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analyses:viechtbauer2007b

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analyses:viechtbauer2007b [2020/07/10 09:39] – Wolfgang Viechtbaueranalyses:viechtbauer2007b [2021/11/08 15:54] – Wolfgang Viechtbauer
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 The first model discussed in the article assumes that the //true// relative improvement rates are identical in the various studies and the only reason why the //observed// relative improvement rates differ from each other is due to sampling error/variability. We can fit this model with: The first model discussed in the article assumes that the //true// relative improvement rates are identical in the various studies and the only reason why the //observed// relative improvement rates differ from each other is due to sampling error/variability. We can fit this model with:
 <code rsplus> <code rsplus>
-Fixed-Effects Model (k = 17)+res <- rma(yi, vi, data=dat, method="EE", digits=2) 
 +res 
 +</code> 
 +<code output> 
 +Equal-Effects Model (k = 17)
  
 I^2 (total heterogeneity / total variability):   68.96% I^2 (total heterogeneity / total variability):   68.96%
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 Model Results: Model Results:
  
-estimate    se  zval  pval  ci.lb  ci.ub  +estimate    se  zval  pval  ci.lb  ci.ub 
-    0.33  0.05  6.78  <.01   0.23   0.42  *** +    0.33  0.05  6.78  <.01   0.23   0.42  ***
  
 --- ---
-Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1+Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 </code> </code>
 Since we are analyzing the log of the relative improvement rates, the model estimate also reflects the log relative rate. For easier interpretation, it is useful to back-transform the results with: Since we are analyzing the log of the relative improvement rates, the model estimate also reflects the log relative rate. For easier interpretation, it is useful to back-transform the results with:
analyses/viechtbauer2007b.txt · Last modified: 2022/08/03 11:24 by Wolfgang Viechtbauer