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Furthermore, mean differences and standard deviation in costs and effects were reported.
Mean differences and 95% confidence intervals were calculated for continuous variables.
Intraobserver systematic errors between the replicate scans were described as mean differences and compared statistically with paired t tests.
Our aim was to investigate commonalities, mean differences and between-subject variability in brain activation for successful response inhibition between the performance-adjusted and fixed delay version.
According to multilevel modeling results, mean differences and effect sizes were: School A = 15.78%, pseudo-R2 = .34 [strong effect]; School B = 14.26%, pseudo-R2 = .22 [moderate effect].
The data was analyzed using fixed-effects or random-effects models with mean differences and risk ratios for continuous and dichotomous variables, respectively.
However, in SAVE each difference in variance across the groups takes up a dimension, and hence SAVE can have difficulty detecting mean differences and covariance differences.
Changes in outcome from baseline were compared between groups by mean differences and their 95% confidence intervals using independent t-test (P = 0.05).
Afterwards, we calculate the mean differences and convert the values to the Pearson correlation coefficient according to Borenstein et al. (2009).
When evaluating the model we paid attention to the fact that the model was to be used for estimating mean differences and not for e.g. obtaining predictions.
However, to focus only on mean differences and effects might overlook some important information in relation to the heterogeneous effect of covariates along the performance distribution.
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