Inquiry on calculating effect size and statistical power!

[Peter Orangi]

In my study comprising treated, control and pure control groups, my outcome variable is increased income. My independent variables are skills (business, farm management, group management) as well as education, sex and age of the household head. I have established that the skills acquired through an intervention positively and significantly contribute to increased incomes. How can i calculate the effect size and statistical power of this intervention using stata? Kindly help me with the procedure and appropriate stata command...

[George Ford]

https://www.statalist.org/forums/forum/general-stata-discussion/general/1671069-power-analysis-for-difference-in-difference

[Asmiro Abeje Fikadu]

Hello Peter
I am not quite sure. However, it can be calculated using the formula: Power_effect = 1 - CDF(Z-score) under normal distribution data.
Z-score = (effect_size/standard_error)
higher power value implies a greater likelihood of showing the true effect of your skill variables on income.

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I have a similar question regarding the effect size and statistical power of a non-normal distribution dataset. I mean, I have executed a quantile regression model, and I want to check the power effect, to confirm my result shows the true effect. I have tried the following code in Stata, but I failed to come up with the final value of the power effect.
My objective is to estimate the effects of the commercialization index on income.

* Set parameters
local effect_size 16.543 // Effect size or coeffect of commercialization index at 50th quantile
local sample_size 610 // Sample size
local quantile 0.5 // Median or 50th quantile (for example)
local n_simulations 1000 // Number of simulations

* Initialize counter for significant results
local significant_count 0

* Loop for simulations
forvalues i = 1/'n_simulations' {
* Generate predictor variable (e.g., randomly from a normal distribution)
set seed 'i'
drawnorm predictors = rnormal('sample_size', 0, 1)

* Generate response variable based on quantile regression model with effect size
gen response = predictors * 'effect_size' + rnormal('sample_size', 0, 1)

* Fit quantile regression model
qreg response predictors, quantile('quantile')

* Check if coefficient estimate is statistically significant
if _b[predictors] < .05 {
* Increment counter if significant
local significant_count 'significant_count' + 1
}
}

* Calculate power
local power = ('significant_count' / 'n_simulations') * 100

* Output power estimate
di "Power: "'power'"%"

Kindly support me where I made error in this simulation?

[Peter Orangi]

Thanks George for the insightful information. I am following the idea and hope to come up with an appropriate command. I truly appreciate