clear
set obs 100
forvalues i=1/5 {
	generate x`i' = rnormal()
	}
alpha x1-x5, asis
* (A missing value means the calculation
*  produced a negative number.  This implies
*  that at least one item is reversed, or else
*  that the internal consistency is zero.)

preserve
* Calculating Alpha
* calculate total score
egen y = rowtotal(x1-x5)

* calculate item variances
collapse (sd) x1-x5 y
forvalues i=1/5 {
	generate v`i' = (x`i')^2
	}
* sum of item variances
egen sv = rowtotal(v1-v5)
* and finally, alpha
generate alpha = (5/4)*(1-(sv/(y^2)))
list alpha
restore

clear
set obs 100
matrix R = (1, 0.3, 0.3, 0.3, 0.3, ///
			1, 0.3, 0.3, 0.3, ///
			1, 0.3, 0.3, ///
			1, 0.3, ///
			1 )
drawnorm x1-x5, corr(R) cstorage(upper)
alpha x1-x5
* Now, "asis" is redundant
alpha x1-x5, asis

clear
set obs 100
matrix R = (1, 0.3, 0.3, 0.3, 0, ///
			1, 0.3, 0.3, 0, ///
			1, 0.3, 0, ///
			1, 0, ///
			1 )
drawnorm x1-x5, corr(R) cstorage(upper)
alpha x1-x5
* Like the first example, sometimes different
alpha x1-x5, asis

clear
set obs 100
matrix R = (1, 0.3, 0.3, 0.3, -0.3, ///
			1, 0.3, 0.3, -0.3, ///
			1, 0.3, -0.3, ///
			1, -0.3, ///
			1 )
drawnorm x1-x5, corr(R) cstorage(upper)
alpha x1-x5
* In this example, always different
alpha x1-x5, asis item