Implications for Meta-analysis Literature Comparison of Weights in Meta-analysis Under Realistic Conditions Michael T. Brannick Liu-Qin Yang Guy Cafri University of South Florida Abstract Study Design Important Notes Study Purpose The overall effect size in meta-analysis is a weighted mean. Does it matter what weights we use? Study Background— Other Weighting Schemes Hedges & Vevea’s (1998) approach in r Shrunken Estimates in r (Empirical Bayes) Combined Estimates in r: REVC by H&S; by H&V Unit Weights in r: The baseline Hunter & Schmidt use N; Hedges converts to z and uses N-3 Study Background— Realistic Simulation This simulation was based on published meta-analyses, so that values of k, N, rho ( ), and REVC ( ) would be representative of I/O meta-analyses. We compared several weighting procedures for random-effects meta- analysis under realistic conditions. Weighting schemes included unit, sample size, inverse variance in r and in z, empirical Bayes, and a combination procedure. Unit weights worked surprisingly well, and the Hunter and Schmidt (2004) procedures worked best overall. Results Published M-As AMJ, JAP and Personnel Psychology; Inclusion criterion: effect sizes (r) available or available after conversion 48 M-As and 1837 effect sizes Inter-rater reliability: 1.0 – Ns;.99 – effect sizes (r) Simulation conditions formed by characteristics of published meta-analyses Average N (N_bar) and the skewness of N distribution (N_skew) for each M-A A median of for the distribution of N_bar (sampling distribution) A median of 2.25 for the distribution of N_skew (sampling distribution) Four conditions along the medians (Figure 1) Sampling studies for the Monte Carlo A published M-A was randomly chosen, its K and Ns were used for that simulation. The parameters for the simulations were chosen from: Choice of parameters The distribution of | |: 10 th, 50 th, and 90 th percentile =.10,.22,.44, respectively The distribution of : 10 th, 50 th, and 90 th percentile =.0005,.0128, and ( ) by 3 ( ) of parameter conditions Therefore, the parameters in the simulation represent published studies Data generation A Monte Carlo program written in SAS IML Picked an M-A under one condition of Figure 1, then picked a parameter combination Sampled r from a normal distribution of that and Meta-analyzed those sampled r(s); repeated 5000 times Estimators H&S (2004) in r, H&V (1998) in z, and the other 4 approaches as described earlier Data analysis and were estimated with each of 6 approaches Root-mean-square-difference (RMSR) between the parameter and the estimate Skewness in the distribution of Ns was shown to have little effect, and so simulations were rerun with only the high/low levels of N considered Figures 2, 4, and 6 show the empirical sampling distributions of the population mean estimates Figure 3, 5, and 7 show the empirical sampling distributions of the REVC estimates The design elements had their generally expected impacts on the estimates The empirical sampling distributions were generally more compact with big Ns The means got larger when the underlying parameters increased The variance of the distribution increases as increases Provided a database and quantitative summary of published M-As of interest Monte Carlo simulation based on representative study characteristics Weights only matter when k and N are small Conclusions Unit weights had surprisingly good estimates, esp. when and are large H&V (1998) in z performed as expected— slight overestimates H&S (2004) in r worked best for estimating overall mean and REVC Study Purposes and Study Background Random-effect models were applied in the current study Sampled actual numbers of studies (K) and sample sizes (N) from the published M-As Used population parameters representing published M-A data Good estimator Good Estimator Distributions of sample sizes from published meta-analyses Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7