metadat (version 1.4-0)

dat.landenberger2005: Studies on the Effectiveness of CBT for Reducing Recidivism

Description

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

Usage

dat.landenberger2005

Format

The data frame contains the following columns:

study	character	(first) author and year
pubtype	character	publication type (book chapter, journal article, report, or thesis)
country	character	country where study was carried out (Canada, New Zealand, UK, or USA)
design	character	study design (matched groups, nonequivalent groups, or randomized trial)
program	character	purpose of setting up the CBT program (for demonstration, practice, or research purposes)
setting	character	treatment setting (community or prison)
designprob	character	indication of study design problems (no, favors the control group, or favors the treatment group)
n.ctrl.rec	numeric	number of recidivists in the control group
n.ctrl.non	numeric	number of non-recidivists in the control group
n.cbt.rec	numeric	number of recidivists in the CBT group
n.cbt.non	numeric	number of non-recidivists in the CBT group
interval	numeric	recidivism interval (in months)
group	numeric	study group (adults or juveniles)
age	numeric	mean age of the study group
male	numeric	percentage of males in the study group
minority	numeric	percentage of minorities in the study group
length	numeric	treatment length (in weeks)
sessions	numeric	number of CBT sessions per week
hrs_week	numeric	treatment hours per week
hrs_total	numeric	total hours of treatment
cbt.cogskills	character	CBT component: cognitive skills (yes, no)
cbt.cogrestruct	character	CBT component: cognitive restructuring (yes, no)
cbt.intpprbsolv	character	CBT component: interpersonal problem solving (yes, no)
cbt.socskills	character	CBT component: social skills (yes, no)
cbt.angerctrl	character	CBT component: anger control (yes, no)
cbt.victimimpact	character	CBT component: victim impact (yes, no)
cbt.subabuse	character	CBT component: substance abuse (yes, no)
cbt.behavmod	character	CBT component: behavior modification (yes, no)
cbt.relapseprev	character	CBT component: relapse prevention (yes, no)
cbt.moralrsng	character	CBT component: moral reasoning (yes, no)
cbt.roletaking	character	CBT component: role taking (yes, no)
cbt.other	character	CBT component: other (yes, no)

Author

Wolfgang Viechtbauer, , https://www.metafor-project.org

Concepts

psychology, criminology, odds ratios, meta-regression

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.

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

Examples

### copy data into 'dat' and examine data
dat <- dat.landenberger2005
head(dat)

if (FALSE) {
### 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

### estimated average OR and corresponding 95% CI/PI
predict(res, transf=exp, digits=2)

### examine if number of treatment sessions per week is a potential moderator
res <- rma(yi, vi, mods = ~ sessions, data=dat)
res

### predicted ORs for 1, 2, 5, or 10 sessions per week
predict(res, newmods=c(1,2,5,10), transf=exp, digits=2)
}