metadat (version 1.4-0)

dat.assink2016: Studies on the Association between Recidivism and Mental Health

Description

Results from 17 studies on the association between recidivism and mental health in delinquent juveniles.

Usage

dat.assink2016

Format

The data frame contains the following columns:

study	numeric	study id number
esid	numeric	effect size within study id number
id	numeric	row id number
yi	numeric	standardized mean difference
vi	numeric	corresponding sampling variance
pubstatus	numeric	published study (0 = no; 1 = yes)
year	numeric	publication year of the study (approximately mean centered)
deltype	character	type of delinquent behavior in which juveniles could have recidivated (either general, overt, or covert)

Author

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

Concepts

psychology, criminology, standardized mean differences, multilevel models, cluster-robust inference

Details

The studies included in this dataset (which is a subset of the data used in Assink et al., 2015) compared the difference in recidivism between delinquent juveniles with a mental health disorder and a comparison group of juveniles without a mental health disorder. Since studies differed in the way recidivism was defined and assessed, results are given in terms of standardized mean differences, with positive values indicating a higher prevalence of recidivism in the group of juveniles with a mental health disorder.

Multiple effect size estimates could be extracted from most studies (e.g., for different delinquent behaviors in which juveniles could have recidivated), necessitating the use of appropriate models/methods for the analysis. Assink and Wibbelink (2016) illustrate the use of multilevel meta-analysis models for this purpose.

References

Assink, M., van der Put, C. E., Hoeve, M., de Vries, S. L. A., Stams, G. J. J. M., & Oort, F. J. (2015). Risk factors for persistent delinquent behavior among juveniles: A meta-analytic review. Clinical Psychology Review, 42, 47--61. https://doi.org/10.1016/j.cpr.2015.08.002

Examples

### copy data into 'dat' and examine data
dat <- dat.assink2016
head(dat, 9)

if (FALSE) {
### load metafor package
library(metafor)

### fit multilevel model
res <- rma.mv(yi, vi, random = ~ 1 | study/esid, data=dat)
res

### use cluster-robust inference methods
robust(res, cluster=study, clubSandwich=TRUE)

### LRTs for the variance components
res0 <- rma.mv(yi, vi, random = ~ 1 | study/esid, data=dat, sigma2=c(0,NA))
anova(res0, res)
res0 <- rma.mv(yi, vi, random = ~ 1 | study/esid, data=dat, sigma2=c(NA,0))
anova(res0, res)

### examine some potential moderators via meta-regression
rma.mv(yi, vi, mods = ~ pubstatus, random = ~ 1 | study/esid, data=dat)
rma.mv(yi, vi, mods = ~ year, random = ~ 1 | study/esid, data=dat)
dat$deltype <- relevel(factor(dat$deltype), ref="general")
rma.mv(yi, vi, mods = ~ deltype, random = ~ 1 | study/esid, data=dat)
rma.mv(yi, vi, mods = ~ year + deltype, random = ~ 1 | study/esid, data=dat)

### assume that the effect sizes within studies are correlated with rho=0.6
V <- vcalc(vi, cluster=study, obs=esid, data=dat, rho=0.6)
round(V[dat$study %in% c(1,2), dat$study %in% c(1,2)], 4)

### fit multilevel model using this approximate V matrix
res <- rma.mv(yi, V, random = ~ 1 | study/esid, data=dat)
res

### use cluster-robust inference methods
robust(res, cluster=study, clubSandwich=TRUE)

### use a correlation of 0.7 for effect sizes corresponding to the same type of
### delinquent behavior and a correlation of 0.5 for effect sizes corresponding
### to different types of delinquent behavior
V <- vcalc(vi, cluster=study, type=deltype, obs=esid, data=dat, rho=c(0.7, 0.5))

### fit multilevel model using this approximate V matrix
res <- rma.mv(yi, V, random = ~ 1 | study/esid, data=dat)
res

### use cluster-robust inference methods
robust(res, cluster=study, clubSandwich=TRUE)
}