Results from 58 studies on the effectiveness of cognitive-behavioral therapy (CBT) for reducing recidivism in juvenile and adult offenders.

dat.landenberger2005

Format

The data frame contains the following columns:

studycharacter(first) author and year
pubtypecharacterpublication type (book chapter, journal article, report, or thesis)
countrycharactercountry where study was carried out (Canada, New Zealand, UK, or USA)
designcharacterstudy design (matched groups, nonequivalent groups, or randomized trial)
programcharacterpurpose of setting up the CBT program (for demonstration, practice, or research purposes)
settingcharactertreatment setting (community or prison)
designprobcharacterindication of study design problems (no, favors the control group, or favors the treatment group)
n.ctrl.recnumericnumber of recidivists in the control group
n.ctrl.nonnumericnumber of non-recidivists in the control group
n.cbt.recnumericnumber of recidivists in the CBT group
n.cbt.nonnumericnumber of non-recidivists in the CBT group
intervalnumericrecidivism interval (in months)
groupnumericstudy group (adults or juveniles)
agenumericmean age of the study group
malenumericpercentage of males in the study group
minoritynumericpercentage of minorities in the study group
lengthnumerictreatment length (in weeks)
sessionsnumericnumber of CBT sessions per week
hrs_weeknumerictreatment hours per week
hrs_totalnumerictotal hours of treatment
cbt.cogskillscharacterCBT component: cognitive skills (yes, no)
cbt.cogrestructcharacterCBT component: cognitive restructuring (yes, no)
cbt.intpprbsolvcharacterCBT component: interpersonal problem solving (yes, no)
cbt.socskillscharacterCBT component: social skills (yes, no)
cbt.angerctrlcharacterCBT component: anger control (yes, no)
cbt.victimimpactcharacterCBT component: victim impact (yes, no)
cbt.subabusecharacterCBT component: substance abuse (yes, no)
cbt.behavmodcharacterCBT component: behavior modification (yes, no)
cbt.relapseprevcharacterCBT component: relapse prevention (yes, no)
cbt.moralrsngcharacterCBT component: moral reasoning (yes, no)
cbt.roletakingcharacterCBT component: role taking (yes, no)
cbt.othercharacterCBT component: other (yes, no)

Details

Landenberger and Lipsey (2005) conducted a meta-analysis of 58 experimental and quasi-experimental studies of the effects of cognitive-behavioral therapy (CBT) on the recidivism rates of adult and juvenile offenders (see also Lipsey et al., 2007). The present dataset includes the results of these studies and a range of potential moderator variables to identify factors associated with variation in treatment effects.

Source

Personal communication.

References

Landenberger, N. A., & Lipsey, M. W. (2005). The positive effects of cognitive-behavioral programs for offenders: A meta-analysis of factors associated with effective treatment. Journal of Experimental Criminology, 1, 451–476. https://doi.org/10.1007/s11292-005-3541-7

Lipsey, M. W., Landenberger, N. A., & Wilson, S. J. (2007). Effects of cognitive-behavioral programs for criminal offenders. Campbell Systematic Reviews, 3(1), 1–27. https://doi.org/10.4073/csr.2007.6

Concepts

psychology, criminology, odds ratios, meta-regression

Examples

### copy data into 'dat' and examine data
dat <- dat.landenberger2005
head(dat)
#>              study pubtype country design  program   setting designprob n.ctrl.rec n.ctrl.non
#> 1    Guerra (1990) journal     USA    rct research    prison         no         23         29
#> 2    Leeman (1993) journal     USA    rct     demo    prison         no         15         22
#> 3  Kownacki (1995) chapter     USA    rct research community         no          4          6
#> 4    Larson (1989) chapter     USA    rct research community         no         11          2
#> 5      Finn (1998)  report     USA    rct practice    prison         no         22         60
#> 6 Goldstein (1989) chapter     USA    rct     demo community         no         14         18
#>   n.cbt.rec n.cbt.non interval     group age male minority length sessions hrs_week hrs_total
#> 1        10        19       12 juveniles  17   50       60     12        1      1.0        12
#> 2         3        17       12 juveniles  16  100       34     26        1      6.0       169
#> 3         2         9        6    adults  28  100       NA      7        1      2.0        14
#> 4         8         5       15    adults  19  100       94     63        1      1.0        63
#> 5        16        66       12    adults  30  100       74     17        5     15.0       240
#> 6         5        28        6 juveniles  NA   NA       NA     13        2      1.5        44
#>   cbt.cogskills cbt.cogrestruct cbt.intpprbsolv cbt.socskills cbt.angerctrl cbt.victimimpact
#> 1           yes             yes             yes            no            no               no
#> 2           yes             yes              no           yes           yes               no
#> 3           yes              no             yes           yes            no               no
#> 4           yes              no             yes           yes            no               no
#> 5           yes             yes             yes            no           yes              yes
#> 6           yes              no              no           yes           yes               no
#>   cbt.subabuse cbt.behavmod cbt.relapseprev cbt.moralrsng cbt.roletaking cbt.other
#> 1           no           no              no            no            yes        no
#> 2           no           no              no           yes            yes        no
#> 3           no           no              no            no            yes        no
#> 4           no           no              no            no            yes       yes
#> 5           no           no              no           yes             no       yes
#> 6           no           no              no           yes            yes       yes

# \dontrun{

### load metafor package
library(metafor)

### calculate log odds ratios (for non-recidivism in CBT vs. control groups) and sampling variances
dat <- escalc(measure="OR", ai=n.cbt.non, bi=n.cbt.rec, ci=n.ctrl.non, di=n.ctrl.rec, data=dat)

### fit random-effects model
res <- rma(yi, vi, data=dat)
res
#> 
#> Random-Effects Model (k = 58; tau^2 estimator: REML)
#> 
#> tau^2 (estimated amount of total heterogeneity): 0.1046 (SE = 0.0352)
#> tau (square root of estimated tau^2 value):      0.3234
#> I^2 (total heterogeneity / total variability):   70.62%
#> H^2 (total variability / sampling variability):  3.40
#> 
#> Test for Heterogeneity:
#> Q(df = 57) = 213.6898, p-val < .0001
#> 
#> Model Results:
#> 
#> estimate      se    zval    pval   ci.lb   ci.ub      
#>   0.4226  0.0605  6.9880  <.0001  0.3041  0.5411  *** 
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 

### estimated average OR and corresponding 95% CI/PI
predict(res, transf=exp, digits=2)
#> 
#>  pred ci.lb ci.ub pi.lb pi.ub 
#>  1.53  1.36  1.72  0.80  2.91 
#> 

### examine if number of treatment sessions per week is a potential moderator
res <- rma(yi, vi, mods = ~ sessions, data=dat)
res
#> 
#> Mixed-Effects Model (k = 58; tau^2 estimator: REML)
#> 
#> tau^2 (estimated amount of residual heterogeneity):     0.0761 (SE = 0.0285)
#> tau (square root of estimated tau^2 value):             0.2758
#> I^2 (residual heterogeneity / unaccounted variability): 62.88%
#> H^2 (unaccounted variability / sampling variability):   2.69
#> R^2 (amount of heterogeneity accounted for):            27.27%
#> 
#> Test for Residual Heterogeneity:
#> QE(df = 56) = 146.6400, p-val < .0001
#> 
#> Test of Moderators (coefficient 2):
#> QM(df = 1) = 6.0695, p-val = 0.0138
#> 
#> Model Results:
#> 
#>           estimate      se    zval    pval   ci.lb   ci.ub    
#> intrcpt     0.2022  0.1015  1.9931  0.0463  0.0034  0.4010  * 
#> sessions    0.0695  0.0282  2.4636  0.0138  0.0142  0.1248  * 
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 

### predicted ORs for 1, 2, 5, or 10 sessions per week
predict(res, newmods=c(1,2,5,10), transf=exp, digits=2)
#> 
#>   pred ci.lb ci.ub pi.lb pi.ub 
#> 1 1.31  1.12  1.53  0.75  2.30 
#> 2 1.41  1.25  1.59  0.81  2.45 
#> 3 1.73  1.49  2.02  0.99  3.04 
#> 4 2.45  1.64  3.66  1.25  4.80 
#> 

# }