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Table 3. Discrimination meta-analysis results using random-effects and robust variance estimation models.
Measure Random-Effects Model RVE ⍴ = .8 RVE ⍴ = 0 RVE ⍴ = .2 RVE ⍴ = .4 RVE ⍴ = .6 RVE ⍴ = 1 
0.234 0.215 0.215 0.215 0.215 0.215 0.215 
95% CI [0.197, 0.271] [0.18, 0.249] [0.18, 0.249] [0.18, 0.249] [0.18, 0.249] [0.18, 0.249] [0.18, 0.249] 
z-value 11.945       
t-value  11.960 11.961 11.961 11.961 11.96 11.96 
tau-squared 0.022 0.018 0.018 0.018 0.018 0.018 0.018 
Q-stat 338.295       
I-squared 82.773 79.510 79.439 79.457 79.474 79.492 79.528 
Index of Dispersion [-0.055, 0.487]       
Measure Random-Effects Model RVE ⍴ = .8 RVE ⍴ = 0 RVE ⍴ = .2 RVE ⍴ = .4 RVE ⍴ = .6 RVE ⍴ = 1 
0.234 0.215 0.215 0.215 0.215 0.215 0.215 
95% CI [0.197, 0.271] [0.18, 0.249] [0.18, 0.249] [0.18, 0.249] [0.18, 0.249] [0.18, 0.249] [0.18, 0.249] 
z-value 11.945       
t-value  11.960 11.961 11.961 11.961 11.96 11.96 
tau-squared 0.022 0.018 0.018 0.018 0.018 0.018 0.018 
Q-stat 338.295       
I-squared 82.773 79.510 79.439 79.457 79.474 79.492 79.528 
Index of Dispersion [-0.055, 0.487]       

Robust variance estimation (RVE) model at ⍴ = .8 is the weight used in all RVE models. 95% CI: 95% confidence intervals around the weighted mean correlation.

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