The extracted data were entered into two spreadsheets (available at http://fearappeals.com/meta-analysis/): one summarising descriptive study data and one to compute effect sizes (and convert effect sizes to Cohen's d where necessary).
Most effects were also in the small to moderate range based on Cohen's interpretation, when converting effects into standardised mean differences.
Using previously reported methods [ 30], we were able to convert effects from all these studies except one [ 27] to relative risks per μg/m3 PM10.
The first used the odds ratios converted to effect sizes for inclusion in meta-analysis 1, and showed a significant negative effect of non-white ethnicity on performance (P<0.001, d=−0.59, −0.84 to −0.35; I=83%).
My plan was to graft myself to the proceedings, to get a flavor for what happens when some of the world's most unlikely places (uh, Kassel?) are showered with serious cash and temporarily altered — converted, in effect, into micro-worlds, lands unto themselves.
When necessary, we converted other effect measures to odds ratios by using available data.
ToM and neurocognitive score differences between patients and controls were converted to effect sizes in terms of Cohen's d.
One study [ 27] reported t-values, which were converted into effect size correlations using r = √[ t 2 /(t 2 + df)].
The survival probability S t), which is the output of the original TKTD model, 19) was then converted to effect as follows: where EFn is the extrapolation factor in simulation run n (out of 1000 runs), C t) [nmol/L] is the measured concentration time series of diazinon(8) and Effect n is the predicted effect size.
Effect sizes for continuous variables are calculated as d = (mean W-mean NW /SD W. Odds ratios, calculated as OR = p W × (1- p NW )/(p NW × (1- p W )), are converted to effect sizes using the method of Chinn [ 34].
