Contents
How to do a meta-analysis of standardized mean?
In studies 5 and 8, the authors only reported the t-statistic from an independent samples t-test comparing the two groups, and in studies 2 and 9, the authors only reported the (two-sided) p-value corresponding to the t-test. For all studies, the group sizes are known. 1)
How to reconstruct data for a meta-analysis?
Given only this information, it is possible to reconstruct the full dataset for the meta-analysis (after each step, I would recommend examining the contents of the dat2 object to better appreciate what the code is doing).
How to find the mean difference in a study?
With this information, we can compute the standardized mean difference (and corresponding sampling variance) for each study with: Finally, a random-effects model can be fitted to these data with:
How to calculate effect sizes based on means?
Effect Sizes Based on Means Introduction Raw (unstandardized) mean difference D Standardized mean difference, d and g Response ratios INTRODUCTION When the studies report means and standard deviations, the preferred effect size is usually the raw mean difference, the standardized mean difference, or the response ratio.
Which is an advantage of raw mean difference?
The primary advantage of the raw mean difference is that it is intuitively meaningful, eitherinherently(forexample,bloodpressure,whichismeasuredonaknownscale) orbecauseofwidespreaduse(forexample,anationalachievementtestforstudents, where all relevant parties are familiar with the scale).
How to do a random effect meta-analysis in R?
Random-effects meta-analyses are very easy to code in R. Compared to the fixed-effects-model Chapter 5.1, we can simply remove the method = “FE” argument, if we want to use the default REML estimator: m_re <- rma(yi = df$d, # The d-column of the df, which contains Cohen’s d vi = df$vi) # The vi-column of the df, which contains the variances m_re