Python

CLES.one_sample

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Calculate common language effect size for 1 sample. See Using_CLES.mlx for a notebook of given examples.

Syntax

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CL = CLES();

thetaHat = CL.one_sample(data1, mu)

Description

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A

thetaHat = CL.one_sample(data1, mu) returns common language effect size for 1 sample comparing mean to mu. example

Examples

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Example 1

Generate some data and calculate the common language effect size.

A = normrnd(5, 3, [100,1]);
CL = CLES(); 
theta = CL.one_sample(A, 3)

thetaHat = 95.0

Input Arguments

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data1

Data vector.

Vector of data to find common language effect size against.

Data Types: (vector, numeric)

mu

Mean for comparison

Mean to compare data vector against.

Data Types: (scalar, float)

Output

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thetaHat

Effect size.

Common language effect size. This value will be between 50 andn 100. This value can be interpreted as the percentage or chance a value from one group is larger than a value from the other group. A thetHat of 50 would me groups are essentially from same distribution. A thetaHat of 100 would mean that 100% of the data in one group is larger than the data in the other group.

Data Types: (scalar, float, numeric)

More About

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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

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Issues and Discussion

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Issues and Discussion.

If you don’t know how to use github (or don’t want to), just send me an email.