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Showing content from http://cran.rstudio.com/web/packages/rlang/../metapower/vignettes/Using-metapower.html below:

Using-metapower

For Fixed-Effects Model, the test of homogeneity examines whether the amount of variation among effect sizes is greater than that of sampling error alone. To compute this, simply wrap the same arguments used in mpower with homogen_power. Since it is hard to justify selecting the standard deviation between a set of studies, homogen_power() automatically computes power across a range of different standard deviation values (i.e., SD = 1, SD = 2.)

(my_homogen_power <- homogen_power(effect_size = .25, study_size = 20, k = 30,i2 = .50, es_type = "d"))
#> 
#>  Power Analysis for Test of Homogeneity in Meta-analysis 
#> 
#>  Effect Size Metric:                d 
#>  Expected Effect Size:              0.25 
#>  Expected Study Size:               20 
#>  Expected Number of Studies:        30 
#> 
#>  Estimated Power: Test of Homogeneity 
#> 
#>  Fixed-Effects Model (SD = 1)        0.06687224 
#>  Fixed-Effects Model (SD = 2)        0.1348743 
#>  Fixed-Effects Model (SD = 3)        0.2966788 
#>  Fixed-Effects Model (SD = 4)        0.55878 
#>  Fixed-Effects Model (SD = 5)        0.8167712 
#> 
#>  Random-Effects Model (i2 = 50%):    0.9872307

For Random-Effects models, homogen_power() computes power given the user-specified heterogeneity value (i.e., \(I^2\))

In this example, we specified i2 = .50, but below are common benchmarks:

1. \(I^2\) = 0% (No Heterogeneity)
2. \(I^2\) = 25% (Low Heterogeneity)
3. \(I^2\) = 50% (Moderate Heterogeneity)
4. \(I^2\) = 75% (Large Heterogeneity)

We can visualize power across a range with plot_homogen_power

plot_homogen_power(my_homogen_power)

Although researchers are primarily interested in conducting meta-analysis to quantify the main effect of a specific phenomenon, It is very common to evaluate the moderation of this overall effect based on a number of study- and/or sample-related characteristics such as task paradigm or age group (e.g., children, adolescents, adults). To compute the statistical power for the detection of categorical moderators, we use the function subgroup_power() with a few additional arguments, mainly:

1. Expected number of groups (n_groups):
2. Expected effect size of each group(effect_sizes):

…for our meta-analysis of face recognition deficits in autism

We may expect that face recognition tasks have larger effect sizes then face perception tasks; therefore, we specify 2 groups and their respective expected effect sizes:

1. n_groups = 2
2. effect_sizes = c(.2,.5)

my_subgroup_power <- subgroup_power(n_groups = 2, 
                    effect_sizes = c(.2,.5), 
                    study_size = 20,
                    k = 30,
                    i2 = .5,
                    es_type = "d")

print(my_subgroup_power)
#> 
#>  Power Analysis for Subgroup analysis: 
#> 
#>  Effect Size Metric:                d 
#>  Number of Subgroups:               2 
#>  Groups:                            
#>  Expected Effect Sizes:             0.2 0.5 
#>  Expected Study Size:               20 
#>  Expected Number of Studies:        30 
#> 
#>  Esimated Power to detect subgroup differences 
#> 
#>  Fixed-Effects Model:               0.4456862 
#>  Random-Effects Model (i2 = 50%):   0.2517633
plot_subgroup_power(my_subgroup_power)

Given, this set of expected values, we have 44.57% to detect between-group differences under a Fixed-Effects model. As expected, moderator effects are much harder to detect and more studies are required, especially when heterogeneity is high.

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