The metafor Package

A Meta-Analysis Package for R

User Tools

Site Tools

analyses:raudenbush2009

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revisionPrevious revision
Next revision		Previous revision
Last revisionBoth sides next revision
analyses:raudenbush2009 [2021/11/08 16:05] – Wolfgang Viechtbaueranalyses:raudenbush2009 [2022/08/03 17:07] – Wolfgang Viechtbauer
Line 11: Line 11:
 dat dat
 </code> </code>
-(I copy the dataset into ''dat'', which is a bit shorter and therefore easier to type further below). The contents of the dataset are: +(I copy the dataset into ''dat'', which is a bit shorter and therefore easier to type further below). The contents of the dataset are:
 <code output> <code output>
    study               author year weeks setting tester    yi     vi    study               author year weeks setting tester    yi     vi
Line 77: Line 77:
 H^2 (total variability / sampling variability):  1.72 H^2 (total variability / sampling variability):  1.72
  
-Test for Heterogeneity: +Test for Heterogeneity:
 Q(df = 18) = 35.830, p-val = 0.007 Q(df = 18) = 35.830, p-val = 0.007
  
 Model Results: Model Results:
  
-estimate       se     zval     pval    ci.lb    ci.ub           +estimate       se     zval     pval    ci.lb    ci.ub 
-   0.084    0.052    1.621    0.105   -0.018    0.185          +   0.084    0.052    1.621    0.105   -0.018    0.185
  
 --- ---
Line 117: Line 117:
 H^2 (total variability / sampling variability):  1.99 H^2 (total variability / sampling variability):  1.99
  
-Test for Heterogeneity: +Test for Heterogeneity:
 Q(df = 18) = 35.830, p-val = 0.007 Q(df = 18) = 35.830, p-val = 0.007
  
 Model Results: Model Results:
  
-estimate       se     zval     pval    ci.lb    ci.ub           +estimate       se     zval     pval    ci.lb    ci.ub 
-   0.089    0.056    1.601    0.109   -0.020    0.199          +   0.089    0.056    1.601    0.109   -0.020    0.199
  
 --- ---
Line 150: Line 150:
 R^2 (amount of heterogeneity accounted for):            100.00% R^2 (amount of heterogeneity accounted for):            100.00%
  
-Test for Residual Heterogeneity: +Test for Residual Heterogeneity:
 QE(df = 17) = 16.571, p-val = 0.484 QE(df = 17) = 16.571, p-val = 0.484
  
-Test of Moderators (coefficient(s) 2): +Test of Moderators (coefficient(s) 2):
 QM(df = 1) = 19.258, p-val < .001 QM(df = 1) = 19.258, p-val < .001
  
 Model Results: Model Results:
  
-         estimate     se    zval   pval   ci.lb   ci.ub     +         estimate     se    zval   pval   ci.lb   ci.ub
 intrcpt     0.407  0.087   4.678  <.001   0.237   0.578  *** intrcpt     0.407  0.087   4.678  <.001   0.237   0.578  ***
 weeks.c    -0.157  0.036  -4.388  <.001  -0.227  -0.087  *** weeks.c    -0.157  0.036  -4.388  <.001  -0.227  -0.087  ***
Line 247: Line 247:
 res.std$HE   <- rma(yi, vi, data=dat, digits=3, method="HE") res.std$HE   <- rma(yi, vi, data=dat, digits=3, method="HE")
  
-round(t(sapply(res.std, function(x) c(tau2=x$tau2, mu=x$b, se=x$se, z=x$zval, ci.lb=x$ci.lb, ci.ub=x$ci.ub))), 3)+round(t(sapply(res.std, function(x) 
 +               c(tau2=x$tau2, mu=x$b, se=x$se, z=x$zval, 
 +                 ci.lb=x$ci.lb, ci.ub=x$ci.ub))), 3)
 </code> </code>
 <code output> <code output>
Line 271: Line 273:
 res.knha$HE   <- rma(yi, vi, data=dat, digits=3, method="HE", test="knha") res.knha$HE   <- rma(yi, vi, data=dat, digits=3, method="HE", test="knha")
  
-round(t(sapply(res.knha, function(x) c(tau2=x$tau2, mu=x$b, se=x$se, z=x$zval, ci.lb=x$ci.lb, ci.ub=x$ci.ub))), 3)+round(t(sapply(res.knha, function(x) 
 +               c(tau2=x$tau2, mu=x$b, se=x$se, z=x$zval, 
 +                 ci.lb=x$ci.lb, ci.ub=x$ci.ub))), 3)
 </code> </code>
 <code output> <code output>
Line 294: Line 298:
 res.hw$HE   <- robust(res.std$HE,   cluster=dat$study, adjust=FALSE) res.hw$HE   <- robust(res.std$HE,   cluster=dat$study, adjust=FALSE)
  
-round(t(sapply(res.hw, function(x) c(tau2=x$tau2, mu=x$b, se=x$se, t=x$tval, ci.lb=x$ci.lb, ci.ub=x$ci.ub))), 3)   +round(t(sapply(res.hw, function(x) c(tau2=x$tau2, mu=x$b, se=x$se, t=x$tval, ci.lb=x$ci.lb, ci.ub=x$ci.ub))), 3)
 </code> </code>
 <code output> <code output>
Line 312: Line 316:
 ==== References ==== ==== References ====
  
-Hedges, L. V. (1983). A random effects model for effect sizes. //Psychological Bulletin, 93//(2), 388--395. +Hedges, L. V. (1983). A random effects model for effect sizes. //Psychological Bulletin, 93//(2), 388--395.
  
 Higgins, J. P. T., & Thompson, S. G. (2002). Quantifying heterogeneity in a meta-analysis. //Statistics in Medicine, 21//(11), 1539--1558. Higgins, J. P. T., & Thompson, S. G. (2002). Quantifying heterogeneity in a meta-analysis. //Statistics in Medicine, 21//(11), 1539--1558.
analyses/raudenbush2009.txt · Last modified: 2022/08/03 17:09 by Wolfgang Viechtbauer