Can 14,737 women be wrong? A meta-analysis of the LSI-R and recidivism for female offenders

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Can 14,737 women be wrong? A meta-analysis ofthe LSI-R and recidivism for female offenders*

Paula SmithFrancis T. CullenEdward J. Latessa

University of Cincinnati

Research SummaryOver the past two decades, researchers have been increasingly inter-ested in measuring the risk of offender recidivism as a means ofadvancing public safety and of directing treatment interventions. In thiscontext, one instrument widely used in assessing offenders is the Levelof Service Inventory-Revised (LSI-R). Recently, however, the LSI-Rhas been criticized for being a male-specific assessment instrument thatis a weak predictor of criminal behavior in females. Through the use ofmeta-analytic techniques, we assessed this assertion. A total of 27 effectsizes yielded an average r value of .35 ([confidence interval] CI = .34 to.36) for the relationship of the LSI-R with recidivism for femaleoffenders (N = 14,737). When available, we also made within-samplecomparisons based on gender. These comparisons produced effect sizesfor males and females that were statistically similar.

Policy ImplicationsThese results are consistent with those generated in previous researchon the LSI-R. They call into question prevailing critiques that the LSI-R has predictive validity for male but not for female offenders. At thisstage, it seems that corrections officials should be advised that the LSI-R remains an important instrument for assessing all offenders as a prel-ude to the delivery of treatment services, especially those based on theprinciples of effective intervention. Critics should be encouraged, how-ever, to construct and validate through research additional gender-specific instruments that revise, if not rival, the LSI-R.

* Direct correspondence to Paula Smith, 600G Dyer Hall, 2600 Clifton Ave.,University of Cincinnati, Cincinnati, OH 45221 (e-mail: [email protected]).

CRIMINOLOGY & Public PolicyVolume 8 Issue 1 Copyright 2009 American Society of Criminology

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Keywords: offender assessment, Level of Service Inventory, rehabili-tation, correctional policy

The dominant reality of U.S. corrections is the inordinate expansion ofthe correctional system. Since the early 1970s, prison and jail populationshave increased from approximately 200,000 to more than 2.3 million(Sabol, Couture, and Harrison, 2007; Warren, 2008). On any given day,one in a hundred Americans is behind bars (Warren, 2008). Equally troub-ling, when probation and parole are considered, the number of individualsunder state supervision exceeds 7 million (Glaze and Bonczar, 2006). Asmight be anticipated, a steady stream of books have been written docu-menting and seeking to understand this era of mass incarceration andcommunity control (see Abramsky, 2007; Gottschalk, 2006; Lynch, 2007;Simon, 2007; Tonry, 2004). Regardless, DiIulio (1991) was prescient whenhe warned nearly two decades ago that there would be “no escape” fromthese correctional realities.

As Feeley and Simon (1992) point out, the growth of the correctionalenterprise has created an organizational crisis as to how to house, feed,supervise, and ultimately release the large and unending stream of human-ity that flows through the system (see also Simon, 1993). At its worst, thissituation has led to a “new penology” in which a disturbing goal of dis-placement transpires—where the larger social purposes of corrections,such as rehabilitation, are replaced by the daily, pragmatic need to moni-tor and process correctional populations. In the best case scenario, apremium is placed on those correctional administrators who can confrontchallenging organizational conditions—the most obvious of which isintense overcrowding or large case loads—and in turn foster a safe envi-ronment and the delivery of human services (see DiIulio, 1987).

Although not new—they extend back to the work of Ernest Burgess in1928 and to Sheldon and Eleanor Glueck in 1950 (Bonta, 1996; Jones,1996)—offender assessment instruments are key tools in managing correc-tional populations. These instruments are employed primarily to measurethe “risk” that an offender will recidivate if released into the community.These assessments coincide with new penology and managerial thinking inthat they can be used to routinize and rationalize decision making thatotherwise would be discretionary (e.g., who to release on parole and whoto give probation). In this way, the correctional system gains in efficiency,and the community presumably gains in public safety. At the very least, itseems that criminal justice officials are taking credible steps to use scienceto manage risk, which includes dangerous offenders (Simon, 1993).


Gender and Risk Assessment 185

These instruments compile information about each offender, which inturn is used to predict the risk of recidivism. For the most part, the riskfactors measured have been historical and “static” in nature (for a review,see Andrews and Bonta, 2006). That is, the instruments have focused onwhat has already occurred in an offender’s life (historical) and on whatcannot be changed (static). For example, a person’s criminal history is animportant predictor of future waywardness; still, an intervention cannotchange a criminal record because it is, by its nature, in the past and thusunchangeable. This approach is not necessarily a problem if one wishesonly to assess the risk of recidivism and to manage inmates or controloffenders under community supervision. But as Bonta (1996:22) pointsout, instruments composed primarily of static factors provide “little direc-tion for rehabilitation. . . . Rehabilitation is based on the premise thatpeople can change, and if assessment is to contribute to rehabilitationefforts, it must be capable of measuring change.”

In this context, an alternative approach to offender assessment has beendeveloped by Canadian psychologists Don Andrews, James Bonta, PaulGendreau, and others (Andrews and Bonta, 2006; Bonta, 1996; Gendreau,1996); together, they are recognized as comprising the Canadians’ schoolof correctional intervention (Cullen, 2002). Their ideas were developedwithin a correctional and national culture that was social-welfare oriented,and they were informed by professional ethics from their home disciplineof psychology. Perhaps not surprisingly, their goal was to improve the livesof offenders; accordingly, they rejected the managerial, new-penologyview of risk assessment prevailing in the United States during the “gettough” era of the 1980s and beyond (Simon, 1993). The Canadians’approach has three major features, which are outlined next.

First, building on the existing empirical evidence (see, e.g., Gendreau,Little, and Goggin, 1996), the approach argues that recidivism is predictedmost accurately by including both static risk factors (e.g., criminal history)and dynamic risk factors. Dynamic risk factors, which are also called“criminogenic needs,” are those predictors of recidivism that are poten-tially mutable. They would include, for example, antisocial attitudes,family functioning, and association with criminal peers.1

1. According to Andrews and Bonta (2006), the major risk factors include thefollowing: (1) antisocial/procriminal attitudes, values, and beliefs; (2) antisocial/procriminal peers; (3) temperament and personality factors such as being aggressive,impulsive, adventurous, and pleasure seeking; (4) a history of antisocial behaviors; (5)family factors such as family criminality, lack of caring and cohesiveness, as well asneglect and abuse; (6) low levels of educational, vocational, or financial achievement;


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Second, rejecting new-penology thinking, the purpose of offenderassessment should be not only risk management but also offender treat-ment. The inclusion of dynamic factors is critical because it is these“criminogenic needs” that rehabilitation programs will target for change.That is, by focusing on and changing these factors (e.g., reducing antisocialthinking), offenders’ recidivism will be reduced. This strategy is referred toas the “need principle.” Another principle of effective intervention setforth by Andrews and his colleagues is the “risk principle,” which statesthat treatment should be devoted primarily to high-risk offenders. The“responsivity principle” asserts that treatment modalities should be usedthat can alter the criminogenic needs that underlie recidivism. Cognitive-behavioral programs are viewed as being particularly appropriate for thistask (Andrews and Bonta, 2006; Gendreau, Smith, and French, 2006; Lip-sey, Chapman, and Landenberger, 2001; see also MacKenzie, 2006).

Third, and of particular concern here, the Canadian scholars have devel-oped an assessment instrument that predicts recidivism and can be used asthe basis for effective intervention. This instrument is called the Level ofService Inventory or, the LSI as it is now commonly known; its revisedversion is termed the LSI-R (Andrews and Bonta, 2006; Bonta, 1996). TheLSI-R consists of 54 items, most of which measure dynamic risk factorsthat are known as having criminogenic needs. Existing studies show thatthe LSI-R has predictive validity with regard to recidivism (Gendreau,Goggin, and Smith, 2002; Gendreau et al., 1996; Vose, Cullen, and Smith,2008). Based on the extant research, Andrews and Bonta (2006:289) con-clude that “all of the comparisons showed the LSI-R to predict as well orbetter than the other [assessment] instruments.”

According to the Canadian scholars, the LSI-R is central to the deliveryof effective offender treatment, whether in prison or in the community.Currently, correctional agencies often place offenders in rehabilitationprograms either collectively (i.e., everyone receives the same program) orbased on inaccurate clinical judgments about what might benefit anoffender. By contrast, a core principle of effective intervention is that“responsive” interventions should be directed at high-risk offenders. Thisis the case for two reasons. First, low-risk offenders—those who would “gostraight” on their own—either do not benefit from, or are made morecriminogenic by, interventions. Second, high-risk offenders not only pose

(7) a lack of prosocial leisure activities; and (8) abuse of drugs and alcohol. Althoughthese risk factors are all viewed as important, it is necessary to note that the first fouritems are referred to as the “Big Four” and are considered to be the most robust amongthe set (Andrews, Bonta, and Wormith, 2006).



the greatest threat to the community but also can potentially experiencesubstantial reductions in recidivism. Again, this means that a valid assess-ment of risk level is essential to an effective correctional intervention(Andrews and Bonta, 2006).

In this context, and given its empirical status, the LSI-R has emerged asperhaps the leading offender assessment instrument in corrections. Again,it gains added credibility because it is rooted in the empirical literature onpredictors of recidivism and is integrated with a prominent paradigm ofcorrectional treatment—which are the key principles of effective interven-tion (Cullen, 2002; see also Cullen and Gendreau, 2000; Gendreau et al.,2006; Ogloff and Davis, 2004). Across North America, the LSI-R is in usein more than 900 correctional agencies (Lowenkamp, Lovins, and Latessa,2009; see also Listwan, Johnson, Cullen, and Latessa, 2008).

It is within this context that a recent, salient critique of the instrumenttakes on special importance: It is claimed that the LSI-R has predictivevalidity for males but not for females (Holtfreter and Cupp, 2007; Reisig,Holtfreter, and Morash, 2006). For Andrews, Bonta, and colleagues, theLSI-R is based on the general principles of social learning theory and cog-nitive psychology. In turn, they believe that the predictors of crime andrecidivism are substantially the same for male and females; that is, theproximate causes of criminality are “general” rather than “gender spe-cific” (for supportive meta-analytic evidence, see Dowden and Andrews,1999; Gendreau et al., 1996; Hubbard and Pratt, 2002; Moffitt, Caspi, Rut-ter, and Silva, 2001; Simourd and Andrews, 1994). In this view, gender isimportant but to the extent that it shapes how and to what extent malesand females are exposed to and/or acquire common risk factors (e.g., anti-social peers).

These observations have three important implications. First, treatmentprograms should have similar effects for males and females. Inappropriateprograms (e.g., boot camps) will not be effective for either gender; by con-trast, programs that adhere to the principles of effective intervention willwork equally well for males and females. Evidence exists to support thisclaim (Andrews and Bonta, 2006). Second, gender may be important inthe way in which treatments are delivered; this approach is termed “spe-cific responsivity” (Andrews and Bonta, 2006). Thus, although cognitive-behavioral programs may be effective across genders, women may respondbetter if the treatment is delivered in a female-only group and where anemphasis is placed on creating “a community with a sense of connection”(Andrews and Bonta, 2006:465). And third, the LSI-R should predictrecidivism and be used as an instrument assessing offender change equallywell for males and females. Notably, Andrews and Bonta (2006:301–302)recently presented data that show similar predictive validity across gender


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for a recent version of the LSI-R on a sample of 561 Ontario probationersfollowed for 3 years after release from supervision. Vose (2008) reportssimilar results regarding the predictive validity of the LSI-R across genderfor probationers and parolees in Iowa.

The current critique of the LSI-R as being a male-specific assessmentinstrument is embedded within the larger criminological debate overwhether the causes of crime are general (as most prevailing theories ofcrime claim) or are gender specific (as feminist scholars argue) (see Dai-gle, Cullen, and Wright, 2007; Miller and Mullins, 2006; Salisbury, 2007).Again, empirical research has shown, now for some time, a similarity inthe causes of crime across gender (Simons, Miller, and Ainger, 1980; Smithand Paternoster, 1987). For example, based on their study in Dunedin,New Zealand, Moffitt et al. (2001:230) conclude that “the same risk factorspredict antisocial behavior in both males and females; we did not detectany replicable sex-specific risk factors for antisocial behavior” (see alsoFarrington and Painter, 2004). Gender-specific theorists disagree, arguingthat these more traditional approaches omit experiences—and thus riskfactors—unique to women. Thus, they point to distinct differences by gen-der in socialization, development, and economic marginalization as theprimary explanations for the gender differences in risk factors (Chesney-Lind, 1989; Daly, 1992, 1994; Reisig, Holtfreter, and Morash, 2002). Forexample, Covington and Bloom (1999:3) maintain that “the philosophy ofcriminogenic risk and needs does not consider factors such as economicmarginalization, the role of patriarchy, sexual victimization, or women’splace in society.” These unique life-course trajectories that lead to criminaloffending are referred to as “gendered pathways” (Daly, 1992, 1994; for asummary, see Miller and Mullins, 2006:228–232).

The construct of gendered pathways holds important implications foroffender assessment and, ultimately, for treatment. If females predomi-nantly enter crime for gender-specific reasons, then the LSI-R, whichassumes the generality of crime causation, would not be of much value inpredicting females’ recidivism or in directing treatment interventions withwomen offenders. This possibility was poignantly suggested by theresearch of Reisig et al. (2006). Based on a sample of 235 female offendersunder community supervision in Minnesota and Oregon, the authorsexamined the LSI-R’s ability to predict recidivism across those whoentered crimes for gender-neutral reasons (economic motivation) orgender-specific reasons (street women, harmed and harming womenwhose lives were filled with chaos and abuse/neglect, battered women, anddrug-connected women). Their analysis revealed that although the LSI-Rpredicted recidivism for the economically motivated offenders, it misclas-sified and had low predictive validity for offenders who entered crimethrough gendered pathways. The economically motivated group, however,



comprised only a quarter of the sample. The policy implications of thisfinding are clear. A “cause for concern” (Reisig et al., 2006:400) wasfound. The LSI-R risks endangering public safety and misallocating treat-ment resources by underclassifying some offenders (especially drug-connected females) and by overclassifying others. In short, the LSI-R maybe appropriate to assess male offenders but not female offenders.

Our project attempts to use a meta-analysis of extant studies to bringadditional evidence to bear on the ability of the LSI-R to predict recidi-vism for female offenders. This approach does not present a direct test ofthe gendered pathways model, because, with the exception of Reisig et al.(2006), current research does not divide offenders into pathway groups.Even so, critics of the LSI-R claim that because of its gender-specificnature, the ability of the LSI-R to predict recidivism for females should below and, in addition, should be far lower than for males (Holtfreter andCupp, 2007). Phrased alternatively, similar effects between the LSI-R andrecidivism across gender groups would increase confidence that the instru-ment can be used to assess not only males but also females.

To assess these issues, we undertook a meta-analysis that involved 25studies and 27 effect sizes representing data on 14,737 female offenders. In16 studies, we calculated the effect sizes for both males and females. Thesedata allowed us to conduct within-sample comparisons.

A meta-analysis involves the quantitative synthesis of research litera-tures, and it is now considered to be the review method of choice inseveral disciplines (Hunt, 1997), which include criminal justice. Further-more, this statistical technique has been applied in several previous studiesto evaluate the predictive validity of offender assessments (e.g., Campbell,French, and Gendreau, 2007; Gendreau et al., 2002). A meta-analysisexpresses the outcome of interest (e.g., offender recidivism) from studiesin a common metric, which is referred to as an effect size, and calculatesan average. Variations in the magnitude of effect sizes can then beexamined in relation to potential moderators. Many advantages are associ-ated with this statistical technique. First, it standardizes the review processand generates a quantitative estimate of the magnitude of an effect. Sec-ond, it can highlight discrepancies and/or systematic shortcomings in aresearch literature. Third, it provides an estimate of the certainty of aneffect through the use of fail-safe statistics. As a literature review tech-nique, however, meta-analysis is not foolproof. Researcher biases canaffect retrieval and coding decisions. In addition, meta-analytic reviewsare often limited by the relative inconsistency with which descriptive infor-mation is included in individual primary studies.


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Method

Sample

To collect studies and data relevant to the topic of the current investiga-tion, searches of literature databases were conducted for all studies on theLSI-R, which include the Social Sciences Citation Index, PsycINFO, andthe bulletins for the annual meetings of the Academy of Criminal JusticeSciences and the American Society of Criminology. Search terms included“Level of Service Inventory,” “Level of Service Inventory-Revised,”“LSI,” “LSI-R,” and “risk assessment.” To obtain additional unpublisheddata, we also contacted researchers who had previously published articleson the LSI-R as well as researchers employed by correctional agenciesthat use the LSI-R. To be included in the current investigation, the pri-mary study or unpublished data (1) had to include a prospective measureof recidivism and (2) were required to provide sufficient statistical infor-mation to allow for the calculation of the effect size statistic (Pearson’s ror point biserial coefficient) for female offenders only. For each primarystudy or data set, the longest follow-up period and the most serious formof recidivism were used to calculate the effect size.

Coding of Studies

Several characteristics of each study or data set were coded.2 Thesecharacteristics included publication information, sample demographics,follow-up information, recidivism measures, effect size, and sample size.

Effect Size Calculation

The Pearson’s r or (point biserial coefficient) was selected as the com-mon metric. The effect size was calculated directly from the publicationwhenever possible. If a sample contained both male and female offenders,a request was made to the authors to provide correlations by gender. In 10instances, the effect sizes for female offenders were calculated or obtainedfrom researchers from unpublished data/manuscripts. Because some sub-jective judgments were made in coding the studies, intercoder reliabilitywas established for the calculation of effect size statistic using a subsampleof the original studies.

2. Effect sizes were initially coded and analyzed by Christopher T. Lowenkampat the Center for Criminal Justice Research, University of Cincinnati. To establish inter-rater reliability, the published studies were then coded independently by the firstauthor.



As recommended in the 2001 APA publication manual (American Psy-chological Association, 2001), 95% confidence intervals (CIs) were alsocalculated to assess the precision and magnitude of the effects. The CIspecifies a range of values about the mean effect size that includes therespective population parameter for a specified percentage of the time(i.e., 95%). The utility of the CI lies in its interpretability: If the intervaldoes not contain 0, then it can be concluded that the mean effect size issignificantly different from 0 (i.e., better than chance alone). Similarly, ifno overlap occurs between the 95% CIs, then the two mean effect sizeswould be assessed as being statistically different from one another at the.05 level. In comparing the CIs of two different means, if no overlap occursor the CIs barely touch, then p < .01. If the overlap is about half the aver-age margin of error (i.e., the proportion overlap is about .50 or less), thenp < .05 (see Cumming and Finch, 2005; Gendreau and Smith, 2007).

Finally, the primary utility of the CI rests in this study on the interpreta-tion of its width. As the width of the CI increases, the precision of theestimate of m decreases (or is associated with more uncertainty) (Hunterand Schmidt, 2004). The judgment of the width of the CI—which wouldlead to a conclusion that additional replication of the results is necessarybefore suggesting policy or clinical guidelines—is subjective; it depends onthe internal width researchers in the various subdisciplines feel is relevant(Smithson, 2003). For a discussion of the different ways the CI can beinterpreted, see Cumming and Finch (2005).

Fail-Safe Estimation

One criticism of meta-analysis is that it is subject to publication bias.Although we conducted additional searches in an attempt to overcomethis limitation (i.e., locating unpublished data), other methods are availa-ble to estimate how important this possible bias might be to the stability ofthe findings generated. One such method is the calculation of a fail-safe Nstatistic. Rosenthal (1979) devised a fail-safe N formula that has been usedextensively in meta-analytic research. The formula is based on p-valuesassociated with the z-statistic. The fail-safe N yielded by Rosenthal’sformula is based on probability values and is not as interpretable as otherpotential measures. As such, we chose to use Orwin’s (1983) modificationof Rosenthal’s (1979) formula. Orwin’s fail-safe N provides the number ofstudies, which produces a particular mean effect size that is required toreduce the average effect size reported in the current analyses to a prede-termined (trivial) level.


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Results

Study Characteristics

A total of 25 published and unpublished data sets were identified andincluded in the current analyses. These 25 data sets yielded a total of 27distinct effect sizes of the relationship between the LSI-R and recidivismfor female offenders. These 27 effect sizes represent data on a total of14,737 female offenders. Table 1 contains a list of the studies and effectsizes (for both females and males) included in this meta-analysis.

Table 2 presents the descriptive statistics on the publication and meth-odological characteristics of the studies included in the sample of effectsizes for female offenders. Approximately half of the effect sizes (55%)were calculated from unpublished data/manuscripts/dissertations that hadbeen collected between 2000 and 2009 (81%). A total of 14 effect sizes(52%) were calculated based on samples with no predominant racialgroup, whereas 6 effect sizes (22%) were generated with samples thatwere predominantly white. Sixty-seven percent of the effect sizes weregenerated based on samples where the average age was between 30 and 35years.

In terms of outcome measures, most data sets tracked offenders formore than 2 years (32%), whereas a smaller percentage (30%) trackedoffenders for 13 to 24 months, and only 5 studies (19%) followed offend-ers for 12 months or less. Thirty percent of the studies used reincarcerationas the outcome measure, and 30% of the studies employed reconviction. Asmall percentage of studies used other measures of recidivism (e.g., self-report, rearrest, or community supervision violation).

Effect Sizes

Table 3 reports the mean effect sizes and associated 95% CIs for therelationship between the LSI-R and recidivism for female offenders. Theeffect sizes generated using fixed-effects and random-effects models arepresented. Note that the overall mean r values (fixed effects = .35; randomeffects = .34) and the Z+ values (fixed effects = .37; random effects = .35)are nearly identical in magnitude. The only notable differences are thewidths of the CIs. The value of the Q-statistic (Q = 273,916, degrees offreedom [d.f.] = 26, p = .000) indicates significant variation in the effectsizes across the studies. Although this finding indicates that potential mod-erator variables might explain this variation, we did not have access to



Table 1. Listing of study year, sample gender, effect size, and sample NAuthor Year Gender r N

Andrews 1984 Females .53 97Andrews 1984 Males .41 464Arnold 2007 Females .29 438Arnold 2007 Males .19 1,097Brews and Wormith 2007 Females .48 3,821Coulson, Ilacqua, Nutbrown, Giulekas, and Cudjoe 1996 Females .51 526Flores, Lowenkamp, Latessa, and Smith 2006 Females .31 556Flores, Lowenkamp, Latessa, and Smith 2006 Males .27 1,547Folsom and Atkinson 2007 Females .30 85Jones-Hubbard (Sample 1) 2002 Females .41 51Jones-Hubbard (Sample 2) 2002 Females .30 37Jones-Hubbard (Sample 3) 2002 Females .11 42Jones-Hubbard (Sample 3) 2002 Males .21 171Kirkpatrick 1999 Females .30 169Lowenkamp 2007 Females .43 25Lowenkamp 2007 Males .29 70Lowenkamp and Bechtel 2006 Females .25 521Lowenkamp and Bechtel 2006 Males .24 976Lowenkamp, Holsinger, and Latessa 2001 Females .37 125Lowenkamp, Holsinger, and Latessa 2001 Males .22 442Lowenkamp and Latessa 2002a Females .25 376Lowenkamp and Latessa 2002b Females .26 425Lowenkamp and Latessa 2002b Males .15 1,035Lowenkamp and Latessa 2005a Females .16 730Lowenkamp and Latessa 2005b Females .31 111Lowenkamp and Latessa 2005a Males .17 3,928Lowenkamp and Latessa 2005b Males .13 278Lowenkamp and Latessa 2006 Females .21 495Lowenkamp and Latessa 2006 Males .16 1,918Lowenkamp, Lovins, and Latessa 2009 Females .34 114Lowenkamp, Lovins, and Latessa 2009 Males .37 370McConnell 1996 Females .60 50Palmer and Hollin 2007 Females .53 96Raynor 2006 Females .34 163Raynor 2006 Males .36 785Raynor and Miles 2007 Females .30 210Raynor and Miles 2007 Males .29 1,170Reisig, Holtfreter, and Morash 2006 Females .16 134Rettinger 1998 Females .58 441WSIPP 2007 Females .28 4,822WSIPP 2007 Males .29 17,711Zajac 2007 Females .07 77

Note. WSIPP = Washington State Institute for Public Policy.

data on what we felt were theoretically important moderators (e.g., whoadministered the assessment, staff training, and other measures related toquality assurance that have been implicated in the use of dynamic riskassessments).


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Table 2. Descriptive statistics—Publication and methodologicalcharacteristics

Characteristic k %

Type of PublicationJournal 10 37Report 1 4Conference paper 1 4Thesis/dissertation 5 18Unpublished data/manuscript 10 37

Decade of Publication1980 1 41990 4 152000 22 81

Predominant Race (= 80%)White 6 22No predominant race 14 52Missing 7 26

Average Age25–29 years 2 730–35 years 18 67Missing 7 26

Follow-Up Time Period12 months or less 5 1913–24 months 18 3025+ months 14 32

Recidivism MeasuresProbation/parole violation 1 4Rearrest 6 22Reconviction 8 30Reincarceration 8 30Mixed 2 7Self-report 1 4Missing 1 4

Nevertheless, two moderators, length of follow-up and publication sta-tus, were related to outcome. First, shorter follow-ups (i.e., 12 months orless) were associated with larger effect sizes (r = .43, CI = .41 to .45) incomparison with longer time intervals (e.g., greater than 2 years, r = .28,CI = .26 to .30). This trend is not surprising given the fact that the LSI-Rcontains several dynamic items that are time sensitive. Second, publisheddata (including journals, book chapters, conference presentations, and the-ses/dissertations) produced considerably larger effect sizes (r = .44,CI = .42 to .46) than unpublished data (r = .27, CI = .25 to .29). This find-ing is consistent with previous research that has documented a potentialpublication bias (Rothstein, Sutton, and Borenstein, 2005). It should beemphasized, however, that an r value of .27 (in the case of unpublished



data) is considered to be both statistically and clinically significant. Finally,given the relatively small number of total effect sizes (k = 27) and missingdata in some cases, many moderators could not be tested with any mean-ingful interpretation. Future replications should extend this line ofresearch.

Table 3. Mean effect sizeModel k N r 95% CI Z+ 95% CI

Fixed effects 27 14,737 .35 .34 to .36 .37 .35 to .38Random effects 27 14,737 .34 .28 to .39 .35 .29 to .41

Orwin’s Fail-Safe N

To calculate Orwin’s fail-safe N, two parameters must be specified(Orwin, 1983). First, the mean effect size of the fugitive literature needs tobe specified. We set this value to r = .00. The second parameter that mustbe set is the value for clinical significance. We chose r = .15 for this valueas it is still slightly above a value associated with “chance” prediction andis also larger than average effect sizes generated based on studies ofunstructured clinical prediction. Based on these parameters and the cur-rent data, a total of 39 studies, which produce a mean effect size of .00,would be needed to reduce the mean effect size generated from the cur-rent data set to a mean effect size of .15. It should be noted, however, thata major advantage of meta-analysis is that it is replicable. Future meta-analyses can (and should) be conducted as additional effect sizes becomeavailable.

Within-Study Comparison of Effect Sizes

Some critics have commented that within-sample comparisons yieldmore sensitive and accurate assessments of the predictive validity formales versus females. We identified 16 data sets that allowed for within-sample comparisons by gender. Table 4 reports the mean effect sizes formales and females for these 16 within-study comparisons (fixed effects =.26 and .27 for males and females, respectively). Once again, the mean rvalues for the fixed-effects and random-effects models were nearly identi-cal. More important, the effect sizes for males and females were verysimilar, and in both cases, the 95% CIs overlapped. The Q statistics indi-cated significant variation in study effect sizes for both males (Q = 147.323,d.f. = 15, p = .000) and females (Q = 28.500, d.f. = 15, p = .019).


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Table 4. Comparison of mean effect sizes by genderModel k N r 95% CI Z+ 95% CI

Fixed EffectsMales 16 33,616 .26 .24 to .26 .26 .25 to .27Females 16 9,250 .27 .25 to .29 .28 .26 to. 30

Random EffectsMales 16 33,616 .24 .20 to 28 .25 .21 to .29Females 16 9,250 .28 .24 to .31 .28 .25 to .32

Table 5. Comparison of mean effect sizes across meta-analysesStudy k N r Z+ 95% CI

Gendreau, Little, and Goggin (1996) 28 4,579 .35 .33 .30 to .36Gendreau, Goggin, and Law (1997) 10 2,525 .23 .22 .18 to .27Gendreau, Goggin, and Smith (2002) 33 7,367 .37 .39 .36 to .41Current study 27 14,737 .35 .37 .34 to .37

Comparison of Results with Previous Meta-Analyses

The results of the current analysis are comparable with the results ofprevious research that compared the predictive validities of the LSI-R inmale versus female offenders. Table 5 presents the results of the currentstudy and the results of other meta-analyses that produced effect sizes onthe relationship between the LSI-R and recidivism.

Three previous meta-analyses that reported effect sizes for the LSI-Rwere identified in the existing research on risk prediction. These meta-analyses contained samples of males and females, although the samplesincluded in the primary studies were predominantly males, which is impor-tant to note. The prior meta-analyses provide a way to place the currentfindings in the context of what is already known about the average r valuesgenerated from research on the predictive validity of the LSI-R withpredominantly male samples.

First, Gendreau et al. (1996) investigated the average effect size of theLSI-R in predicting recidivism. The study included 28 effect sizes gener-ated from data on 4,579 individuals. Notably, the point estimates fromboth the current study and the Gendreau et al. (1996) study are similar inmagnitude, and the CIs overlap. This information suggests that the meaneffect sizes are in fact sampled from the same distribution. Second, Gen-dreau, Goggin, and Law (1997) examined the relationship between theLSI-R and prison misconducts. Although not a study of recidivism per se,the point estimates for this study and our analysis are somewhat similar,and the CIs almost overlap. Third, Gendreau et al. (2002) produced a



mean effect size for the LSI-R that was slightly higher than the effect sizereported in the current study. Again, however, the intervals generatedbased on the data used in the Gendreau et al. (2002) study overlap withthe CIs from the current investigation.

Conclusion

Policy Recommendation

It has been demonstrated empirically that actuarial assessments in vari-ous fields (e.g., medicine, mental health, criminal justice, and education)are far superior to clinical assessments in their ability to predict outcomesand do so well beyond chance levels (Bonta, Law, and Hanson, 1998;Grove, Zald, Lebow, Snitz, and Nelson, 2000; Hanson and Bussiere, 1996;see also Ayres, 2007). In this context, the LSI-R has emerged as a signifi-cant actuarial instrument for assessing offenders within the realm ofcorrections. As recent meta-analyses have confirmed, the LSI rivals, if notsurpasses, the predictive validity of other assessment instruments (Camp-bell et al., 2007; see also Andrews and Bonta, 2006; Gendreau et al., 2002).

The LSI-R’s somewhat hidden, or at least often overlooked, value isthat it promotes a progressive policy agenda. Unlike previous predictioninstruments that relied mainly on static, historical factors, the LSI-R mea-sures dynamic factors—criminogenic needs. This approach not onlyincreases the predictive power of the LSI-R but also directs attention tosources of offender recidivism that can be changed and thus are amenableto treatment. In this model, therefore, offenders are not portrayed assuper-predators who endanger the community but as clients whoseinvolvement in crime can be reduced through interventions that areresponsive to the factors that underlie recidivism. More generally, pro-grams that adhere to the principles of effective intervention have thecapacity to achieve meaningful reductions in recidivism for high-riskoffenders.

Evidence-based corrections, however, demand that treatment practices,including offender assessment, be subjected to rigorous scrutiny (Cullenand Gendreau, 2000; MacKenzie, 2006). The value of the critical analysisby Reisig et al. (2006) is that it called into question the wisdom of usingthe LSI-R to categorize female offenders by risk level and, ultimately, todecide who should receive treatment resources. The notion of genderedpathways has traction because of the growing literature that supports its


198 Smith et al.

existence (Miller and Mullins, 2006; Salisbury, 2007); it undermines confi-dence in the LSI-R because Andrews and colleagues’ approach toassessment is rooted in a general, social learning-cognitive theory ofhuman behavior.

Still, in the end, it is inadvisable to reject the LSI-R’s status as a valuableassessment instrument for female offenders based on a single study—how-ever persuasive it is. Rather, our approach, which was inspired by Reisig etal. (2006), was to conduct the most systematic investigation of the issues athand using meta-analysis to organize the extant knowledge. In this way,we propose that at this stage, the empirical status of the LSI-R should bebased not on a sample of 235 offenders but on the 14,737 female offendersrepresented in the studies that were meta-analyzed. Although the currentresearch does not represent a direct test of the gendered pathways per-spective, it should be noted that the variation in effect sizes for males wasgreater than for females. If the LSI-R only predicted recidivism for “eco-nomically motivated offenders” (who constituted just one quarter of thetotal sample of female offenders in the Reisig et al. study), then morevariation would have been expected for females, and this trend shouldhave attenuated the overall effect size.

More importantly, the results of the current investigation indicate thatthe relationship between the LSI-R and recidivism for females is statisti-cally and practically similar to that for males. Furthermore, the resultsfrom the current study, which focuses on female offenders, are similar tothe results that previous meta-analyses have generated when using mixedsamples or samples dominated by male offenders. Based on the extantdata, it seems that the LSI-R performs virtually the same for femaleoffenders as it does for male offenders.

This finding is of practical and policy significance. It means that at thisstage, correctional officials should be advised to use the LSI-R to assessnot only male but also female offenders. This is especially the case forthose agencies that currently have the LSI-R in place. The gender critiqueof the instrument cannot be dismissed fully, but the “best bet” for assess-ing female offenders remains the LSI-R. It also is instructive that littledispute surrounds the LSI-R’s value in assessing risks and needs for maleoffenders.

Gender, Assessment, and Correctional Treatment

Corrections is often a dismal social domain, which is largely hidden frompublic view. It has a history of subjecting offenders to painful conditionsand to well-intentioned but flawed rehabilitation programs that are besttermed as “quackery” (Latessa, Cullen, and Gendreau, 2002; MacKenzie,



2006). When correctional interventions fail, it feeds the notion that “noth-ing works” to change offenders—an idea that in turn lends credence tonew-penology thinking that offenders are merely human products in anoverflowing assembly line that need to be managed efficiently and withminimal risk to public safety. Phrased differently, for corrections to have ahigher social purpose (Allen, 1981), it must be shown that it can be a con-duit for the delivery of effective, humane interventions that reformoffenders and, in so doing, protect the community.

From an evidence-based perspective, the data we have presented sup-port the continued use in correctional agencies of the LSI-R as a meansfor assessing male and female offenders and, in turn, for delivering treat-ment services. This recommendation, however, is provisional. As Merton(1973) points out, a hallmark of science is “organized skepticism.” Knowl-edge grows not through blind acceptance but through continued empiricalinvestigation. Furthermore, and equally important, the LSI-R should notbe viewed in narrow, technical terms. Beyond effect sizes and predictivevalidity, this instrument exists within a broader scholarly context in whichan ongoing debate surrounds the role of gender in correctional treatment.We alluded to this debate previously, but we return to it now because itframes the choices researchers concerned with the treatment of femaleoffenders will face in the time ahead.

The Canadians scholars’ principles of effective intervention comprise apowerful correctional paradigm (Cullen, 2002). We use the term “para-digm” purposefully, because this approach integrates coherentlycriminological knowledge, prescriptions for the content of treatment, andthe technology for delivering interventions (see also Kuhn, 1962). Theirpsychology of criminal conduct is enmeshed initially within a view ofbehavior drawn from social learning, cognitive, and personality theories—all frameworks that are supported by thousands of studies. They hold thatthese fundamental sources of human behavior are general. In particular,they recognize action as preceded by thought (i.e., cognition or attitudes),prior learning and current supportive associations, and personality (i.e.,traits and temperament). Consistent with this theoretical approach, meta-analyses show that antisocial attitudes, antisocial peers, personality, andpast involvement are the strongest predictors of recidivism. These factorsare prominently featured in the LSI-R, which is an instrument used toassess offenders and shown—as in our study—to have predictive validity.Interventions that target these empirically established predictors forchange reveal large reductions in recidivism. Furthermore, in their Psy-chology of criminal conduct—which is now nearly 600 pages in its fourthedition—Andrews and Bonta (2006) compile in remarkable detail the evi-dence that supports this paradigm.


200 Smith et al.

MacKenzie’s (2006) systematic review in What works in correctionsoffers collateral support to the Canadians’ targeting proximate factors forintervention. Based on the extant evidence, she concludes that escapingfrom crime must first involve “a cognitive transformation . . .within theindividual” (2006:337). Programs that proved effective typically focused“on individual-level change”; in fact, such a change is “required before tiescan be formed with social institutions” (2006:337).

The Canadians do not completely ignore gender, but they do relegate itto a place of secondary importance. In their view, gender is a “specificresponsivity” factor, which means that how a treatment is delivered tomales and females should consider gender (e.g., whether a cognitive inter-vention should use structured learning tasks or require sensitivity on thepart of offenders) (Andrews and Bonta, 2006). The Canadians reject, how-ever, gender-specific assessment and treatment interventions. They arepersuaded that the proximate sources of criminal conduct are the same formen and women. “We have not found any evidence,” conclude Andrewsand Bonta (2006:467), “that the antisocial behavior of demographicallydefined groups is insensitive to personality, attitudes, associates, or behav-ioral history. Nor have we found that the impact of RNR [risk-needs-responsivity] adherence and breadth on future offending varies with age,race, or gender.” In their model, gender differences may occur in moredistal experiences, such as the degree of sexual victimization. But if so,such factors have their effects on criminal conduct by fostering crimi-nogenic personality traits, attitudes, associates, and behavioral history—which are sometimes called “the Big Four” (as we noted in footnote 1). “Ahistory of being victimized may well contribute to crime,” observeAndrews and Bonta (2006:467–468), “but, in terms of the researchreviewed in this text, it does so through the Big Four.”

The Canadians’ contentions can be criticized for being reductionist—forfocusing on the Big Four and ignoring the complexity of factors thatunderlie female criminality. This reasoning is problematic. The Canadiansare not offering a criminological theory but a treatment theory. Their goalis to identify those proximate, dynamic factors that, if changed, lowerrecidivism. Many facets of male and female criminality may differ dramati-cally, but effective treatment—regardless of gender—will focus on thedynamic risk factors (or criminogenic needs) shown by the empirical evi-dence to predict recidivism. This is why, in their view, the LSI-R shouldhave the content it does and should have similar predictive validity acrossgender.



The power of the Canadians’ paradigm is that it has been constructedover the course of 30 years and is evidence based. When policy makersand practitioners must decide how to allocate scarce resources to fundtreatment programs, it is risky to ignore the Canadians’ principles of effec-tive intervention and to implement programs that have little proven trackrecord of reducing recidivism. This is not to say, however, that the issue ofgender-specific offender rehabilitation is settled—far from it. Still, it doesmean that systematic efforts must be taken to create the empirical base formoving forward in this direction.

First, from the emerging literature on gendered pathways, it will beimportant to produce meta-analyses that can demonstrate that, acrossmany studies, gender-specific factors predict recidivism above and beyondthose identified by the Canadians. Individual studies with striking findingsmay be illuminating, but they are just one data point. It is the organizationof knowledge, especially its quantitative synthesis, which provides theempirical grounds to build a consensus that a given factor is a strong pre-dictor of recidivism. Second, to form the basis of a treatment theory, thesefactors will have to be shown to be dynamic and not static as well as ame-nable to change through practical treatment modalities (e.g., cognitive-behavioral programs and skill building). Third, assessment instrumentswill have to be developed that can measure these factors and that havemore predictive validity than the LSI-R. Fourth, carefully conductedexperimental and quasi-experimental evaluation studies will need to beundertaken that can show across a variety of correctional contexts thatthese gender-specific interventions reduce recidivism more than programsbased on the Canadians’ principles of effective intervention.

Notably, important steps are being made in this direction. In conjunc-tion with the National Institute of Justice, Patricia Van Voorhis andcolleagues have been validating a series of new risk/need assessments forfemale offenders (see Van Voorhis, Salisbury, Wright, and Bauman, 2008;Van Voorhis, Wright, Salisbury, and Bauman, 2009). These assessmentsinclude items to evaluate both “gender-neutral” and “gender-responsive”factors (i.e., trauma and abuse, unhealthy relationships, parental stress,depression, and self-efficacy). In fact, the Women’s Supplemental Risk/Needs Assessment is designed to supplement existing risk/needs assess-ments such as the LSI to expand the number of factors assessed (VanVoorhis et al., 2008). Based on a sample of 1,613 female offenders drawnfrom different correctional settings (i.e., prison, probation, and pre-release), the analysis revealed that gender-neutral factors have predictive


202 Smith et al.

validity. Even so, gender-specific factors improved the prediction of recidi-vism meaningfully. In our view, these findings support both the continueduse of the LSI-R in correctional agencies and the ongoing, expandedefforts to incorporate gender-specific predictors into this assessmentinstrument.

In the end, effective intervention with offenders is an empirical, not anideological, issue. Taking this evidence-based approach involves twoimperatives. First, at any given time, it is incumbent on criminologistsadvising policy makers and practitioners to know fully the empirical statusof competing treatment approaches. The approach with the strongestempirical support should be recommended as being the standard treat-ment in the field. Second, no treatment paradigm should be viewed assacrosanct. The research and the construction of more effective interven-tions, which include assessment instruments, should be ongoing. We owethis to the offenders we bring under our auspices and to members of thepublic whose victimization we may have the power to prevent.

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Paula Smith, Ph.D., is assistant professor of Criminal Justice and associate director ofthe Corrections Institute at the University of Cincinnati. Her research interests includeoffender classification and assessment, correctional rehabilitation, the psychologicaleffects of incarceration, program implementation and evaluation, the transfer of knowl-edge to practitioners and policy makers, and meta-analysis. She has authored severalarticles, book chapters, and conference presentations on the above topics. Recent publi-cations have appeared in Criminal Justice and Behavior, Criminology & Public Policy,Corrections Management Quarterly, and the International Journal of Offender Therapyand Comparative Criminology.


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Francis T. Cullen, Ph.D., is Distinguished Research Professor of Criminal Justice andSociology at the University of Cincinnati. His works include Reaffirming rehabilitation,Combating corporate crime, Corporate crime under attack, Rethinking crime and devi-ance theory, Taking stock: The status of criminological theory, Criminological theory:Context and consequences, and Criminological theory: Past to present—Essential read-ings. His current research focuses on the impact of social support on crime, themeasurement of sexual victimization, public opinion about crime control, and rehabili-tation as a correctional policy. He is a Past President of both the American Society ofCriminology and the Academy of Criminal Justice Sciences.

Edward J. Latessa, Ph.D., is professor and head of the Division of Criminal Justice atthe University of Cincinnati. He has published more than 75 works in the area of crimi-nal justice, corrections, and juvenile justice. He is coauthor of seven books, includingthe twelfth edition of Corrections in America and the fourth edition of Corrections inthe community. Latessa has served as President of the Academy of Criminal JusticeSciences (1989–1990). He also has received several awards, including the Simon DinitzCriminal Justice Research Award from the Ohio Department of Rehabilitation andCorrection (2002), the Margaret Mead Award for dedicated service to the causes ofsocial justice and humanitarian advancement by the International Community Correc-tions Association (2001), and the Peter P. Lejins Award for Research from theAmerican Correctional Association (1999).

Documents

Can 14,737 women be wrong? A meta-analysis of the LSI-R and recidivism for female offenders