dat.tannersmith2016 {metadat}

Studies on the Relationship between School Motivation and Criminal Behavior

Description

Results from 17 studies on the correlation between school motivation/attitudes and subsequent delinquent/criminal behavior.

Usage

dat.tannersmith2016

Format

The data frame contains the following columns:

studyid	numeric	study identifier
yi	numeric	r-to-z transformed correlation coefficient
vi	numeric	corresponding sampling variance
sei	numeric	corresponding standard error
aget1	numeric	age at which the school motivation/attitudes were assessed
aget2	numeric	age at which the delinquent/criminal behavior was assessed
propmale	numeric	proportion of male participants in the sample
sexmix	character	whether the sample consisted only of males, only of females, or a mix

Details

The dataset includes 113 r-to-z transformed correlation coefficients from 17 prospective longitudinal studies that examined the relationship between school motivation/attitudes and subsequent delinquent/criminal behavior.

Multiple coefficients could be extracted from the studies “given the numerous ways in which school motivation/attitudes variables could be operationalized (e.g., academic aspirations, academic self-efficacy) as well as the numerous ways in which crime/delinquency could be operationalized (e.g., property crime, violent crime)” (Tanner-Smith et al., 2016).

Since information to compute the covariance between multiple coefficients within studies is not available, Tanner-Smith et al. (2016) illustrate the use of cluster-robust inference methods for the analysis of this dataset.

Note that this dataset is only meant to be used for pedagogical and demonstration purposes and does not constitute a proper review or synthesis of the complete and current research evidence on the given topic.

Concepts

psychology, criminology, correlation coefficients, multilevel models, cluster-robust inference, meta-regression

Author(s)

Wolfgang Viechtbauer, [e-mail], https://www.metafor-project.org

Source

Tanner-Smith, E. E., Tipton, E. & Polanin, J. R. (2016). Handling complex meta-analytic data structures using robust variance estimates: A tutorial in R. Journal of Developmental and Life-Course Criminology, 2(1), 85–112. ⁠https://doi.org/10.1007/s40865-016-0026-5⁠

Examples

### copy data into 'dat' and examine data
dat <- dat.tannersmith2016
head(dat)

## Not run: 

### load metafor package
library(metafor)

### compute mean age variables within studies
dat$aget1 <- ave(dat$aget1, dat$studyid)
dat$aget2 <- ave(dat$aget2, dat$studyid)

### construct an effect size identifier variable
dat$esid <- 1:nrow(dat)

### construct an approximate var-cov matrix assuming a correlation of 0.8
### for multiple coefficients arising from the same study
V <- vcalc(vi, cluster=studyid, obs=esid, rho=0.8, data=dat)

### fit a multivariate random-effects model using the approximate var-cov matrix V
res <- rma.mv(yi, V, random = ~ esid | studyid, data=dat)
res

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

### note: the results obtained above and below are slightly different compared
### to those given by Tanner-Smith et al. (2016) since the approach illustrated
### here makes use a multivariate random-effects model for the 'working model'
### before applying the cluster-robust inference methods, while the results given
### in the paper are based on a somewhat simpler working model

### examine the main effects of the age variables
res <- rma.mv(yi, V, mods = ~ aget1 + aget2,
              random = ~ 1 | studyid/esid, data=dat)
robust(res, cluster=studyid, clubSandwich=TRUE)

### also examine their interaction
res <- rma.mv(yi, V, mods = ~ aget1 * aget2,
              random = ~ 1 | studyid/esid, data=dat)
robust(res, cluster=studyid, clubSandwich=TRUE)

### add the sexmix factor to the model
res <- rma.mv(yi, V, mods = ~ aget1 * aget2 + sexmix,
              random = ~ 1 | studyid/esid, data=dat)
robust(res, cluster=studyid, clubSandwich=TRUE)


## End(Not run)