cfMeDIP
Calculating effect size:
https://www.simplypsychology.org/effect-size.html
What is effect size?
Statistical significance is the least interesting thing about the results.
Describe the results in terms of measures of magnitude – not just, does a treatment affect people, but how much does it affect them.
Effect size is a quantitative measure of the magnitude of the experimenter effect. The larger the effect size the stronger the relationship between two variables.
Can determine effect size when comparing any two groups to see how substantially different they are.
Typically, research studies will comprise an experimental group and a control group. The experimental group may be an intervention or treatment which is expected to effect a specific outcome. For example, assess the effect of a therapy on treating depression. The effect size value will show us if the therapy as had a small, medium or large effect on depression.
How to calculate and interpret effect sizes
Effect sizes either measure the sizes of associations between variables or the sizes of differences between group means.
Cohen's d
Cohen's d is an appropriate effect size for the comparison between two means. It can be used, for example, to accompany the reporting of t-test and ANOVA results. 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.
Effect size = [(mean of experimental group) - (mean of control group)] / standard deviation of population
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. Standard deviations are equivalent to z-scores (1 standard deviation = 1 z-score).
Cohen suggested that:
d=0.2 be considered a 'small' effect size
d=0.5 represents a 'medium' effect size
d=0.8 a 'large' effect size.
This means that if two groups' means don't differ by 0.2 standard deviations or more, the difference is trivial, even if it is statistically significant.
R Development Core Team (2012). R: A Language and Environment for Statistical Computing.
Calculate Cohen's d in R: R power package
https://www.simplypsychology.org/effect-size.html
What is effect size?
Statistical significance is the least interesting thing about the results.
Describe the results in terms of measures of magnitude – not just, does a treatment affect people, but how much does it affect them.
Effect size is a quantitative measure of the magnitude of the experimenter effect. The larger the effect size the stronger the relationship between two variables.
Can determine effect size when comparing any two groups to see how substantially different they are.
Typically, research studies will comprise an experimental group and a control group. The experimental group may be an intervention or treatment which is expected to effect a specific outcome. For example, assess the effect of a therapy on treating depression. The effect size value will show us if the therapy as had a small, medium or large effect on depression.
How to calculate and interpret effect sizes
Effect sizes either measure the sizes of associations between variables or the sizes of differences between group means.
Cohen's d
Cohen's d is an appropriate effect size for the comparison between two means. It can be used, for example, to accompany the reporting of t-test and ANOVA results. 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.
Effect size = [(mean of experimental group) - (mean of control group)] / standard deviation of population
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. Standard deviations are equivalent to z-scores (1 standard deviation = 1 z-score).
Cohen suggested that:
d=0.2 be considered a 'small' effect size
d=0.5 represents a 'medium' effect size
d=0.8 a 'large' effect size.
This means that if two groups' means don't differ by 0.2 standard deviations or more, the difference is trivial, even if it is statistically significant.
R Development Core Team (2012). R: A Language and Environment for Statistical Computing.
Calculate Cohen's d in R: R power package