# The metafor Package

A Meta-Analysis Package for R

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tips:comp_two_independent_estimates

# Differences

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 tips:comp_two_independent_estimates [2020/07/03 10:28]Wolfgang Viechtbauer tips:comp_two_independent_estimates [2020/07/03 10:32] (current)Wolfgang Viechtbauer Both sides previous revision Previous revision 2020/07/03 10:32 Wolfgang Viechtbauer 2020/07/03 10:28 Wolfgang Viechtbauer 2020/07/03 10:23 Wolfgang Viechtbauer 2019/09/26 07:15 external edit 2020/07/03 10:32 Wolfgang Viechtbauer 2020/07/03 10:28 Wolfgang Viechtbauer 2020/07/03 10:23 Wolfgang Viechtbauer 2019/09/26 07:15 external edit Line 113: Line 113: Signif. codes: ​ 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Signif. codes: ​ 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 ​ - The result is very similar to what we saw earlier: The coefficient ​for the ''​alloc''​ dummy is equal to $b_1 = -0.490$ ($SE = 0.362$) and not significant ($p = .176$). + The result is very similar to what we saw earlier: The coefficient ​corresponding to the ''​alloc''​ dummy is equal to $b_1 = -0.490$ ($SE = 0.362$) and not significant ($p = .176$). However, the results are not exactly identical. The reason for this is as follows. When we fit separate random-effects models in the two subsets, we are allowing the amount of heterogeneity within each set to be different (as shown earlier, the estimates were $\hat{\tau}^2 = 0.393$ and $\hat{\tau}^2 = 0.212$ for studies using and not using random assignment, respectively). On the other hand, the mixed-effects meta-regression model fitted above has a single variance component for the amount of residual heterogeneity,​ which implies that the amount of heterogeneity //within each subset// is assumed to be the same ($\hat{\tau}^2 = 0.318$ in this example). However, the results are not exactly identical. The reason for this is as follows. When we fit separate random-effects models in the two subsets, we are allowing the amount of heterogeneity within each set to be different (as shown earlier, the estimates were $\hat{\tau}^2 = 0.393$ and $\hat{\tau}^2 = 0.212$ for studies using and not using random assignment, respectively). On the other hand, the mixed-effects meta-regression model fitted above has a single variance component for the amount of residual heterogeneity,​ which implies that the amount of heterogeneity //within each subset// is assumed to be the same ($\hat{\tau}^2 = 0.318$ in this example). 