dat.assink2016 {metadat} | R Documentation |
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) |
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.
Concepts
psychology, criminology, standardized mean differences, multilevel models, cluster-robust inference
Note
The year
variable is not constant within study 3, as this study refers to two different publications using the same data.
Author(s)
Wolfgang Viechtbauer, wvb@metafor-project.org, https://www.metafor-project.org
Source
Assink, M., & Wibbelink, C. J. M. (2016). Fitting three-level meta-analytic models in R: A step-by-step tutorial. The Quantitative Methods for Psychology, 12(3), 154–174. https://doi.org/10.20982/tqmp.12.3.p154
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)
## Not run:
### 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)
### 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)
## End(Not run)