Correctional Service Canada
Symbol of the Government of Canada

Common menu bar links

Research Reports

Warning This Web page has been archived on the Web.

Case Needs Review: Employment Domain

Paul Gendreau, Claire Goggin, Glenn Gray
Centre for Criminal Justice Studies
University of New Brunswick
Saint John, NB
June 2000


A narrative review and meta-analysis of the employment domain was conducted. Sixty-seven studies generated 200 effect sizes with recidivism. The employment domain produced a correlation with recidivism of r = .13. This modest result was in keeping with that of a previous meta-analysis wherein employment was subsumed within a social achievement domain (r = .15). An examination of the mean r values associated with the nine categories of the employment domain indicated that education/employment (r = .26), employment needs at discharge (r = .15), and employment history (r = .14) were among the most powerful predictor categories. Further, a literature search uncovered several measures that assessed the employment construct. Specific recommendations were made as to how to improve the Case Needs Identification Analysis measure employed by the Federal Correctional Service of Canada.




Of all of the predictors of offender recidivism, the employment/education domain (hereafter known as employment) is probably the most prosaic. Indeed, it has engendered little debate compared to other predictors such as social class of origin, personal distress and personality (e.g., psychopathy) (see Gendreau, Little, & Goggin, 1996; Gendreau, Goggin, & Paparozzi, 1996). It has been taken for granted that the employment domain is a moderately good predictor of recidivism. Meta-analyses of the juvenile offender literature have confirmed this (cf. Lipsey & Derzon, 1997; Loeber & Stouthamer-Loeber, 1987; Simourd & Andrews, 1994). In these reviews, however, it should be noted that the employment domain was made up almost entirely of educational achievement items. A meta-analysis of the general adult offender prediction literature has essentially corroborated the juvenile results (Gendreau, Little, & Goggin, 1996). Their meta-analysis had a social achievement domain in which a majority of the effect sizes were employment/education predictors. The social achievement domain ranked in the top third of predictors behind companions, criminal history, criminogenic need, and antisocial personality. Furthermore, surveys of adult male and female offenders have also revealed that employment/vocational/financial needs are pre-eminent (Motiuk, 1996). Additionally, Zamble (1993) found that financial gain was a primary motive for a quarter of his offender sample.

Almost all adult offender risk instruments include an employment item (cf. Gendreau, Little, et al., 1996). However, to our knowledge, only two risk measures, the Level of Service Inventory-Revised (LSI-R; Andrews & Bonta, 1995) and the Case Needs Identification and Analysis (CNIA) protocol (Motiuk, 1993; Motiuk & Brown, 1994) have explored the area in any depth. The LSI-R has 10 items in this regard, the CNIA has 35. Given that the Gendreau, Little, et al. (1996) meta-analysis did not examine separately the employment domain predictors and the fact that one of the major risk/need assessment protocols in corrections (the CNIA) is currently undergoing significant revisions, to that end, a reassessment of the predictive validity of the employment domain is timely. Thus, the purpose of the present study is as follows:

  1. To update the Gendreau, et al., (1996) meta-analysis vis-à-vis the education/employment items of that study’s social achievement domain.
  2. To review the psychological test literature for recent psychometric instruments that measure the employment construct.


Sample of Studies

A literature search for relevant studies published between January 1994 and December 1997 was conducted using the ancestry approach and library abstracting services. These studies were added to the existing database reported in the Gendreau, Little, et al., (1996) meta-analysis. As well, studies from two recent meta-analyses of the predictors of recidivism for mentally disordered and sexual offenders were added (Bonta, Law, & Hanson, 1998; Hanson & Bussière, in press). For a study to be included, the following criteria applied:

  1. Data on the offender was collected prior to the recording of the criterion measure. A minimum follow-up period of six months was required. If a study reported more than one follow-up period, data from the longest interval was used.
  2. Treatment studies that directly attempted to change offender attitudes or behaviour were not included.
  3. Recidivism had to be recorded when the offender was an adult (18 years or older).
  4. The criterion had to have a no-recidivism category. Studies that used "more" vs. "less" crime categorizations were not used. The criterion measures were arrest, conviction, incarceration, or probation/parole violation.
  5. Each study was also required to report statistical information that could be converted into a common metric or effect size (i.e., Pearson r).

Design and Procedure

Coding the Studies

For each study the following information was recorded:

  1. Study characteristics: published document and decade of publication.
  2. Study sample characteristics: age, gender, race, type of offender, intake risk level, and history of violent offence.
  3. Study methodology: sample size, type of outcome criterion, length of follow-up, extreme groups design, subject attrition, adequate description of subjects, assessment/reporting of multiple recidivism outcomes, and recidivism data assessed by raters blind to assessment of predictors.

An index of study quality was obtained by scoring responses to methodological quality items

(see METH1, METH2, METH10, METH11, and METH12 in Appendix).

Predictor Categories

The employment predictor domain was first divided into 7 categories, which were comprised of the following constituents:

  1. Employment history - frequently unemployed, ever fired, unstable work history.
  2. Employment needs at discharge - no employment plans after release, poor job motivation, employment need.
  3. Employment status at intake - unemployed at intake, not employed prior to incarceration.
  4. Financial - poor financial management, major financial problems, low income.
  5. Education/employment – LSI-R education/employment domain, academic/vocational.
  6. School achievement - fewer years of education, less than grade 12, poor school achievement.
  7. School maladjustment - ever suspended/expelled, school discipline problems.

Effect Size Calculation

The procedures for calculating effect sizes in predictor studies have been detailed elsewhere (Gendreau, Goggin, & Law, 1997; Gendreau, Little, et al., 1996). Briefly, Pearson product-moment correlation (r) coefficients were produced for all predictors in each study that reported a numerical relationship with a criterion. When statistics other than Pearson r were presented, their conversion to r was undertaken using the appropriate statistical formulae (Rosenthal, 1991). Where a p value of greater than .05 was the only reported statistic, an r of .0 was assigned.

Next, the obtained correlations were transformed using Fisher's table. Then, according to the procedures outlined by Hedges and Olkin (1985, p. 230-232), the statistic z±, representing the weighted estimation of Pearson r, was calculated for each predictor category by dividing the sum of the weighted zrs per predictor category by the sum of each predictor's sample size minus three across that category.

In order to determine the practical utility of various predictors relative to each other, the common language (CL) effect size indicator (McGraw & Wong, 1992) was also employed. The CL statistic converts an effect size into the probability that the value of a predictor-criterion relationship sampled at random from the distribution of one predictor category (e.g., education/employment) will be greater than that sampled from another distribution (e.g., offender SES). The CL statistic requires mean and standard deviation values for calculation; thus it is not applicable to the z± statistic which lacks variance.

Significance Testing.

To determine which of the predictor categories predicted criterion significantly different from zero, the mean z± values for each group were multiplied by the value of (N - 3k)½, where N = the number of subjects per predictor category and k = the number of predictors per category (Hedges & Olkin, 1985).

A one-way analysis of variance (ANOVA) and the Student Newman Keuls (SNK) test using Pearson r were also employed to assess differences in the relationship of moderator variables (i.e., length of follow-up, study characteristics, etc.) with outcome criteria. The CL statistic does not involve significance testing.


Study Characteristics

We identified 67 studies as suitable for the meta-analysis which generated 200 effect sizes. For those variables where at least 50% of the studies reported information on sample and study characteristics, the results were as follows: (a) 82% of effect sizes came from studies which assessed males only or mixed gender samples, (b) 76% of effect sizes were associated with adult or mixed adult/juvenile samples, (c) 69% of studies came from the 1980s or 1990s, (d) 62% of effect sizes were associated with subjects of mixed risk levels, (e) 16% of effect sizes were associated with offenders with a violent or sexual offence history, (f) 91% of effect sizes came from studies with a 1 year or greater follow-up period, (g) 75% of outcomes included conviction, incarceration, or a combination thereof, and (h) 82% of effect sizes were associated with non-violent recidivism.

Meta-Analysis: Predictive Validities

The sixty-seven studies generated 200 effect sizes or individual correlations between an employment or education predictor and a criterion (i.e., recidivism). There were seven predictor categories (see Table 1). The results in Table 1 are interpreted in the following manner. Reading from the left of row 1, the employment history category produced 34 effect sizes involving 23,415 offenders. The mean correlation (r) was .14 and the confidence interval (CI) about mean r ranged from .11 to .17. The weighted r (z+) for the same category was .18 and it’s CI ranged from .17 to .19. Each of the seven predictor categories predicted recidivism significantly greater than 0.

Table 1: Mean effect sizes for predictor domains: First categorization
Predictor (k) N M r CI M z+ CI
1. Employment history (34) 23,415 .14(.10) .11 to .17 .18* .17 to .19
2. Employment needs at discharge (16) 4,961 .15(.12) .09 to .21 .19* .16 to .22
3. Employment status at intake (28) 12,990 .11(.13) .06 to .16 .10* .08 to .12
4. Financial (27) 14,457 .13(.10) .09 to .17 .10* .08 to .12
5. Education/employment (20) 9,142 .26(.18) .18 to .34 .10* .08 to .12
6. School achievement (60) 37,245 .10(.10) .07 to .12 .10* .09 to .11
7. School maladjustment (15) 11,822 .14(.08) .10 to .19 .11* .09 to .13
Total (200) 114,032 .13(.12) .12 to .15 .12* .11 to .13

Note. k = effect sizes per predictor domain; N = subjects per predictor domain; M r = mean Pearson r (SD); M z+ = [(zr)x(n – 3)] ÷ (n – 3)½] where n = number of subjects per effect size; CI = confidence interval about the mean Pearson r and mean z+.

* p < .05.

When examining mean r, the CIs for the education/employment predictor category (5) did not overlap with those of predictor categories 6 or 8, and overlapped only minimally with those of categories 1, 3, 4, and 6. In the case of weighted r (z+), the employment needs at discharge predictor category did not overlap with predictor categories 3 to 5 and 6 to 7. The drop in value from a mean r of .26 to a mean z+ of .10 for the education/employment category reflects the fact that three effect sizes within that group had large sample sizes and produced weak correlations with the criterion ( r < .12).

As outlined in Table 2, the common language effect size indicator (CL) demonstrated that the education/employment predictor category produced higher correlations with the criterion than did it’s counterparts, ranging from 70% of the time compared with employment needs at discharge to 81% of the time compared with offender SES. Employment needs at discharge produced higher correlations with criterion than did seven other predictor categories 52% to 63% of the time. Of the two school-based predictors, school maladjustment outperformed school achievement 62% of the time.

Table 2: Common language effect size indicatorsa
EE 66 73 71 73 74 78 81 83
EN   56 55 57 59 64 58 68
SM     51 52 54 61 61 63
EH       52 55 60 62 64
F         53 58 59 61
ES           55 56 58
PP             50 52
SA               52
a Common language effect size indicators for mean r values. Predictor domains are listed on the left in rank order of number of favourable comparisons.
EE = education/employment; EN = employment needs at discharge;
SM = school maladjustment; EH = employment history; F = financial;
ES = employment status at intake; PP = probation/parole schooling/training;
SA = school achievement; O = offender SES.

The predictors listed in Table 1 were then collapsed into three categories: education, employment, and education/employment combined. The results are described in Table 3.

Table 3: Mean effect sizes for predictor domains: Second categorization
Predictor (k) N M r CI M z+ CI
Education (75) 49,067 .11(.10) .08 to .13 .11* .10 to .11
Employment (105) 55,823 .13(.11) .11 to .15 .14* .14 to .16
Education/employment (20) 9,142 .26(.18) .18 to .34 .10* .08 to .12
Total (200) 114,032 .13(.12) .12 to .15 .12* .12 to .13

*p < .05.

Note. k = effect sizes per predictor domain; N = subjects per predictor domain;
M r = mean Pearson r (SD); M z+ = [(zr)x(n – 3)] ÷ (n – 3)½] where n = number of subjects per effect size;
CI = confidence interval about the mean Pearson r and mean z+.

For mean r, the CIs for the education/employment category do not overlap with the other two groups. Using weighted mean r values (z+), however, the employment category CIs do not overlap with the education or combined education/employment categories.

The CL index indicated that the education/employment predictor category produced higher correlations with the criterion than employment and education 74% and 79% of the time, respectively.

Meta-Analysis: Moderators

An analysis of the relationship between mean effect size per predictor category (k = 9) by study moderators was also conducted, resulting in few meaningful comparisons. For example, mean effect sizes did not differ by any of the study descriptors (i.e., journal, report, or book, published or not, study decade) or offender demographic characteristics (i.e., age, race, or gender). For all comparisons, F < 1.

With regard to study characteristics, the use of high, low, or mixed risk samples resulted in no difference in mean effect size [F (2, 190) £ 1]. Given the limited number of effect sizes associated with designated offender populations (i.e., sex offenders (k = 30) versus mentally disordered offenders (k = 16) versus all others (k = 168)), no comparison of average effect size was attempted. Skewing of the distribution of effect sizes associated with offenders with a history of violence (k = 34) versus those without (k = 167) also prevented further analysis.

Several methodological variables, including a composite index of quality, were also examined. None showed a significant relationship with effect size, with one exception. That is, effect sizes associated with an adequate description of subjects (i.e., details on age, race, and gender) were significantly lower than those generated by studies where demographic data was not provided [F(1, 206) = 7.63, p<.05].

In addition, effect sizes generated by studies that used a follow-up period of less than or equal to 2 years (r = .15) or greater than 5 years (r = .15) were significantly higher than those from studies with an “in-between” length of follow-up (r = .10) [F(2,206) = 4.28, p<.05]. Similarly, average effect sizes associated with probation/parole violation (r = .19) or incarceration (r = .19) were significantly greater than those of all other types of outcome criteria [F(4,189) = 5.63, p<.05].

Assessment Protocols

In addition to the LSI-R and the CNIA, nine potentially useful “employment” assessment protocols were identified. They are the Australia Work Ethic scale ( Ho & Lloyd, 1984), the Awareness of Limited Opportunity (Landis, Dinitz, & Reckless, 1963), the Employment Checklist (Gillis, 1998), the Intrinsic Job Motivation scale (Warr, Cook, & Wall, 1979), the Maladaptive Behavior Record (Witherspoon, Jenkins, deValera, & Sanford, 1975), the Occupational Self Efficacy Scale (Fletcher, Hansson, & Bailey, 1992), the Value of Employment (Andrews, Wormith, & Kiessling, 1985), the Work Beliefs scale (Haller & Miller, 1963), and the Work Involvement scale (Kanungo, 1982).


This meta-analysis confirmed the utility of the employment predictor domain. The mean effect sizes for both the unweighted and weighted r values (.13 and .12 respectively) were almost identical to the social achievement predictor domain results reported in Gendreau, Little, et al. (1996). In that study, 67% of the social achievement effect sizes (k = 112) were in the employment domain which, in turn, produced a mean r of and z+ values of .15 and .13, respectively. Given that the present database consists of 200 effect sizes and 114,032 offenders, the employment predictor domain is solidly established as a moderately strong predictor of recidivism.

In fact, further research may establish that the results reported here have underestimated the predictive potential of the employment domain. Historically, the standard approach to enquiring about employment type questions in offender risk measures has been to limit questions to basic grade achieved/employment history items. Rather, more attention should be focused on assessing the offenders’ values, beliefs, satisfactions, etc. with employment and related skill acquisition. In effect, we are advocating that this domain be considered in a much more dynamic fashion similar to what has been argued for the conceptualization of IQ with offenders (Cullen, Gendreau, Jarjoura & Wright, 1997). In support of this view, inspection of our database revealed that these few items that assessed “non-rewarding work”, “poor job motivation”, etc. sometimes produced rs greater than .20. Indeed, in one large scale follow-up of offenders, a measure of work beliefs (Haller & Miller, 1963), compared to a wide range of predictor domains, generated the strongest correlations with recidivism (Gendreau, Grant, Leipciger, & Collins, 1979).

Finally, it should be noted that the present database contained very few studies on female and native samples. Our review of the studies on females indicated some inconsistencies. For example, in one study, the employment domain was a significant predictor of recidivism, with results similar to that of males (Ilacqua, Coulson, Giulekas, & Nutbrown, 1995). On the other hand, while Lambert and Madden (1976) reported sizeable correlations of employment with recidivism, Bonta et al. (1995) did not. There were two studies on natives (Bonta, 1989; Bonta, Pang, & Wallace-Capretta, 1997). For whatever reason, the mean r value obtained for non- natives was higher than for natives (r = .26 vs .18). Obviously, much more research is needed regarding gender and race.

Recommendations: CNIA

The employment domain of the CNIA consists of 6 principle components and 10 subcomponents. The database in this meta-analysis substantiates the continued use of the first three indicators in the education/skills sub-component, five of the indicators in the history sub-component, as well as all of the indicators in the dismissed/departure, economic gain, and the history (from the interventions principal component) sub-components. Unfortunately, this meta-analysis did not contain effect sizes that addressed the content of the other CNIA employment indicators.

Our recommendations regarding possible revisions of the employment domain of the CNIA reflect, in part, clinical wisdom as well as the meta-analysis. They are:

  1. Continue to use the above-noted indicators, although some judicious pairing (e.g., choose one of “less than grade 8" or “less than grade 10", etc.) would be helpful. Also, review the necessity of including 35 indicators in the employment domain. The LSI-R employment/education section contains 10 items, although we are certainly not suggesting a draconian reduction in items to the CNIA employment domain.
  2. Add an item or two on school maladjustment factors.
  3. There is a wealth of useful items in some of the “employment” assessment protocols located in our review. Serious consideration should be given to adapting several items from the following scales: Australia Work Ethic, Intrinsic Job Motivation, Occupational Self Efficacy, Work Beliefs, and Work Involvement. All but the Work Beliefs scale can be obtained from C. Gillis, Department of Psychology, Carleton University.
  4. The final recommendation is controversial. The logic, albeit tenuous, is as follows. The employment domain is a useful predictor of recidivism. Good employment skills are necessary for a successful pro-social re-integration into society. A huge amount of research has shown that the best predictor of job success, by far, is the General Aptitude Test Battery (Nelson Canada, 1987) (Hunter & Hunter, 1984). We well realize measures such as this (or approximately equivalent Canadian tests like the Canadian Adult Achievement Test (Harcourt Brace Janovich, 1986) are time consuming. Nevertheless, our opinion is that these measures would provide information that would assist the case management process considerably re: offenders’ rehabilitation. For readers interested in other assessment approaches in this area please consult Sternberg and Grigorenko (1997).


References marked with an asterisk indicate studies included in the meta-analysis.

Andrews, D. A., & Bonta, J. (1995). LSI-R: The level of service inventory- revised. Toronto, Ont.: Multi-Health Systems, Inc.

*Andrews, D. A., Kiessling, J. J., Mikus, S., & Robinson, D. (1986). The construct validity of interview-based risk assessment in corrections. Canadian Journal of Behavioural Science, 18, 460-470.

*Andrews, D. A., & Robinson, D. (1984). The level of supervision inventory: second report. Research Services and the Community Division of the Ontario Ministry of Correctional Services.

Andrews, D. A., Wormith, J. S., & Kiessling, J. J. (1985). Self-reported propensity and criminal behaviour: Threats to the validity of the assessments of personality (Programs User Branch User Report). Ottawa, Ontario: Solicitor General Canada.

*Angel, Green, Kaufman, & Loon, (1971). Prevention detention: An empirical analysis.

Harvard Civil Rights - Civil Liberties Law Review, 6, 300-396.

*Babst, D. V., Inciardi, J. A., & Jaman, D. R. (1971). The uses of configural analysis in parole prediction research. Canadian Journal of Criminology, 13, 200-208.

*Barbaree, H. E., & Marshall, W. L. (1988). Deviant sexual arousal, offense history, and demographic variables as predictors of reoffense among child molesters. Behavioral Sciences and the Law, 6, 267-280.

*Bencivengo, E. P., Meyer, L. C., Romero, J., & Wright, L. S. (1980). A ten-year follow-up of sex offender recidivism (Report). Philadelphia, PA: Joseph J. Peters Institute.

*Blum, F. J., & Chagnon, M. (1967). Extraversion and subsequent recidivism for a selected group of young adult offenders. Canadian Journal of Criminology, 9, 94-98.

*Bonta, J. (1981). Prediction of success in community resource centres. Paper presented at the 34th Annual Convention of the Ontario Psychological Association, Toronto, Ontario.

*Bonta, J. (1989). Native inmates: Institutional response, risk, and needs. Canadian Journal of

Criminology, 31, 49-61.

*Bonta, J., & Higginbottom, S. (1991). Parole risk prediction: a pilot project. Poster presented at the 121st Congress of Correction of the American Corrections Association, Minneapolis, MN.

*Bonta, J., LaPrairie, C., & Wallace-Capretta, S. (1997). Risk prediction and re-offending: Aboriginal and non-aboriginal offenders. Canadian Journal of Criminology, 39, 127-144.

Bonta, J., Law, M., & Hanson, K. (1998). The prediction of criminal and violent recidivism among mentally disordered offenders: A meta-analysis. Psychological Bulletin, 123, 123-142.

*Bonta, J., & Motiuk, L. L. (1985). Utilization of an interview- based classification instrument: A study of correctional halfway houses. Criminal Justice and Behavior, 12, 333-352.

*Bonta, J., & Motiuk, L. L. (1982). Assessing incarcerated offenders for halfway houses. Unpublished manuscript, Ottawa-Carleton Detention Centre, Ottawa, Ontario.

Bonta, J., Pang, B., & Wallace-Capretta, S. (1995). Predictors of recidivism among incarcerated female offenders. The Prison Journal, 75, 277-294.

*Bonta, J., Parkinson, R., Pang, B., & Barkwell, L. (1993). Toward a revised Manitoba Classification system. Unpublished manuscript.

*Broadhurst, R. G., & Maller, R. A. (1991). Sex offending and recidivism (Report No.3). Nedlands, Western Australia: The University of Western Australia, Crime Research Center.

*Buikhuisen, W., & Meijs, B. W. G. P. (1983). A psychosocial approach to recidivism. In K. T. Van Dusen & S. A. Mednick (Eds.), Prospective studies of crime and delinquency (pp. 99-113). Boston, MA: Kluwer-Nijhoff.

*Carlson, K. A. (1973). Some characteristics of recidivists in an Ontario institution for adult male first incarcerates. Canadian Journal of Criminology and Corrections, 15, 397-409.

*Cloninger, C. R., & Guze, S. B. (1973). Psychiatric disorders and criminal recidivism.

Archives of General Psychiatry, 29, 266-269.

Cullen, F. T., Gendreau, P., Jarjoura, G. Roger, & Wright, J. P. (1997). Crime and the bell curve: Lessons from intelligent criminology. Crime and Delinquency, 43, 387-411.

*Epperson, D. L., Kaul, J. D., & Huot, S. J. (1995, October). Predicting risk of recidivism for incarcerated sex offenders: Updated development on the sex offender screening tool (SOST). Paper presented at the Fourteenth Annual Research and Treatment Conference of the Association for the Treatment of Sexual Abusers, New Orleans, LA.

*Farrington, D. P. (1989). Long-term prediction of offending and other life outcomes. In H. Wegner, L. Losel, & J. Hisel (Eds.), Criminal behavior and the justice system (pp.26-39). New York: Springer Verlag.

*Federoff, J. P., Wisner-Carlson, R., Dean, S., & Berlin, F. S. (1992). Medroxy-progesterone acetate in the treatment of paraphilic sexual disorders. Journal of Offender Rehabilitation, 18, 109-123.

*Fitch, J. H. (1962). Men convicted of sexual offences against children. British Journal of

Criminology, 3, 18-37.

Fletcher, W. L., Hansson, R. O., & Bailey, L. (1992). Assessing occupational self-efficacy among middle-aged and older adults. Journal of Applied Gerontology, 11, 489-501.

*Frisbie, L. V., & Dondis, E. H. (1965). Recidivism among treated sex offenders (Research Monograph No. 5). State of California: Department of Mental Hygiene.

Gendreau, P., Goggin, C., & Law, M. (1997). Predicting prison misconducts. Criminal Justice and Behavior, 24, 414-431.

Gendreau, P., Goggin, C., & Paparozzi, M. (1996). Principles of effective assessment for community corrections. Federal Probation, 60, 64-70.

Gendreau, P., Grant, B. A., Leipciger, M., & Collins, S. (1979). Norms and recidivism rates for the MMPI and selected experimental scales on Canadian delinquent sample. Canadian Journal of

Behavioural Science, 11, 21-31.

Gendreau, P., Little, T., & Goggin, C. (1996). A meta-analysis of the predictors of adult offender recidivism: What works! Criminology, 34, 575-607.

*Gendreau, P., Madden, P., & Leipciger, M. (1979). Norms and recidivism for first incarcerates: Implications for programming. Canadian Journal of Criminology, 21, 416-441.

Gillis, C. (1998). The prediction of employment status in a sample of federal offenders on conditional release. Manuscript in preparation.

*Gottfredson, D. M., Wilkins, L. T., & Hoffman, P. B. (1978). Guidelines for parole and sentencing: A policy control method. Lexington, MA: DC Heath.

*Grygier, T., Blum, F., & Porebski, R. (1971). Decision and outcome: studies in parole prediction. Canadian Journal of Criminology and Corrections, 13, 133-146.

*Guin, C. C. (1991). Juvenile to adult criminality. Unpublished manuscript, University of Texas at Arlington.

Haller, A. O., & Miller, I. W. (1963). The occupational aspiration scale: theory structure and correlates (Technical Bulletin 288). East Lansing, MI: Michigan State University Agricultural Experimental Station.

Hanson, R. K., & Bussière, M. T. (in press). Predicting relapse: A meta-analysis of sexual offender recidivism studies. Journal of Consulting and Clinical Psychology.

*Hanson, R. K., Steffy, R. A., & Gauthier, R. (1992). Long-term follow-up of child molesters: Risk predictors and treatment outcome (Report No. 1992-02). Ottawa, Ontario: Ministry of the Solicitor General.

Harcourt Brace Janovich. (1986). CAAT: Canadian adult achievement test. Toronto: Author.

*Harris, G. T., Rice, M. E., & Quinsey, V. L. (1993). Violent recidivism of mentally disordered offenders: The development of a statistical prediction instrument. Criminal Justice and Behavior, 20, 315-335.

*Hassin, Y. (1986). Two models for predicting recidivism. British Journal of Criminology, 26, 270-286.

Hedges, L. V., & Olkin, I. (1985). Statistical methods for meta-analysis. San Diego, CA: Academic Press.

Ho, R., & Lloyd, J. (1984). Development of an Australian work ethic scale. Australian

Psychologist, 19, 321-332.

*Hodgins, S. (1983). A follow-up study of persons found incompetent to stand trial and/or not guilty by reason of insanity in Quebec. International Journal of Law and Psychiatry, 6, 399-411.

*Hodgins, S. (1987). Men found unfit to stand trial and/or not guilty by reason of insanity: Recidivism. Canadian Journal of Criminology, 29, 51-70.

*Hundleby, J. D., Scapinello, K. F., & Stasiak, G. A. (1992). Thirteen to thirty: A follow-up study of young training school boys (Report No. 1992-01). Ottawa, Ontario: Ministry of the Solicitor General.

Hunter, J. E., & Hunter, R. F. (1984). Validity and utility of alternative predictors of job performance. Psychological Bulletin, 96, 72-98.

Ilacqua, G. E., Coulson, G. E., Giulekas, D., & Nutbrown, V. (1995). Bonnie & Clyde: A comparison of criminogenic risk, experiences and recidivism of incarcerated male and female young offenders matched with the YO-LSI (Report). Brampton, Ontario: Vanier Centre for Women.

Kanungo, R. N. (1982). Measurement of job and work involvement. Journal of Applied

Psychology, 67, 341-349.

*Klassen, D., & O’Connor, W. A. (1989). Assessing the risk of violence in released mental patients: A cross-validation study. Psychological Assessment: A Journal of Consulting and Clinical

Psychology, 1, 75-81.

*Klein, S. P., & Caggiano, M. N. (1986). The prevalence, predictability, and policy implications of recidivism (Report R-3413-BJS). Washington, DC: Bureau of Justice Statistics, U.S. Department of Justice.

*Knight, B. J, Osborn, S. G., & West, D. J. (1977). Early marriage and criminal tendency in males. British Journal of Criminology, 17, 348-360.

*Lambert, L. R., & Madden, P. G. (1976). The adult female offender: The road from institution to community life. Canadian Journal of Criminology and Corrections, 18, 3-15.

Landis, J. R., & Dinitz, S, & Reckless, W. (1963). Implementing 2 theories of delinquency: Value orientation and awareness of limited opportunity. Sociology and Social Research, 47, 408-416.

*LeBlanc, M., Vallières, É., & McDuff, P. (1993). The prediction of males’ adolescent and adult offending from school experience. Canadian Journal of Criminology, 10, 459-478.

Lipsey, M. S., & Derzon, J. H. (1997, March). Predictors of violent or serious delinquency in adolescence and early adulthood: A synthesis of longitudinal research. Paper prepared for the OJJDP Study Group on Serious and Violent Juvenile Offenders.

Loeber, R., & Stouthamer-Loeber, M. (1987). Prediction. In H. C. Quay (Ed.), Handbook of juvenile delinquency (pp. 325-382). New York: Wiley.

*MacNaughton-Smith, P. (1976). Permission to be slightly free. Ottawa, Ontario: Law Reform Commission of Canada.

*Maletsky, B. M. (1993). Factors associated with success and failure in the behavioral and cognitive treatment of sexual offenders. Annals of Sex Research, 6, 241-258.

*McGarvey, B., Gabrielli, W. F., Jr., Bentler, P. M., & Mednick, S. A. (1981). Rearing social class, education, and criminality: A multiple indicator model. Journal of Abnormal Psychology, 90, 354-364.

McGraw, K. O., & Wong, S. P. (1992). A common language effect size. Psychological

Bulletin, 111, 361-365.

*Minor, K. I., & Hartmann, D. J. (1992). An evaluation of the Kalamazoo probation enhancement program. Federal Probation, 56, 30-35.

*Morrow, & Peterson (1966). Follow-up of discharged psychiatric offenders: “Not guilty by reason of insanity” and “criminal sexual psychopaths”. Journal of Criminal Law, Criminology and Police

Services, 57, 31-34.

*Motiuk, L. L. (1991). Antecedents and consequences of prison adjustment: A systematic assessment and reassessment approach. Unpublished doctoral dissertation, Carleton University, Ottawa, Ontario.

Motiuk, L. L. (1993). Where are we in our ability to assess risk? Forum on Corrections

Research, 5, 14-18.

Motiuk, L. (1996, September). Assessment methods in corrections. Paper presented at the meeting of the International Community Corrections Association, Austin, TX.

Motiuk, L. L. (1997). Classification for correctional programming: The Offender Intake Assessment (OIL) process. Forum on Corrections Research, 9, 18-22.

*Motiuk, L. L., Bonta, J., & Andrews, D. A. (1990). Dynamic predictive criterion validity in offender assessment. Paper presented at the Canadian Psychological Association Annual Convention, Ottawa, Ontario.

*Motiuk, L. L., & Brown, S. L. (1993). The validity of offender needs identification and analysis in community correction (Report No. R-34). Ottawa, Ontario: Research and Statistics Branch, Correctional Service of Canada.

*Motiuk, L. L., & Porporino, F. J. (1988). Offender risk/needs assessment: A study of conditional releases (Report No. R-01). Ottawa, Ontario: Correctional Service of Canada.

*Motiuk, L. L., & Porporino, F. J. (1989). Field test of the community risk/needs management scale: A study of offenders on case load (Report No. R-06). Ottawa, Ontario: Research Branch, Communications and Corporate Development, Solicitor General Canada/Correctional Service Canada.

Nelson Canada. (1986). General aptitude test battery (1986 edition.). Scarborough, Ontario: Author.

*Pallone, N. J., & Hennessey, J. J. (1977). Empirical derivation of a scale for recidivism proneness among parolees: A multivariate model. Offender Rehabilitation, 2, 95-110.

*Pallone, N. J., & Hennessey, J. J. (1977). Some correlates of recidivism among misdemeanants and minor felons: A 12-month follow-up. Journal of Social Psychology, 101, 321-322.

*Petersilia, J., & Turner, S. (1987). Guideline-based justice: Prediction and racial minorities. In

*D. M. Gottfredson & M. Tonry (Eds.), Prediction and classification: Criminal justice decision making(pp. 151-177). Chicago, IL: University of Chicago Press.

*Platt, J. J., & Labate, C. (1976). Recidivism in youth heroin offenders and characteristics of parole behaviour and environment. International Journal of Addictions, 11, 651-657.

*Polk, K. (1975). Schools and the delinquency experience. Criminal Justice and Behavior, 2, 315-338.

*Quinsey, V. L., Rice, M. E., & Harris, G. T. (1995). Actuarial prediction of sexual recidivism.

Journal of Interpersonal Violence, 10, 85-105.

*Quinsey, V. L., Warneford, A., Pruesse, M., & Link, N. (1975). Released Oakridge patients: A follow-up study of review board discharges. British Journal of Criminology, 15, 264-270.

*Reddon, J. R., Struder, L., & Estrada, L. (1995). [Recidivism data from the Phoenix Program for sex offender treatment]. Unpublished raw data.

*Roff, J. D., & Wirt, R. D. (1984). Childhood aggression and social adjustments as antecedents of delinquency. Journal of Abnormal Child Psychology, 12, 111-126.

*Rogers, S. (1981). Factors related to recidivism among adult probationers in Ontario. Toronto, Ontario: Ontario Ministry of Correctional Services.

Rosenthal, R. (1991). Meta-analytic procedures for social research. Newbury Park, CA: Sage.

*Roundtree, G. A., Edwards, D. W., & Parker, J. B. (1984). A study of the personal characteristics of probationers as related to recidivism. Journal of Offender Counselling, Services, and

Rehabilitation, 8, 53-61.

*Russo, G. (1994). Follow-up of 91 mentally ill criminals discharged from the maximum security hospital in Barcelona P.G. International Journal of Law and Psychiatry, 17, 279-301.

Simourd, L., & Andrews, D. A. (1994). Correlates of delinquency: A look at gender differences. Forum on Corrections Research, 6, 26-31.

Sternberg, R. J., & Grigorenko, E. L. (1997). Are cognitive styles still in style? American

Psychologist, 52, 700-712.

*Thornberry, T. P., & Jacoby, J. E. (1979). The criminally insane: A community follow-up of mentally ill offenders. Chicago, IL: University of Chicago Press.

*Thornton, D. (1995). [A 16-year follow-up of 244 sexual offenders from HM Prison Service]. Unpublished raw data.

*Villeneuve, D. B. (1993). Predictors of general and violent recidivism among mentally disordered federal incarcerates. Unpublished doctoral dissertation, Queen’s University, Kingston, Ontario.

*Wadsworth, M. E. J. (1979). Roots of delinquency: infancy, adolescence and crime. Oxford: Robertson.

Warr, P. B., Cook, J., & Wall, T. D. (1979). Scales for the measurement of some work attitudes and aspects of psychological well-being. Journal of Occupational Psychology, 52, 129-148.

*Wilbanks, W. L. (1985). Predicting failure on parole. In D. P. Farrington & R. Tarling (Eds.), Prediction in criminology (pp. 78-94). Albany, NY: State University of New York Press.

*Wilson, P. R. (1994). Recidivism and vocational education. Journal of Correctional

Education, 45, 158-163.

Witherspoon, A. D., Jenkins, W. O., deValera, E. K., & Sanford, W. L. (1975). A manual for the use of maladaptive behavior record in corrections. Montgomery, AL: Rehabilitation Research Foundation.

*Wormith, J. S., & Goldstone, C. S. (1984). The clinical and statistical prediction of recidivism. Criminal Justice and Behavior, 11, 3-34.

Zamble, E. (1993). Expanding the recidivism inquiry: A look at dynamic factors. Forum on

Corrections Research, 5, 27-30.

*Zamble, E., & Porporino, F. (1990). Coping, imprisonment, and rehabilitation: Some data and their implications. Criminal Justice and Behavior, 17, 53-70.


Coding Guide
RECORD Reprint record number
AUTHOR Study author
YEAR Study/report year
DECADE Study/report decade
JOURNAL Journal/book/report/etc.
  Value Labels
  1 Journal
  2 Report
  3 Unpublished report
  4 Book
CODE2 Coder’s identity
  Value Labels
  1 Glenn Gray
  2 Paul Gendreau
  3 Claire Goggin
STUDY1 Published
  Value Label
  1 yes (edited journal, edited book, or book)
  2 no
  3 unable to determine
  9 missing
SAMP1 Age of sample at time of initial assessment
NOTE: the predictor may be measured when the sample was less than 18 years of age, but the data must be measured when the sample was 18 years of older.
  Value Label
  1 juvenile (80% of sample less than 18 years)
  2 adult (80% of sample greater than 18 years)
  3 mixed age group
  9 missing
SAMP2 Gender of sample
  Value Label
  1 male (>90%)
  2 female (>90%)
  3 mixed
  9 missing
SAMP3 Race of sample
  Value Label
  1 White (>90%)
  2 Black (>90%)
  3 Aboriginal (>90%)
  4 Other
  5 Undifferentiated
  9 Missing
SAMP6 Risk level of sample
NOTE: to be “author-defined”, the author must report the use of an actuarial risk-assessment protocol in defining the risk level of the sample. This will over-ride the risk level determined by the Centre for Criminal
Justice Studies research team.
  Value Label
  1 Author-defined: low risk
  2 Author-defined: high risk
  3 Author-defined: mixed
  4 CCJS-defined: low risk (<=1 conviction, no previous incarcerations)
  5 CCJS-defined: high risk (>=2 convictions, any previous incarcerations)
  6 CCJS-defined: mixed
  9 missing
SAMP7 History of violence/sexual offence
  Value Label
  1 Yes to either (80% minimum)
  2 No to both (80% minimum)
  9 Missing
SAMP8 Type of offender
  Value Label
  1 Sex offender (80% minimum)
  2 Mentally disordered (80% minimum)
  3 Undifferentiated
  9 Missing
SAMP9 Violent vs. non-violent sample
  Value Label
  1 Non-violent (80% minimum)
  2 Violent (80% minimum)
  3 Undifferentiated
  9 Missing
PREDICT Predictor variable
CAT2 First predictor categorization
  Value Label
  1 School achievement
  2 School maladjustment
  3 Probation/parole schooling/training
  4 Education/employment
  5 Offender SES
  6 Financial
  7 Employment status at intake
  8 Employment history
  9 Employment needs at discharge
CAT3 Second predictor categorization
  Value Label
  1 Education
  2 Employment
  3 Education/employment
METH1 Extreme groups design
  Value Label
  0 No (9 to SAMP1, SAMP2, or SAMP3)
  1 Yes
METH11 Assessment/reporting of multiple outcomes
  Value Label
  1 No
  2 Yes
  3 Unknown
METH12 Recidivism data evaluated by raters blind to assessment of the predictor
  Value Label
  0 No
  1 Yes
  2 unknown
QUALITY Overall study quality
NOTE: Score determined by responses to METH1, METH2, METH10, METH11, and METH12.
QUALITY2 Overall study quality split a median of QUALITY score
  Value Label
  0 Less than or equal to median value
  1 Greater than median value
METH3 Follow-up interval in years
  Value Label
  2 6 months -<1 year
  3 1 year -<2 years
  4 2 years - <5 years
  5 5 years +
  9 Missing
METH4 Type of recidivism
  Value Label
  1 Probation/parole violation
  2 Arrest
  3 Conviction
  4 Incarceration
  5 Mixed
METH5 Study sample size
NRYES Number of non-recidivists HIGH on predictor
NRNO Number of non-recidivists LOW on predictor
RYES Number of recidivists HIGH on predictor
RNO Number of recidivists LOW on predictor
RECIDPC Percentage of recidivism
METH6 Statistical test employed
  Value Label
  1 r
  2 chi-square
  3 t
  4 F
  5 p
  6 %
METH7 Value of statistic
DEGREE Degrees of freedom
METH8 Pearson r value