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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].
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It was determined that this sample size had excellent power to detect medium effect sizes for both continuous and dichotomous outcomes [ 10].
Multiple linear regression analysis was used to determine adjusted effect sizes for predictor variables of continuous outcomes.
The effect size for continuous variables was calculated with Cohen's d.
The objective of this study was to investigate a novel light backscatter sensor, with a large field of view relative to curd size, for continuous on-line monitoring of coagulation and syneresis to improve curd moisture content control.
Hedges' g was calculated as effect size for continuous data; Mantel-Haenszel Risk ratios (MH RR) and 95% confidence interval (95% CI) were used for dichotomous data.
The standardised mean difference (SMD; Hedges' g) was used as the effect size for continuous outcomes and studies were weighted using the inverse of variance.
Instead, weighted mean values (by group size) for continuous outcomes were calculated without dispersion using Excel:mac 2001 on a Macintosh G4.
We used one-way analyses of variance (for large sample size) and Mann–Whitney U-tests (for small sample size) for continuous variables, and chi-square tests for comparing categorical variables between two groups.
In this latter case, there currently appears to be no unanimous agreement about which effect size is preferable, and different textbooks of meta-analyses provide differently nuanced recommendations about the selection of the appropriate effect size for continuous variables.
Effect estimates were calculated in such a way that a beneficial effect of CT was always represented by a negative effect size (for continuous outcomes) or by an odds ratio smaller than 1 (for dichotomous outcomes).
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