Hi Paul, I was reading a meta-analysis about hypertrophy and it used effect size. And the author wrote this:. Studies have used wholebody lean mass 11,26 , regional lean mass 27,35 , muscle thickness 31,40 , muscle cross-sectional area 31,35 , or muscle circumference 30—32 to measure hypertrophy.
Different regions of a particular muscle may also be measured Thus, comparisons across studies can be difficult. The calculation of a standardized effect size ES can aid in the comparison across studies 3. From what I understand, he means effect size can be used to measure different measures of muscle growth and be compared.
Is this true? If you are releasing a second edition , I have some suggestions. Now I understand. This is the apples and and oranges problem I discuss on p. The author is interested in the effect of X set volume — whatever that is on Y hypertrophy but since the measurement of Y varies across studies, he is not sure that direct comparisons are possible.
The solution he proposes calculating standardized ES does not deal with this particular problem but the problem of comparing results effects not variables that have been reported in different metrics.
If the assumption I stated on the first line of this answer is incorrect, and a higher positive number means a higher bad outcome say longer psychotic episode, more unwanted side effects, etc. Sign up to join this community. The best answers are voted up and rise to the top. Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams?
Learn more. How are these negative values understood? Ask Question. Asked 7 years, 2 months ago. Active 7 years, 2 months ago. Just use this form to sign up for the spreadsheet, and for more practical updates like this one:. Where M 1 and M 2 are the means for the 1st and 2nd samples, and SD pooled is the pooled standard deviation for the samples. SD pooled is properly calculated using this formula:. In the first, more lengthy formula, X 1 represents a sample point from your first sample, and Xbar 1 represents the sample mean for the first sample.
The distance between the sample mean and the sample point is squared before it is summed over every sample point otherwise you would just get zero. Obviously, X 2 and Xbar 2 represent the sample point and sample mean from the second sample. It is also widely used in meta-analysis.
To calculate the standardized mean difference between two groups, subtract the mean of one group from the other M1 — M2 and divide the result by the standard deviation SD of the population from which the groups were sampled.
A d of 1 indicates the two groups differ by 1 standard deviation, a d of 2 indicates they differ by 2 standard deviations, and so on. This means that if the difference between two groups' means is less than 0. This parameter of effect size summarises the strength of the bivariate relationship. According to Cohen , , the effect size is low if the value of r varies around 0. A lower p -value is sometimes interpreted as meaning there is a stronger relationship between two variables.
It can be argued that emphasizing the size of effect promotes a more scientific approach, as unlike significance tests, effect size is independent of sample size. Unlike a p -value, effect sizes can be used to quantitatively compare the results of studies done in a different setting.
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