Calculate cohen’s d for 2 paired samples. See Using_cohens_d.ipynb for a notebook of given examples.
Syntax
d = cD.cohensd_2paired(pre, post)
d = cD.cohensd_2paired(pre, post, Name=Value)
Description
A
d = cD.cohensd_2paired(pre, post) returns cohen’s d for 2 paired samples. example
B
d = cD.cohensd_2paired(pre, post, Name=Value) returns cohen’s d for 2 paired samples with additional options specified by one or more name-value pair arguments. For example, you can compare to a mean. example
Examples
Example 1
Generate some random data and find cohen’s d.
mu = np.array([3, 5])
sigma = np.array([[1, 0.6], [0.6, 3]])
data = np.random.multivariate_normal(mu, sigma, (100,))
cD.cohensd_2paired(data[:,0], data[:,1])
d = 1.4494613347104766
Example 2
Generate some random data and calculate cohen’s d with mean of 15.
mu = np.array([3, 5])
sigma = np.array([[1, 0.6], [0.6, 3]])
data = np.random.multivariate_normal(mu, sigma, (100,))
cD.cohensd_2paired(data[:,0], data[:,1])
d = 12.800599127957925
pre
Pre data vector
Vector of pre data.
Data Types: (numeric, vector)
post
Post data vector.
Vector of post data
Data Types: (numeric, vector)
Name-Value Arguments
Specified optional pairs of Name=Value arguments. Name is the is the argument name and Value is the corresponding value. You can specify several name and value pair arguments in any order as Name1=Value1,...,NameN=ValueN.
Example: mu=15 specifies compare difference in pre and post against mean of 15.
mu
Mean to compare against (default=0)
Mean to compare difference of pre and post data vectors against.
Data Types: (scalar, numeric, float)
Output
d
Effect size.
Cohen’s d effect size for 2 paired samples.
Data Types: (scalar, float, numeric)
More About
Lecture
I refrained from outputting size of effect (e.g., ‘small’, ‘medium’, ‘large’) because these are arbitrary and should be thought of as such.
Tips
Issues and Discussion
Issues and Discussion.
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