic results have supported treatment and rehabilitation (McLaren 1992; Lipsey 1999b). One of the first meta-analyses to examine treatment effectiveness was conducted by Garrett in Garrett (1985) examined 126 studies involving interventions with juvenile delinquents. She concluded that experimental groups (those who received treatment) outperformed (in terms of success) control groups by.37 standard deviations across all the studies she examined. 11

22 One of the most cited meta-analysis on the topic of correctional interventions was conducted by Andrews et al. (1990b). These researchers categorized 70 studies of juvenile delinquents and adult offenders into four groups: appropriate services, inappropriate services, unspecified services and criminal sanctions. They found that appropriate services were more strongly related to reductions in recidivism than the other categories of services (phi=.30 for appropriate services). Positive findings were also reported in subsequent studies employing the meta-analysis technique. In one of the most comprehensive meta-analyses to date, Lipsey (1992a) analyzed 443 studies of juvenile delinquency and discovered an effect size of.10, which translated into a recidivism difference of five percent between the experimental and control groups. Similarly, Lipsey s (1999b) analysis of 200 studies of serious juvenile delinquency resulted in a slightly higher effect size of.12. Further reductions in recidivism were experienced by programs that were experimental or demonstration programs (Lipsey 1999a). Violent juveniles were also found to have benefited from treatment interventions. In an Office of Juvenile Justice and Delinquency Research Brief, Lipsey, Wilson and Cothern (2000) reported that serious juvenile offenders averaged a 12 percent decrease in recidivism after participation in various interventions. Specific interventions have been found to be effective with delinquent and offender populations. Wilson and Lipsey (2000) found that wilderness programs had some effect on delinquents. They found that juveniles who participated in wilderness programs (k=28) had recidivism rates nine percent (effect size=.18) lower than delinquents who did not participate in these interventions. Latimer (2001) revealed that interventions which focused on family interventions had an effect size of.15 on juveniles. Finally, Dowden and Andrews (1999b) 12

23 observed an effect size of.07 for those programs that adhered to specific principles of effective intervention. While the majority of meta-analyses have focused on studies involving juvenile delinquents (Garrett 1985; Whitehead and Lab 1989; Izzo and Ross 1990; Lipsey 1990; 1999; McLaren 1992; Palmer 1992; Losel 1995a; Dowden and Andrews 1999; 2000; Wilson and Lipsey 2000; Latimer 2001), a few studies have attempted to ascertain effects of correctional interventions on mixed samples of delinquents and adult offenders. Lipsey, Chapman, and Landenberger (2001) found that the experimental group (juveniles and adults participating in cognitive behavioral programs) had 66 percent fewer recidivists than the control group. Dowden and Andrews (1999a) discovered a slightly higher effect size of.14 in a meta-analysis of 26 studies, focusing primarily on juvenile and adult female offenders. Wilson, Gallagher, and MacKenzie (2000) revealed participation in work, educational and vocational programming could reduce recidivism rates by 11 percent. Whitehead and Lab (1989), skeptics of the effectiveness of rehabilitation, found that while no single treatment was effective for all juveniles, that some individual programs did reduce recidivism among their participants (e.g., system diversion programs), although they concluded that treatment was generally ineffective 3. Table 2.1 summarizes the results of several meta-analyses regarding correctional treatment. Small effect sizes have prompted some researchers to declare treatment and rehabilitation ineffective (e.g., Martinson 1974; Whitehead and Lab 1989). However, results from these and other studies 4 have been consistently positive (Lipsey 1995). The rare, negative deductions have 3 Although in a later publication (Lab and Whitehead 1990: 408) they reported an effect size of.12 for these studies, indicating a six percent reduction in recidivism rates for those juveniles who participated in some interventions. 4 Gendreau and Ross (1987) found reductions in recidivism between percent, however, some studies showed reductions in recidivism by 50 to 80 percent. Gendreau and Goggin (1996) explained that programs that adhered to the principles of effective intervention produced reductions in recidivism of 15-13

24 been misleading. For example, Whitehead and Lab (1989) required very high standards 5 for success. They required phi coefficients of.20 or.30 (effect sizes or differences between experimental and control group recidivism rates between 20-30%), claiming that.30 is too far from 1.0 (a perfect correlation) to be meaningful (Lab and Whitehead 1990). However, their high standard of effectiveness is unrealistic. As Losel (1995a) suggested, even small effect sizes should not be dismissed. He cited the medical field as an example. Losel reported that the mean effect size of chemotherapy in preventing mortality was.05 in breast cancer patients and the impact of an aspirin on a heart attack was.04. McGuire (2000) states that the effect size of aspirin on myocardial infraction was.034. However, such modest effects are nevertheless considered sufficient justification for the extensive use of these interventions in healthcare services (McGuire 2000:4). Losel (1995a: 32) also pointed out that In offender treatment, it is necessary to consider a series of factors that make a large effect seem unrealistic. comparisons are rarely made between treatment and nothing. Less explicit treatments or punishment in the control group can also lead to behaviour modifications. In addition, characteristics are evident in the lives of the offender that are unrelated to treatment or interventions (those characteristics that are present prior to and after treatment and related to misbehavior) and yet, may be responsible for outcomes. Despite a few dissensions, it appears as though the majority of the empirical evidence obtained from meta-analyses supports treatment as an effective method in reducing recidivism. 30 percent. Lipsey (1990) found reductions in recidivism of 9-12 percent. McGuire (2000) asserted that interventions have produced reductions in recidivism by 5-10 percent (McGuire 2000). 5 It appears as though the lack of confidence in effect sizes of.30 or less stems from the practice of squaring the r and producing a percentage of explained variance (e.g., Lab and Whitehead 1990). However, this practice is now outdated (Gendreau, Goggin and Paparozzi 1996). 14

25 The second, possibly larger contribution made by meta-analyses is the identification of those characteristics that make interventions effective. This research has provided corrections with a virtual checklist of characteristics of what works and what does not work in correctional interventions. Effective Elements of Correctional Treatment It has not been sufficient to empirically prove that rehabilitation and interventions are effective with offenders. Through the results of meta-analyses, researchers have been able to determine that effective intervention can be distinguished from ineffective treatment through various characteristics of the treatment. Although the meta-analysis technique pools results, it also provides estimates of the heterogeneity of individual study results. If this variation is larger than what can be expected through sampling error, then it is suggested that differences in results exist among the studies. Researchers then begin dissecting the studies, based on these estimates, to determine those factors that make some interventions more effective than others (Lipsey 1992a; 1995; 1999a). Effective treatment is distinguished from ineffective treatment through what are now known as the Principles of Effective Intervention (Gendreau et al. 1999a). These principles entail many elements of intervention and treatment programming. Classification Classification is the first step to effective correctional treatment. Andrews (1994:3) states that appropriate correctional treatment service is dependent upon assessments that are sensitive to risk, need and responsivity. Classification is essential for matching offenders to appropriate treatments that promote success while on supervision (Fulton et al. 1994). According to Fulton et al. (1994: II-4), case 15

26 classification: consists of a set of guidelines that specifically attempts to link types of clients, in this case probationers and parolees, with the clinical and administrative decisions of the probation or parole officer. Essential to the case classification process is that of prediction; that is, the ability to make decisions about the offender s future behavior based on past and present factors. It is these past and present factors that are defined in terms of risk and needs. Matching offenders with appropriate services has been consistently found to be effective case management practice (Andrews et al. 1990b; Fulton, Stone and Gendreau 1994). There are several elements to determining which services are appropriate for offenders. There are four principles to classification: risk, need, responsivity and professional discretion (Andrews and Bonta 1994; 1998; Kennedy 1999; McGuire 2000). Estimates have shown that adherence to the principles of risk, need and responsivity has reduced recidivism by approximately 30 percent (Andrews et al. 1990b; Dowden and Andrews 1999b 6 ). While the elements of need, responsivity and professional discretion are important and necessary for case planning, this dissertation is focused on the first classification principle, risk. Risk The principle of risk involves two separate, but related concepts. First, this principle assumes that criminal behavior is predictable (Andrews 1994; 1996; Andrews and Bonta 1994; Van Voorhis 1997). Second, the risk principle purports that more intensive services should be 6 Dowden and Andrews (1999b) found an effect size of.28 for the most promising programs. 16

27 Table 2.1. Summary of Meta-Analytic Research Author(s) Garrett, C Year Published Description K 7 Mean Effect Size Residential Programs for Juvenile Delinquents a Whitehead and Lab 1989 Juvenile Programs Izzo and Ross 1990 Juvenile Programs 46 N/A Andrews, et al. 1990b Adult and Juvenile Programs (Treatment Only) b9 Lipsey, M. 1992a Juvenile Delinquents c Cox et al Alternative School 57 Findings More rigorous studies were associated with lower effect sizes. Behavioral approaches, family treatment and contingency management were effective treatments. System diversion and programs with a behavioral base were the most promising interventions. Programs with a theoretical basis were five times as effective as non-theoretical programs. Cognitive programs were twice as effective as non-cognitive programs. Appropriate programs (those that adhered to the principles of risk, need and responsivity) effectively reduced recidivism. Effect sizes are largely impacted by methodology of the study, especially the extent of involvement by the researcher. Treatment modality was the most strongly correlated individual variable..00(comparison) Similar trends were found with vote.40 (pre/post) 10 counting and meta-analytic technique. 7 K= Number of studies contributing to the effect size cited in the meta-analysis 8 As cited in Lab and Whitehead (1990:408). 9 Includes only appropriate services. Andrews et al. (1990b) state that appropriate services include those that adhere to the principles of risk, need and responsivity. 17

28 Table 2.1. Summary of Meta-Analytic Research Author(s) Nagayama Hall 1995 Year Published Description K 7 Mean Effect Size Placement for Juveniles Adult and Juvenile Sex Offenders 12.12b Dowden and Andrews 1999a Female Offenders b Dowden and Andrews 1999b Lipsey, M. 1999a Lipsey, M. 1999b Juvenile Program Adherence to Risk and Needs b Demonstration Programs for Juveniles c Serious Juvenile Delinquents c Findings counting and meta-analytic technique. Targeting a select population was significantly related to overall effect size. Longer follow-up periods (longer than 5 years), outpatient, cognitive behavioral and hormonal treatments produced larger effect sizes. Antisocial cognition and family process were strongly correlated with effect size, involvement of the evaluator was significantly correlated with effect size. Support was found for risk, need and responsivity principles (.12 for services delivered to higher risk,.22 for need, and.24 for responsivity). 20% of variation in effect size explained through methodological variables, Programs categorized as most favorable programs had mean effect sizes of , producing a 38-40% recidivism reduction. Programs with high levels of program integrity and longer follow-up periods were more effective. 10 Effect sizes relating to delinquency only. Other outcomes were used. Overall effect size was.11 for comparison groups and.36 for pre/post test groups. 18

29 Table 2.1. Summary of Meta-Analytic Research Author(s) Redondo et al Dowden and Andrews 2000 Gallagher et al Lipsey et al Year Published Description K 7 Mean Effect Size Juvenile and Adult Offenders in Europe 32.12b Violent Offenders - Examines Principles of Classification b Juvenile and Adult Sex Offenders c Serious Juvenile Offenders d11 Findings Treatment cluster of variables (including type of treatment) associated with 48% of explained variance in effect size, cognitive behavioral treatment highest (.23).. Programs that targeted criminogenic needs were associated with higher effect sizes (.69) than programs that targeted non-criminogenic needs (-.30). Composite scores of recidivism and longer follow-up periods (longer than 5 years) were methodological variables associated with higher effect sizes. Type of treatment (behavioral and psychosocial), outpatient location and non criminal justice sponsored programs were program factors associated with higher effect sizes. Juvenile offender characteristics, treatment elements, dosage, general program characteristic clusters were associated with 50% of the variance in overall effect sizes for both institutionalized and non- 11 This report did not report effect size, only differences between experimental and control groups (6% reduction from 50% base rate). The effect size in this table is estimated for simple comparison. Estimates are based on multiplying the percentage of difference by 2 and multiplying by

30 Table 2.1. Summary of Meta-Analytic Research Author(s) Wilson et al Year Published Description K 7 Mean Effect Size Adults in Educational, Work and Vocational Programs d12 Wilson and Juvenile Wilderness Lipsey 2000 Programs c Latimer, J Lipsey et al Programs focusing on families in Youth Treatment Juvenile and Adult Cognitive Behavioral Programs 14.66e Findings institutionalized juveniles. However, the strength of the clusters varied between these two groups. Methodology and type of intervention explained much of the variance in effect size, largest difference was between post-secondary programs and vocational programs. Wilderness programs with a treatment component were correlated with larger effect sizes. Programs that were voluntary, short term (30 days or less), community based, grounded in sound theory and included the entire family were most effective Higher effect sizes were found in demonstration programs than practical programs. 12 Recidivism rates for the program (experimental groups) were averaged and then compared to the comparison group base rate of 50%. Since this study reported only odds ratios, an effect size was estimated. 20

31 Table 2.1. Summary of Meta-Analytic Research Author(s) MacKenzie et al Year Published Description K13 Mean Effect Size Juvenile and Adults in Boot Camps e Findings Boot camps with a treatment component were more effective. Also, adult programs with an aftercare component were also more effective. a=effect size computed as the difference between the mean of the treatment group and control group divided by the standard deviation of the control group. b=effect size computed as a phi coefficient or Pearson Product Moment Correlation. c=effect size computed as the difference between the mean of the treatment group and control group, divided by the pooled standard deviation of those groups. d=estimated effect size based on reported differences in the recidivism rates between the experimental and control groups. e=effect size based on odds ratio of treatment to control group recidivism. 13 K= Number of studies contributing to the effect size cited in the meta-analysis 21

32 reserved for higher risk offenders (Gendreau and Ross 1987; Andrews et al. 1990a; 1990b; Andrews 1994; Andrews and Bonta 1994; Gendreau et al. 1994; Gendreau 1996; Gendreau and Goggin 1996). Andrews and Hoge (1995) call this second component the case classification risk principle. One of the most important issues in criminal justice is the prediction of criminal behavior (Andrews and Bonta 1994). That is, the identification of those offenders who will continue to violate the law and those who will not. Attempts to predict criminal behavior are not a recent phenomenon (Bonta 1996), nor are they uncommon. In fact, one of the first attempts to develop an actuarial prediction instrument was in 1928 by Burgess (Burgess in Bonta 1996). Burgess developed an empirical assessment that included 21 items and differentiated offenders based on a total score of how many items were present in the individual offender s case. More than threequarters (76%) of offenders who scored high on Burgess scale recidivated, compared with only 1.5 percent of those who scored low on the scale (Bonta 1996). Similarly, Sheldon and Eleanor Glueck (1950) also attempted to statistically predict those juveniles who were at risk for delinquent behavior in Massachusetts. However, actuarial risk assessments have progressed greatly from the works of Burgess and Gluecks. Bonta (1996) describes the progression of risk instruments in terms of generations of assessments. First generation assessments relied solely on the intuition of the assessor. They were subjective and based solely on the professional judgment of the officer. These assessments relied on the collection and interpretation of information so that the likelihood of illicit behavior could be ascertained. Due to differences in how information was collected, what information was collected and how this information was interpreted, bias, discrimination and unfairness 22

33 easily entered into the determination of risk. While these types of assessments are still in use today, decisions that are based completely on professional judgment are questionable at best. Second generation assessments attempted to rectify many of the issues surrounding first generation assessments. Second generation assessments improved on the first generation by using statistical techniques for identifying factors that were considered in the decisions. This generation opened the decision making process and increased accountability because factors used to make decisions were visible. Both the Gluecks and Burgess instruments would be considered second generation assessments. However, second generation assessments suffered from their own problems. Second generation assessments were heavily dependent upon static, unchangeable factors. This reliance on static factors complicated treatment and case planning. Current examples of second generation assessments include the Salient Factor Score (SFS) (Gottfredson, Wilkins and Hoffman 1978) and the Wisconsin Risk Assessment (Baird, Heinz and Bemus 1979). Third generation assessments typically combine risk and needs factors into one instrument. These assessments contain dynamic factors that can serve as intermediate outcome indicators for both the correctional program and the offender. These indicators can assist in the case planning process by outlining the offender s treatment needs and achievements. The Level of Service Inventory (LSI) (Andrews and Bonta 1995) and the Offender Intake Assessment (OIA) (Motiuk 1997) are both examples of third generation risk assessments. In an attempt to discover which generation of instruments were most accurate, Gendreau et al. (1996b) meta-analytically compared the predictive ability of the LSI to the SFS, the 23

34 Wisconsin and a category of other 14. They found that while the LSI-Revised instrument was more strongly associated with risk (r 15 =.33) than either the SFS (r=.26), the Wisconsin (r=.32) or the Other category (r=.30), the differences between these instruments were not significant, although all of the instruments were significantly related to recidivism. Therefore, empirical evidence, at least from one study, suggests that second and third generation risk assessments are closely associated with recidivism. Prediction of Antisocial Behavior The prediction of criminal behavior is the first of two concepts contained in the risk principle. While the ability to predict behavior may be questionable (e.g. Cullen and Gilbert 1982; Gove, Hughes and Geerken 1985; Lab and Whitehead 1990), there is sufficient evidence to suggest that prediction is plausible (Gottfredson and Hirschi 1990; Loeber and LeBlanc 1990; Moffitt 1993; Sampson and Laub 1993; Farrington 1994). There have been a number of attempts to predict future criminal behavior that have met with varying degrees of success. There are many benefits to the prediction and identification of risk. For example, Farrington (1987) suggests that prediction serves two purposes: to give decision makers more information on who will and will not succeed and to provide insight into possible new theories of misbehavior. However, as Brennan (1987) points out, risk alone may not be able to help practitioners in explaining or treating criminal behavior. It is the combination of risk and needs (as well as static and dynamic factors) that can assist programs in developing comprehensive treatment plans, useful for determining program activities while on supervision. 14 The Other category primarily contains variations on the SFS. 15 The weighted estimation of r is presented, accounting for variations in the sample sizes of the original studies. 24